# Dissecting the morphology of star forming complex S193

Rakesh Pandey1, Saurabh Sharma2,Lokesh Dewangan1,D. K. Ojha3, Neelam Panwar2
Arpan Ghosh2, Tirthendu Sinha4, Aayushi Verma2, and Harmeen Kaur5
1Physical Research Laboratory, Navrangpura, Ahmedabad - 380 009, India.
2Aryabhatta Research Institute of Observational Sciences (ARIES), Manora Peak,
Nainital, 263 002, India<EMAIL_ADDRESS>
3Tata Institute of Fundamental Research, Homi Bhabha Road, Colaba, Mumbai -
400 005, India
4Satyendra Nath Bose National Centre for Basic Sciences (SNBNCBS), Block-JD,
Sector-III, Salt Lake, Kolkata-700 106, India
5Center of Advanced Study, Department of Physics DSB Campus, Kumaun University
Nainital, 263002, India
<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We have studied a star-forming complex S193 using near-infrared (NIR)
observations and other archival data covering optical to radio wavelengths. We
identified stellar clusters in the complex using the NIR photometric data and
estimated the membership and distance of the clusters. Using the mid-infrared
(MIR) and far-infrared (FIR) images, the distribution of the dust emission
around H ii regions is traced in the complex. The $Herschel$ column density
and temperature maps analysis reveal 16 cold dust clumps in the complex. The
H$\alpha$ image and 1.4 GHz radio continuum emission map are employed to study
the ionised gas distribution and infer the spectral type and the dynamical age
of each H ii region/ionised clump in the complex. The 12CO(J =3$-$2) and
13CO(J =1$-$0) molecular line data hint at the presence of two velocity
components around [-43,-46] and [-47,-50] km/s, and their spatial distribution
reveals two overlapping zones toward the complex. By investigating the
immediate surroundings of the central cluster [BDS2003]57 and the pressure
calculations, we suggest that the feedback from the massive stars seems
responsible for the observed velocity gradient and might have triggered the
formation of the central cluster [BDS2003]57.

###### keywords:

stars: luminosity function, mass function – stars:formation – dust, extinction
– H ii regions

††pubyear: 2023††pagerange: Dissecting the morphology of star forming complex
S193–Dissecting the morphology of star forming complex S193

##  1 Introduction

Understanding the formation process of massive stars has been one of the
leading research goals of many current and past studies (André et al., 2016;
Torii et al., 2017; Motte et al., 2018; Fukui et al., 2018b; Fukui et al.,
2021) as they have a profound effect on the Galactic evolution. The formation
processes of massive stars are also less understood as they are of a short
time scale and are less frequent than the lower mass stars. Since massive
stars start affecting their natal environment soon after their formation,
these processes are even more complex (Azimlu & Fich, 2011). Stellar collision
(Bonnell et al., 1998), competitive accretion (Bonnell et al., 2001) and
monolithic collapse of a dense cloud are considered in the literature as
plausible scenarios to explain the formation process of massive stars. In
recent years, cloud-cloud collision (CCC) has emerged as a very promising
mechanism to explain massive star formation, as demonstrated by numerous
theoretical and observation works (Furukawa et al., 2009; Dewangan et al.,
2017b; Fukui et al., 2018b; Fukui et al., 2021; Liang et al., 2021). The CCC
model was first conceptualized by Habe & Ohta (1992) where they realized the
possibility of massive star formation in the compressed layer between one
small and another large cloud formed via supersonic collision. Recently, Fukui
et al. (2021), with a detailed magneto-hydrodynamic simulation, show that the
dense gas cores form in the compressed layer between two clouds, which
preferentially triggers the formation of O and early B-type stars. The
powerful winds, jets, and outflows from the massive stars change the physical
conditions such as temperature, density, and turbulence in the surrounding
molecular cloud (Gritschneder et al., 2009). For example, H ii regions are
formed through the expansion of ionised gas in the surrounding cloud due to
the highly energetic UV radiation and winds from these massive stars. As most
of the star formation occurs in a group or cluster, massive stars are often
associated with young star clusters and H ii regions in a star-forming region.
The feedback from the massive stars thus plays a vital role in governing the
star formation process around them. It can push forward and increase the star
formation rate, known as positive feedback or slow down or terminate star
formation with negative feedback. The outcome depends not only on the process
but the surrounding environment itself (Shima et al., 2017).

Lying between the star-forming regions W4 and W5, the S193 complex
($\alpha$2000 =02h47m25s, $\delta$2000 = 61$\degr$56$\arcmin$57$\arcsec$)
consists of three H ii regions Sh 2-192 (hereafter, S192), Sh 2-193
(hereafter, S193) and Sh 2-194 (hereafter, S194) (Qin et al., 2008; Blitz et
al., 1982). This region also hosts three previously identified massive stars,
i.e., S192-1 (B2.5V), S193-1 (B2.5V) & S193-3 (B1.5V) (Russeil et al., 2007),
along with three IRAS sources, IRAS 02435+6144 ($\alpha$2000 =02h47m25s,
$\delta$2000 = 61$\degr$56$\arcmin$57$\arcsec$), IRAS 02437+6145 ($\alpha$2000
=02h47m40.4s, $\delta$2000 = 61$\degr$45$\arcmin$55$\arcsec$), IRAS 02439+6143
($\alpha$2000 =02h47m53.6s, $\delta$2000 = 61$\degr$56$\arcmin$01$\arcsec$)
and a Be star D75b ($\alpha$2000 =02h47m35s, $\delta$2000 =
61$\degr$55$\arcmin$30$\arcsec$) (Gkouvelis et al., 2016). Two open clusters,
i.e, [BDS2003]57 (Russeil et al., 2007) and Teutsch 162 (hereafter, T162) (Qin
et al., 2008), are also part of this region. In the S193 complex, Azimlu &
Fich (2011) studied the molecular gas in a velocity range of [-44,-50] km/s
(see also Qin et al. (2008)) and identified ten molecular clumps. The
velocities of the ionised gas toward S192, S193, and S194 were reported to be
-44.7, -49.6, and -44.7 km/s, respectively (Anderson et al., 2014). The S193
complex is thus an exciting region to investigate the star formation processes
involving massive stars, young star clusters, and molecular clumps. In this
paper, we have done a comprehensive multiwavelength study of the S193 complex,
which is presented as follows. Section 2 has information about the
multiwavelength data sets used in this study and explains the data reduction
procedures regarding our near-infrared (NIR) observations. In Section 3, we
analyzed the multiwavelength data sets to estimate the various morphological
parameters and probed the physical environment around the S193 complex. In
section 4, we have discussed the star formation scenario in the S193 complex
and summarized our study in Section 5.

##  2 Observation and data reduction

###  2.1 Near-infrared (NIR) Imaging data

We observed the S193 complex in broad-band NIR ($JHK$) filters using the TIFR
Near Infrared Spectrometer and Imager
(TIRSPEC)111http://www.tifr.res.in/$\sim$data/tirspec/. The instrument is
mounted on the 2 m Himalayan $Chandra$ Telescope (HCT), Hanle, Ladakh, India.
Ninan et al. (2014) have provided the details of this instrument and its
detector array specifications. TIRSPEC covers $307^{\prime\prime}\times
307^{\prime\prime}$ of the sky in the imaging mode; thus it took four
pointings to cover the entire S193 complex ($\sim 10^{\prime}\times
10^{\prime}$). In each pointing, we took five dithered positions with seven
frames in each dithered position. The exposure time of frames in all three
bands ($JHK$) is 20 secs. We have also observed the central embedded cluster
‘[BDS2003]57’ (FOV $\sim$ $1^{\prime}\times 1^{\prime}$ ) in $JHK$ bands using
the TIFR-ARIES Near Infrared Spectrometer (TANSPEC; Sharma et al., 2022)
mounted on the 3.6m Devasthal Optical Telescope (DOT), Nainital, India. We
observed the source in six sets, each containing seven dithered positions with
five frames in each position. The exposure time of frames in the K band is 20
secs, while for the J and H bands, it is 50 secs. The NIR data reduction,
which includes cleaning the raw images, and performing photometry and
astrometry, is done using the procedure explained in Pandey et al. (2020).
Using the transformation equations below, we transformed our instrumental
$JHK$ magnitudes into a standard Vega system.

$(J-K)=(0.92\pm 0.12)\times(j-k)+(0.64\pm 0.06)$ (1) $(H-K)=(0.99\pm
0.03)\times(h-k)+(-0.04\pm 0.02)$ (2) $(K-k)=(-0.04\pm
0.10)\times(H-K)+(-4.85\pm 0.02)$ (3)

In the above equations, $jhk$ and $JHK$ denote the instrumental and standard
magnitudes, respectively. The coefficients in the above equations were
generated using the stars common in our observations and the 2MASS catalogue
for one of the pointings in the TIRSPEC observations. Similarly, the
coefficients were estimated separately for each pointing and instrument. We
made a combined catalogue that includes the TIRSPEC and TANSPEC observations
by considering only those stars with less than 0.1 mag photometric
uncertainty. The magnitudes of the stars which are saturated in our
observations are taken from the 2MASS catalogue. Table 1 provides the
statistics of our NIR observations.

###  2.2 Archival data

The multiwavelength archival data sets used in the present study are tabulated
in Table 2. The optical photometric data in the grizy bands (i.e., g, r, i, z,
and y) were taken from the Pan-STARRS1 Surveys (PS1). The 5$\sigma$ limiting
magnitudes of point sources in g, r, i, z, and y bands are 23.3, 23.2, 23.1,
22.3, and 21.4 mag, respectively (Chambers et al., 2016). We particularly used
g and i band photometric data from the survey, to plot the colour-magnitude
diagram (CMD, cf. Section 3.3). We accesed the point-source catalogue of 2MASS
survey in NIR $J$, $H$, and $K$ bands. The catalogue is 99 % complete upto the
$J$= 15.8 mag, $H$=15.1 mag, and $K$=14.3 mag (Skrutskie et al., 2006), we
used the 2MASS NIR data to generate the surface density map of the stars in
the targeted region (cf. Section 3.1). The Gaia Early Data Release 3 (DR3)
(Gaia Collaboration et al., 2021) data are used in the present work for
estimating the member stars of the clusters using their proper motion. Gaia
DR3 provides astrometry, photometric, and radial velocity measurements of the
nearly $\sim$ 1.8 billion sources, the data shows completeness of 99 % upto
the G $\sim$ 20 (for b $\sim$ 30$\degr$), and G $\sim$ 22 (for higher
lattitudes).
We used Mid-infrared (MIR) and Far-infrared (FIR) images from $Spitzer$ and
$Herschel$, respectively to trace the heated dust emission in the S193
complex, while Submillimetre (Sub-mm) images from $Herschel$ are effective in
tracing the cold dust emission. The radio continuum image from the NRAO VLA
Sky Survey (NVSS) along with the H$\alpha$ image from the INT Photometric
H$\alpha$ Survey of the Northern Galactic Plane (IPHAS), are used in this
work, to trace the distribution of the ionised gas (cf. Section 3.6.2). We
also used the 12CO(J =3$-$2) line data from the 15m JCMT telescope (James
Clerk Maxwell Telescope) archive and the 13CO(J =1$-$0) data from the 13.7m
Purple Mountain Observatory (PMO, China) acquired as a part of the Milky Way
Imaging Scroll Painting (MWISP)project
222http://english.dlh.pmo.cas.cn/ic/in/. The molecular line data are used for
tracing the kinematics of the molecular gas in the targeted region (cf.
Section 3.6.4).

Figure 1:  (a) Colour composite image of the S193 complex made using 2MASS $K$
(red), $H$ (green), $J$ (blue) band images. The surface density contours from
the present analysis (see Section 3.1) are overplotted with the yellow colour,
while the core region of the three clusterings ([BDS2003]57, T162, and SEC) is
marked with the red square. (b) TANSPEC $K$ (red), $H$ (green), and $J$ (blue)
band colour composite image of the core region of the [BDS2003]57 clustering
[FOV $\sim$ $56^{\prime\prime}\times 56^{\prime\prime}$]. TIRSPEC $K$ (red),
$H$ (green) and $J$ (blue) band colour composite image of the core region of
the T162 [FOV $\sim$ $86^{\prime\prime}\times 86^{\prime\prime}$] and the SEC
[FOV $\sim$ $90^{\prime\prime}\times 90^{\prime\prime}$] are shown in (c) and
(d), respectively.  Figure 2:  PM vector-point diagrams for [BDS2007]57 (a),
Tuesch 162 (b), and SEC (c) clustering.  Figure 3:  Membership probability
(Pμ), PM errors ($\sigma_{PM}$) and parallax of all the stars within the
clustering (circle having radius 3′) identified in the S193 complex as a
function of $G$ magnitude. The probable member stars (P${}_{\mu}>$80 %) are
shown by green circles, while 24 members of the S193 complex having parallax
values with good accuracy (err $<$ 0.1 mas) are shown by red triangles.
Figure 4:  g vs (g-y) CMD, plotted using the PS1 data, the black dots
represent the star within the cluster while green dots represent the member
star identified in the present analysis (see section 3.2). The ZAMS and 1 Myr
isochrone by Pastorelli et al. (2019) corrected for distance 4.84 kpc and
reddening $E(g-y)=1.827$ mag, are shown with blue and red continuous curves,
respectively. The star symbols denote the previously identified massive stars
and the bright member stars.

##  3 Results and Analysis

###  3.1 Stellar clustering/groupings in the S193 complex

To identify clustering/grouping in S193 complex, we estimated the stellar
surface density distribution (Gutermuth et al., 2009; Sharma et al., 2016). We
used the nearest neighbour (NN) method to generate surface density maps in the
region (Gutermuth et al., 2005). We took a grid size of 6′′ and determined
local surface density by varying radial distance so that it encompasses
through the nearest 6th NN in 2MASS catalog. This technique has been used and
described in our previous work also (Pandey et al., 2020, 2022). We have shown
the density contours in Figure 1a with the yellow colour, and the lowest
contour is drawn at 1$\sigma$ above the mean stellar density (8.7
stars/arcmin2), and step size of 1$\sigma$ (2.9 stars/arcmin2). We can see a
fragmented overdensity of stars in the S193 region with three sub-groupings,
one at the centre (CC), another at the North-East (NE) direction, and another
at the South-East direction (SEC). All three sub-groupings are connected
through the lowest density contours and seem to be part of a similar
population. The clusterings at the centre and NE direction coincide with the
previously identified star clusters, [BDS2003]57 and T162, respectively. We
have roughly estimated the cluster sizes by visually inspecting the surface
density contours. The box enclosing different cluster regions are marked in
Figure 1 with red boxes. In this way, the size of the clustering [BDS2003]57
is found to be $\sim$ (1$\times$1) arcmin2 while the size of the Tuesch 162
and SEC is found to be $\sim$ (1.4$\times$1.4) arcmin2, (1.5$\times$1.4
arcmin2), respectively. From the 2MASS NIR data, we found 40 stars inside the
[BDS2003]57, it has a mean stellar density of $\sim$ 34 stars/arcmin2. The
Tuesch 162 contains 29 stars inside it with a mean stellar density of $\sim$
14.1 stars/arcmin2, and the SEC has 30 stars inside and it has a mean stellar
density of 13.3 stars/arcmin2. As the 2MASS data is bit shallow, there is a
probability of finding more low-mass/embedded stars within the cluster region,
thus above estimates can be taken as lower limits. Figure 1 shows the colour
composite image of three clusters (see b, c, and d panels) made using $JHK$
band images. The central clustering [BDS2003]57 (cf. Figure 1b) is covered in
our TANSPEC observations, while the other two clusterings (cf. Figure 1 c, d)
are covered in our TIRSPEC observations (cf. Section 2.1). The identified
groupings ([BDS2003]57, T162, and SEC) lie within a circle centred at
$\alpha_{2000}$: 02h47m34s.29, $\delta_{J2000}$:
61$\degr$57$\arcmin$20.93$\arcsec$ and radius of 3′ (shown with a white circle
in Figure 1a).

###  3.2 Membership analysis of the stars in the S193 complex

Recently released Gaia data release 3 (Gaia Collaboration et al., 2021) is
becoming highly impactful in the study of star clusters by providing precise
measurements of the proper motion (PM) and parallax of stars. As we have found
three inter-connected stellar groupings/clusterings in the S193 complex
(Section 3.1), it is now important to find whether these clusterings are
connected in the proper motion space and further estimate their member stars.
In Figure 2, we have shown the vector point diagrams (VPDs) of three
clusterings in which proper motions in RA and DEC are plotted as X and Y axes,
respectively. In the VPDs, we can see that the stars in all three clusterings
show the distribution around $\mu_{\alpha}$cos($\delta$) = -0.70 mas yr-1 and
$\mu_{\delta}$ = 0.17 mas yr-1, this suggests that the three clusters are also
interconnected in the proper motion space. We thus determined the membership
probability of all the stars in a circular FOV of 3′ (which accommodates all
three clusterings, see Section 3.1) using the method described in Balaguer-
Núnez et al. (1998) and Sharma et al. (2020). The procedure starts with
determining frequency distributions of cluster stars ($\phi^{\nu}_{c}$) and
field stars ($\phi^{\nu}_{f}$) using equations 3 and 4 of Balaguer-Núnez et
al. (1998). The PM centre and its corresponding dispersion for the member
stars ($\mu_{\alpha}$cos($\delta$) = -0.70 mas yr-1, $\mu_{\delta}$ = 0.17 mas
yr-1, $\sigma_{c}$, $\sim$0.5 mas yr-1) as well as for the field stars
($\mu_{xf}$ = 0.29 mas yr-1, $\mu_{yf}$ = -0.50 mas yr-1, $\sigma_{xf}$ = 4.10
mas yr-1 and $\sigma_{yf}$ = 2.85 mas yr-1) is determined using the procedure
previously used and described in Pandey et al. (2020, 2022) and Sharma et al.
(2020). We calculated PM dispersion for the cluster by using the radial
velocity dispersion of 1 kms-1 for open clusters (Girard et al., 1989) and
assuming a distance of 4.84 kpc (present estimate), the PM dispersion comes
out to be 0.5 mas yr-1. We used this value as a PM dispersion for the cluster
stars (for both RA and DEC), the same method has been used in previous studies
(Yadav et al., 2013; Sharma et al., 2020; Pandey et al., 2022). The field
stars are taken from the VPD of all stars within a 3’ circle enclosing all
three clustering and roughly falling outside of the PM distribution of cluster
stars (for more details, see Sharma et al. (2020)). Finally, we have
determined the membership probability, i.e., the ratio of the distribution of
cluster stars to all stars, using the below-mentioned equation.

$P_{\mu}(i)={{n_{c}\times\phi^{\nu}_{c}(i)}\over{n_{c}\times\phi^{\nu}_{c}(i)+n_{f}\times\phi^{\nu}_{f}(i)}}$
(4)

Where $n_{c}$ (=0.19) and $n_{f}$ (=0.81) correspond to the normalized number
of stars for the cluster and field region, respectively.

In Figure 3, membership probability, errors in proper motion, and parallaxes
are plotted against the G magnitude. By applying the membership criteria of
considering only those stars as a member which have a membership probability
Pμ $>$ 80 %, we identified 80 stars as the member of clustering in the S193
complex. Figure 3 shows the member stars as green circles. Figure 3 depicts
that the member stars are quite effectively separated from the field stars in
the brighter end while in the fainter end, they are not well separated (see
top panel of Figure 3) because of the large uncertainties in the proper motion
at fainter magnitudes (middle panel). In Table 3, we have tabulated the 80
member stars of the S193 complex.

###  3.3 Reddening and distance of the S193 complex

Few distance estimates are available in the literature for the S193 complex
but with different values. Russeil et al. (2007) reported a distance of
$2.4\pm 0.32$ kpc based on the individual distances of S192 and S193 H ii
regions, while, Qin et al. (2008) and Azimlu & Fich (2011) reported distances
as 5.2 kpc and $2.96\pm 0.54$ kpc, respectively. Qin et al. (2008) reports a
distance based on the galactic rotation curve and the accompanying cloud’s CO
velocity, whereas Russeil et al. (2007) and Azimlu & Fich (2011) report
spectrophotometric distances.
Out of the 80 member stars of the S193 complex, the distance of the 23 stars
having parallax values with good accuracy (i.e., error$<$ 0.1 mas, red
triangles in Figure 3) having distance estimation in Bailer-Jones et al.
(2021), is further used to constrain the distance of the clusters and the S193
complex. We estimated the distance of each clustering in the S193 complex by
taking the mean of the distance of the above stars provided in Bailer-Jones et
al. (2021), which belongs to that particular cluster. In this way, the
distance of the cluster [BDS2003]57 is found to be 4.59$\pm$1.4 kpc while the
distance of the T162 and SEC is found to be 4.96$\pm$0.67 kpc and 4.51$\pm$1.6
kpc, respectively. The distance of the whole S193 complex is found to be
4.84$\pm$1.4 kpc. The distance of the clusters matches each other and the
distance of the S193 complex within the error, which suggests that all the
clustering are at the same distance and are part of the S193 complex. Our
derived distance of the S193 complex matches with the distance derived by Qin
et al. (2008) and Chan & Fich (1995), while differs with the distance
estimated by Russeil et al. (2007) and Azimlu & Fich (2011). We have also
calculated the distance of massive stars in the complex using distance
estimates from Bailer-Jones et al. (2021). The distance of the massive star
S192-1 is 5.99$\pm$0.45 kpc, while the distance of the massive star S193-1 and
S193-3 is found to be 5.06$\pm$0.64 and 10$\pm$2 kpc, respectively. The
distance of the massive stars S192-1 and S193-1 matches the distance of the
S193 complex within error, while the S193-3 is much far away. Russeil et al.
(2007) also estimated the distance of the massive star S193-3 as 8.0 kpc and
concluded that the S193-3 is a background star and not a part of the S193
complex. We have also used the CMD of the stars in the S193 complex to
validate the derived distance of the S193 further. This technique is very much
reliable and widely used by many authors (for example, Phelps & Janes, 1994;
Sharma et al., 2006; Sharma et al., 2017, 2020). In Figure 4, we have shown
the $(g-y)$ vs. $g$ CMD of the stars in the S193 complex (black dots) taken
from the PS1 archive (see Table 2). The member stars in Figure 4 are shown
with green circles. The reddening value in the direction of the S193 is taken
from the Bayester dust map by Green et al. (2019). We retrieved the
E(g-y)=1.827 mag for our studied target from the extinction map of Green et
al. (2019), then it is converted to Av using the color relations given by Wang
& Chen (2019). We found the reddening value to be AV= 2.41 mag in the
direction of the S193 complex. The ZAMS (continuous blue curve) and PMS
isochrone of 1.0 Myr (continuous red curve) is also shown in the figure, which
is taken from Pastorelli et al. (2019). Both the ZAMS and isochrone are
corrected for the reddening (E(g-y)= 1.827 mag) and distance (4.84 kpc). The
ZAMS matches the distribution of the member stars, which gives confirmation
regarding the distance and reddening of the S193 complex.

Apart from the previously identified massive stars (S192-1, S193-1, and
S193-3), the S193 complex hosts few other bright member stars which are shown
with star symbols in Figure 4. The star BG2 lies at the boundary of the S193
complex and has no radio or 22 $\mu$m emissions traced around it (cf. Figure
9). This star is located at a distance of 2.75$\pm$0.21 kpc (Bailer-Jones et
al., 2021), thus is probably a foreground star. The star BG1 and S194-1 lie
inside the H ii region S193 and S194, respectively, and seem to be associated
with the radio emission with an envelope kind of structure visible in the WISE
22 $\mu$m band (cf. Figure 9). The location of the BG1 and S194-1 in the CMD
indicate their spectral types as B5V and B1.5V, respectively. The star S192-1
(previously B2.5V star) seems to be around B1.5V, while S193-1 (previously
B2.5V) seems to be around B2.5V in the CMD. With our radio analysis (cf.
Section 3.6.2), we found that the ionising source responsible for creating the
H ii regions S192, S193, and S194, should have a spectral type between
B0-B0.5. Hence, the BG1 (B5V) along with the S193-1 (B2.5V) may be jointly
responsible for creating the H ii region S193. The brightest massive stars
S192-1 (B1.5V) and S194-1 (B1.5V) could be the ionizing source of S192 and
S194, respectively.

Figure 5:  (a) Completeness factor as a function of $J$ magnitude derived from
the artificial star experiments (ADDSTAR, see Section 3.4 for details) on the
TANSPEC, TIRSPEC $J$ and $H$ band images. The $H$-band completeness factor is
off-setted by the mean colour of the MS stars (i.e., 1.0 mag). The continuous
curves are the smoothed bezier curves for the data points for completeness.
(b) Field stars distribution generated by using a nearby reference field
(magenta circles) using TIRSPEC $J$ band data. A straight line is a least
square fit to the data points.

Figure 6:  $J$ vs. $J-H$ CMD for (a) stars in the [BDS2003]57 cluster region,
(b) stars in the reference region, and (c) statistically cleaned sample of
stars in the [BDS2003]57 cluster region. The ZAMS (blue curve) and isochrone
of 2 Myr (red curve) by Pastorelli et al. (2019) along with reddening vector
for different mass stars (black slanted dashed lines in panel (c)) are also
shown. Stars which are on the left side of the 2 Myr isochrone in panel (c)
are used to estimate the MF of the region. All the curves are corrected for
the distance of 4.84 kpc and foreground extinction of AV=2.41 mag. The black
horizontal dashed-line in panel (c) represents the 50% completeness limit of
the TANSPEC data. Figure 7:  A plot of the MF for the statistically cleaned
CMD for the stellar sources in the [BDS2003]57 cluster region. Log $\phi$
represents log($N$/dlog $m$). The error bars represent $\pm\sqrt{N}$ errors.
The solid line shows the least squares fit to the MF distribution (black
dots). The open circle is the data point falling below the completeness limit
of 0.3 M⊙.

###  3.4 Mass Function

The mass function (MF) is an important statistical tool for understanding the
formation of stars and is expressed by a power law, $N(\log m)\propto
m^{\Gamma}$ and the slope of the MF is given as: $\Gamma=d\log N(\log m)/d\log
m$, where $N(\log m)$ is the number of stars per unit logarithmic mass
interval. We have used our deep NIR data from TANSPEC observations to generate
the MF of the central cluster [BDS2003]57 which has the maximum number density
among all three clusterings found in this region. We have utilized the NIR $J$
versus $J-H$ CMDs of the sources in the target region and that of the nearby
field region of equal area and decontaminated the former sources from the
foreground/background stars and corrected for data in-completeness using a
statistical subtraction method (cf. Pandey et al., 2022, 2020; Sharma et al.,
2020, 2017, 2012; Sharma et al., 2008). We have calculated the completeness
factor (CF) for both $J$ and $H$ bands and taken the minimum of them according
to the mean $J-H$ colours ($\sim$ 1 mag) of the MS stars as our final
completeness value (see Sagar & Richtler, 1991). To detemiine the CF, we used
the IRAF ADDSTAR routine as described in our previous publications (Pandey et
al., 2022, 2020). We have derived the CF for the TANSPEC and TIRSPEC data,
which is shown as a function of $J$-band in the left panel of Figure 5. As
expected, the incompleteness of the data increases with increasing magnitude
and the photometric data is more than 50% complete up to $\sim$19.5 mag and
$\sim$18.0 mag for the TANSPEC and TIRSPEC observations, respectively. As
TIRSPEC observations are shallow and we do not have field region observations
using the TANSPEC, we have used the distribution of a nearby reference field
in TIRSPEC pointing and the completeness corrected luminosity function (right
panel of Figure 5) of this region is used to calculate the number of field
stars between $J\sim$ 18.0 to 19.5 mags.

In Figure 6, we have shown the $J$ versus $J-H$ CMDs for the stars lying
within the central cluster ‘[BDS2003]57’ in panel (a) and for those in the
reference field region in panel (b). In panel (c), we have plotted the
statistically cleaned $J$ versus $J-H$ CMD for the central cluster
‘[BDS2003]57’ of $\sim$2 Myr of age. The number of stars in different mass
bins was then calculated by counting the stars along the reddening vector for
a 2 Myr isochrone. The corresponding MF is plotted in Figure 6 (right panel).
The 50% completeness limit (J $\sim$9.5 mag) of the TANSPEC photometry
corresponds to the detection limit of $\sim$ 0.3 M⊙ stars of an age of
$\simeq$2 Myr age, embedded in the nebulosity up to $A_{V}\simeq$2.4 mag
(foreground reddening) and $\sim$3 mag (differential reddening) (cf. Figure 6
(left panel) ‘c’). There seems to be a break in the slope of the MF
distribution of ‘[BDS2003]57 cluster at $\sim$1.5 M⊙ and the slope ‘$\Gamma$’
in the mass range $3>M_{\odot}>1.5$ and $1.5>M_{\odot}>0.3$ is estimated as
$-2.46\pm 0.01$ and $+0.64\pm 0.20$, respectively (cf. Figure 7).

###  3.5 YSOs identification and classification

We have used combined NIR catalogue (see Section 2) along with $Spitzer$ MIR
data to identify the YSOs in the $\sim 10^{\prime}\times 10^{\prime}$ FOV of
the S193 complex. The classification schemes of identifying YSOs are discussed
and explained in our previous publications (Pandey et al., 2020, 2022). In
Figure 8 (a) we show the NIR TCD (${[H-K]}$ vs. ${[J-H]}$) made by plotting
the stars in our combined NIR catalogue (black dots), the thick magenta curve
in the figure shows the redenned MS and giant branches, dotted magenta line
shows the locus of dereddened CTTS, and dashed magenta lines show the
redenning lines. The classification scheme by (Gutermuth et al., 2009) is used
to identify the YSOs using NIR TCD. Figure 8 (b) shows the
${[[3.6]-[4.5]]_{0}}$ vs. ${[K]-[3.6]]_{0}}$ TCD, made using the $Spitzer$
data, we adopted the method by Gutermuth et al. (2009) to identify and
classify the YSOs. Finally, we were able to identify 2 Class i and 25 class
Class ii YSOs using MIR data, and 5 Class ii sources using NIR data.

Figure 8:  a) ${[H-K]}$ vs. ${[J-H]}$ TCD for the sources corresponding to the
same FOV. We show the reddened MS and giant branches (Bessell & Brett, 1988)
by thick magenta dashed curves in the figure. The locus of dereddened CTTSs
(Meyer et al., 1997) is shown by dotted magenta line. We also show parallel
magenta dashed lines drawn from the tip (spectral type M4) of the giant branch
(left reddening line), from the base (spectral type A0) of the MS branch
(middle reddening line) and from the tip of the intrinsic CTTS line (right
reddening line). The increment of $A_{V}$ = 5 mag on the reddening lines is
shown with crosses, the red circles are the identified Class ii sources. b)
${[[3.6]-[4.5]]_{0}}$ vs. ${[[K]-[3.6]]_{0}}$ TCD of all the sources inside
the FOV of S193 complex ($\sim 10^{\prime}\times 10^{\prime}$). We used the
colour criteria by Gutermuth et al. (2009) to identify and classify the YSOs.
We show the identified Class i and Class ii YSOs with green and red circles,
respectively. Figure 9:  (a) Colour composite image of the S193 complex ($\sim
10^{\prime}\times 10^{\prime}$ FOV) made using $WISE$ 22 $\mu$m (red),
$Spitzer$ 3.6 $\mu$m (green) and $IPHAS$ H$\alpha$ (blue. (b) Colour composite
image of the S193 complex ($\sim 10^{\prime}\times 10^{\prime}$ FOV) made
using $WISE$ 22 $\mu$m (red), $WISE$ 12 $\mu$m (green), $IPHAS$ H$\alpha$
(blue) images is overplotted with CGPS radio continuum contours. The
individual H ii regions (S192, S193, and S194) are also marked over the image,
and the magenta circles show the YSOs identified in the present study. The
lowest radio contour is at 5 K and the step size is 0. 5 K. (c) $Spitzer$
ratio map of the S193 complex obtained by dividing $Spitzer$ 4.5 $\mu$m image
by $Spitzer$ 3.6 $\mu$m image. (d) The figure shows the same image as in panel
(a), overplotted with the $JCMT$ (12CO(J =3$-$2)) contours. The lowest contour
is at 2 K km s-1 and the step size is 4 K km s-1. The arrows marked two
prominent peaks (P1 and P2) in CO emission. In the images, star symbols
represent the previously identified massive stars, while triangles are the
IRAS sources. We have also shown previously identified Be star with the cross
symbol and the candidate massive sources with the plus symbol.

###  3.6 Multiwavelength view of the S193 complex

####  3.6.1 Distribution of the dust

In Figure 9a, we have shown the color composite image of the S193 complex
($\sim 10^{\prime}\times 10^{\prime}$ FOV) made by using the $WISE$ 22 $\mu$m
(red), $Spitzer$ 3.6 $\mu$m (green), and $IPHAS$ H$\alpha$ (blue) images. The
previously identified massive stars in the S193 complex (see Section 1) are
shown with the star symbols, while a Be type star and candidate massive
sources (see Section 1) are shown with the cross and plus symbols,
respectively. The H ii regions in the S193 complex (S192, S193, and S194,
marked in Figure 9b) are well traced by the H$\alpha$ emission in the region.
We can also see the warm dust envelopes around the massive stars and central
cluster ([BDS2003]57) traced by the $WISE$ 22 $\mu$m emission. Prominent poly-
cyclic aromatic hydrocarbon (PAH) features at 3.3, and 11.3 $\mu$m are
indicative of PDRs (photo-dissociation regions) which lies within the
bandwidth of the 3.6 $\mu$m and 12 $\mu$m images (see e.g., Peeters et al.,
2004). The PDR regions traced by the 3.6 $\mu$m emission are distributed
around the H ii regions in arc-type or circular sub-structures. The S192 H ii
region, having PDRs circularly distributed around the warm gas with massive
stars at the center, looks like a classical example of a MIR bubble (cf.
Churchwell et al., 2006).

In Figure 9b, we have shown the color composite image of the S193 complex made
by using the $WISE$ 22 $\mu$m (red), $WISE$ 12 $\mu$m (green), and $IPHAS$
H$\alpha$ (blue) images. The image is also over-plotted with the radio
continuum contours from the $CGPS$ 1420 MHz map. The spatial distribution of
the young stellar objects (YSOs; see Section 3.5) also shown with the magenta
color circles. The heated (22 $\mu$m emission) and the ionised gas (radio
emission) seem to be distributed at the location of the massive stars and are
surrounded by the PDRs (12 $\mu$m emission). The YSOs in this complex are
generally located, away from the massive stars, in regions with higher gas and
dust emissions (12 $\mu$m emission). We have also shown the $Spitzer$ ratio
map (4.5 $\mu$m/3.6 $\mu$m) in the Figure 9c. The prominent molecular hydrogen
($H_{2}$) line emission ($\nu$ = 0–0 $S$(9); 4.693 $\mu$m) and the Br-$\alpha$
emission (at 4.05 $\mu$m) comes within the bandwidth of $Spitzer$ 4.5 $\mu$m
band. Therefore, the bright and dark regions in this ratio map trace mainly
the Br-$\alpha$ emission and PDRs, respectively (for more details, see Pandey
et al., 2020). Note that both of these Spitzer images have the same PSF,
allowing to remove of point-like sources as well as continuum emission (for
more details, see Dewangan et al., 2017a). In Figure 9c, the bright regions
representing the $H_{2}$ and Br-$\alpha$ emission almost mimic the radio
continuum emission from the hot ionised gases. The distribution of the dark
lanes (PDRs) around the bright regions/radio emission indicates the impact of
massive stars in the S193 complex. In Figure 9d, we show the same image as in
Figure 9a, also overplotted with the $JCMT$ (12CO(J =3$-$2)) contours (see
Section 3.6.4). Interestingly, we can also see a bright patch at P1 (see
Figure 9c) located away from the radio continuum emission. The cavities in the
molecular cloud are clearly visible which shows the impact the H ii regions
have produced in their surrounding environment. The CO emission seems to peak
at [BDS2003]57 (P1), above the H ii region S193 (P2), and at the SEC
clustering.

####  3.6.2 Distribution of the ionised gas

To trace the distribution of ionised gas in the S193 complex, we have used
both the radio continuum (free-free emission) and the H$\alpha$ images. In
Figure 9b, we can see that the H$\alpha$ emission in the S193 complex is
mostly associated with the H ii regions. In the H ii region S192, the
H$\alpha$ emission is almost circularly enclosed by the PDRs like a MIR
bubble. In the case of H ii regions S193 and S194, the H$\alpha$ emission is
more or less extended and is partially surrounded by the PDRs in arc-like sub-
structures. Radio continuum contours from the 1.42 GHz map of $CGPS$ are also
overplotted in Figure 9b. They also show similar distribution with separate
peaks for each of the H ii regions. The massive star S192-1 (located inside
the H ii region S192) and S193-1 (located inside the H ii region S193) might
be the ionizing sources of the S192 and S193 H ii regions, respectively. There
is no previously identified massive star inside the boundary of H ii region
S194, but there is a bright star showing an envelope of warm dust at 22 $\mu$m
(plus symbol), which could be the possible ionizing source of S194.

By estimating the Lyman continuum flux associated with the ionised gas in each
of the H ii regions, we can also verify the spectral type of the ionizing
sources. Thus, this could lead us to constrain the massive star responsible
for creating the H ii regions S192, S193, and S194. We have used the following
equation from Schmiedeke et al. (2016) to determine the Lyman continuum flux
associated with each H ii region.

$\begin{split}N_{\mathrm{UV}}(s^{-1})&=7.5\,\times\,10^{46}\,\left(\frac{S_{\mathrm{(}}\nu)}{\mathrm{Jy}}\right)\left(\frac{D}{\mathrm{kpc}}\right)^{2}\\\
&\left(\frac{T_{e}}{10^{4}\mathrm{K}}\right)^{-0.45}\times\,\left(\frac{\nu}{\mathrm{GHz}}\right)^{0.1}\end{split}$
(5)

In the above equation, NUV denotes the Lyman continuum photons per second, Te
denotes the electron temperature, $\nu$ is the frequency, Sν is the integrated
flux, D is the distance of the S193 complex, i.e., 4.84 kpc. We have assumed
that all ionization flux in an H ii region is generated by a single massive
star and adopted the value of $T_{e}$ as 10000 K. To calculate the integrated
flux, we have used the task jmfit in AIPS on the NVSS (1.4 GHz) map. The
values of the integrated flux for the H ii regions S192, S193 and S194 are 67
mJy ($\sigma$=0.63 mJy/beam), 42.9 mJy ($\sigma$=0.54 mJy/beam), and 11 mJy
($\sigma$=0.44 mJy/beam), respectively. The radius of the H ii regions S192,
S193, and S194 are found to be 1.85 pc, 1.79 pc, and 1.20 pc, respectively, at
a distance of 4.84 kpc. The log(NUV) values for the H ii regions S192, S193,
and S194 are then estimated as 47.08, 46.89, and 46.30, respectively. By
comparing these log(NUV) values with those given in Panagia (1973), we have
found that the spectral type of the ionizing sources for all the H ii regions
in the S193 complex is between B0.5-B0 type.

We have also calculated the dynamical age of the H ii regions by using the
following equation by Dyson & Williams (1980b):

$t_{dyn}=\Big{(}\frac{4R_{s}}{7c_{s}}\Big{)}[\Big{(}\frac{R_{H\,{\sc
ii}}}{R_{s}}\Big{)}^{7/4}-1]$ (6)

where, cs is the isothermal sound velocity in the ionised gas (cs = 11 km s-1)
(Stahler & Palla, 2005), R${}_{H\,{\sc ii}}$ is the radius of the H ii region,
and Rs is the Strömgren radius of the H ii region which is given by:

$R_{s}=\Big{(}\frac{3S_{\nu}}{4\pi{{n_{0}}^{2}}{\beta_{2}}}\Big{)}^{1/3}$ (7)

where, n0 is the initial ambient density (in cm-3) and $\beta$2 is the total
recombination coefficient to the first excited state of hydrogen $\beta$2 =
$2.6\times 10^{-13}$ (Stahler & Palla, 2005). The dynamical age of the H ii
regions S192, S193, and S194 for n0=103(104) cm-3 are estimated as 0.6 (1.9)
Myr, 0.6 (2.0) Myr, 0.4 (1.4) Myr, respectively.

####  3.6.3 Embedded $Herschel$ clumps/condensation in the S193 complex

In this section, we have examined the $Herschel$ column density
($N(\mathrm{H}_{2})$) and temperature map and identified the embedded
structures in the S193 complex. We have used the maps produced for the EU-
funded ViaLactea project (Molinari et al., 2010a) and are publicly
available333http://www.astro.cardiff.ac.uk/research/ViaLactea/. A Bayesian
PPMAP approach is used on the $Herschel$ images at 70, 160, 250, 350 and 500
$\mu$m to produce these maps (Molinari et al., 2010b; Marsh et al., 2015,
2017).

‘

Figure 10:  (a) $Herschel$ column density map of the S193 complex overplotted
with the column density contours. (b) Clumps identified in the S193 complex
using the $Herschel$ column density map are marked with the corresponding IDs.
(c) $Herschel$ temperature map, the cold gas clump at position P1 is also
marked. The position of clumps P1 and P2 is marked in all three panels. The
symbols in all three panels are similar to those shown in Figure 9.

We show the $Herschel$ column density map of the S193 complex in Figure 10a.
To identify clumps/condensation, we have employed the clumpfind algorithm
(Williams et al., 1994) on the $Herschel$ column density map. The clumpfind
provides the total column density and the corresponding area of each clump as
an output. We identified 16 clumps in our target field, shown in Figure 10b.
We have further calculated the mass of each of the clump by employing the
following equation:

$M_{area}=\mu_{H_{2}}m_{H}Area_{pix}\Sigma N(H_{2})$ (8)

where $\mu_{H_{2}}$ is the mean molecular weight per hydrogen molecule (i.e.,
2.8), $m_{H}$ is the mass of the hydrogen atom, and $Area_{pix}$ is the area
subtended by one pixel (i.e., 6$\arcsec$pixel-1). $\Sigma N(\mathrm{H}_{2})$
signifies the total column density estimated using the clumpfind (see also
Dewangan et al., 2017a).

The mass and size of the identified clumps are provided in the Table 4. We
have the mass range of 224.6 M⊙ to 1141.9 M⊙ for the clumps identified in the
present study. The most massive clump (M⊙=1141.9, ID=1) lies almost at the
center of S193 complex coinciding with the boundary of [BDS2003]57 and peak P1
(see Section 3.6.1). Other two massive clumps (M⊙=912.2, ID=2) and (M⊙=527.8,
ID=7) are located near P2 (see Section 3.6.1). We can also see the two clumps
(IDs=3 and 9) associated with the SEC clustering (see Section 3.1). In Figure
10c, we also show the $Herschel$ temperature map of the S193 complex. The S193
complex can be seen glowing with a temperature greater than 17.5 K in the
$Herschel$ temperature map. A temperature around 14 K can be seen at P1,
confirming the presence of a cold dust clump at this position.

Figure 11:  The 12CO(J =3$-$2) and 13CO(J =1$-$0) profile in the direction of
6 small regions (i.e. m1 to m6; see Figure 12a). Figure 12:  a) $JCMT$ (12CO(J
=3$-$2)) moment-0 map along with the four regions (m1 to m4) from where we
have extracted the spectra are shown with the red circles. Molecular clumps
(C1 to C10) identified by Azimlu & Fich (2011) are also marked in the figure.
(b) MWISP (12CO(J =3$-$2)) moment-0 map. c) $JCMT$ (12CO(J =3$-$2)) moment-1
map. d) $JCMT$ (12CO(J =3$-$2)) moment-2 map, the map is overplotted with the
lines along which PV maps are obtained (see; Figure 14).  Figure 13:  (a) Two-
color composite image of S193 complex produced using 12CO(J =3$-$2) intensity
map ((red:[-43,-46], green:[-47,-50] kms-1), the $CGPS$ 1420 MHz contours are
also shown with white color. (b) Filled contours showing the surface density
map of the S193 complex, the map is also overplotted with the 12CO(J =3$-$2 )
contours showing two velocity components. The blue dashed line shows the
possible axis of collision.

####  3.6.4 Kinematics of the molecular gas in the S193 complex

To trace the distribution of the molecular gas and determine the gas
kinematics in the S193 complex, we have used the 12CO(J =3$-$2) $JCMT$ and
MWISP 13CO(J =1$-$0) line data (cf. Section 2). In Figure 11, we have shown
the 12CO and 13CO velocity profiles at six different locations in the
direction of the S193 complex (m1-m6; see Figure 12(a)). In some of the
locations we got a double-peaked velocity profile in the 12CO, these positions
also host some of the molecular clumps identified by Azimlu & Fich (2011) .
However, at positions P1 and P2 (where the intensity is maximum) we did not
get a double-peaked velocity profile. In the positions (m1 and m2; cf. Figure
12(a)) the shape of the 13CO profile almost mimics the 12CO while at positions
m3, m4 we could see a double-peaked velocity profile of 13CO. Overall, the
velocity profiles indicate a velocity range of [-43,-50] kms-1 with double
peaks, i.e., at approximately -45 kms-1 and -48 kms-1 at few particular
locations. These double-peaked velocity profiles could show two molecular
clouds having different velocity components which might be interacting with
each other (see for example; Dewangan et al., 2019).

In Figure 12 we have shown moment-0 (a), moment-1 (c), and moment-2 (d) map of
12CO(J =3$-$2). The intensity map (moment-0) is integrated over a velocity
range of [-43,-50] kms-1. The molecular gas shows fragmented morphology around
the H ii regions with cavities created by the feedback from the massive stars.
We can see two prominent peaks in the intensity map, one at P1 and another at
P2, where we have also seen peaks in the $Herschel$ column density map (see
Section 3.6.3). The location of massive stars, IRAS sources, and the YSOs
identified in the present study are also shown in the figure. Most of the YSOs
and IRAS sources are distributed towards peak P1. The ten molecular clumps
identified by Azimlu & Fich (2011) are also shown in the figure and are well
correlated with our moment-0 map.

Azimlu & Fich (2011) have reported the masses and radii of the detected clumps
to be in between 61-7 M⊙ and 0.5-0.2 pc, respectively. These values are a bit
lower than those we have estimated using the Hershel column density maps. They
have adopted a different value of the distance for the S193 complex, i.e.,
2.96 kpc, than the present study (4.84 kpc). The most massive clump C3
(61$\pm$19 M⊙), together with clumps C5 and C6, lie within the peak P1,
whereas the least massive clump C2 (7$\pm$2 M⊙), along with clump C1 is
located towards the identified SEC clustering (cf. Figure 12(a)). There
appears to be some correlation between the dust and gas clumps as the most
massive dust clumps are distributed towards peak P1 while the less massive
clumps are scattered towards SEC (cf. Section 3.6.3). In Figure 12b, we have
shown the first-moment (moment-1) map of 12CO (J =3$-$2), which is the
intensity-weighted mean velocity of the emitting gas. The first-moment map
shows two velocity components, i.e., one mainly in the eastern direction at
[-43,-46] kms-1 and another mainly in the western direction at [-47,-50]
kms-1, entangling at P1 and P2. The moment-2 map, which shows velocity
dispersion, indicates relatively high values at P1 and P2.

Figure 12(b) shows the moment-0 map of 13CO(J =1$-$0). The 13CO gas is
relatively optically thin and traces dense gas compared to the 12CO. The
moment-0 map, integrated in a velocity interval of [-43,-50] kms-1 peaks at P1
and P2 and shows almost the same morphology as 12CO despite having low
resolution (cf. Section 2.2).

In Figure 13a, we show the color composite image of S193 complex generated
from the 12CO maps at [-43,-46] and [-47,-50] kms-1 in red and green colors,
respectively. The molecular cloud near the massive star seems to be blown away
from the massive stars, as indicated by the $CGPS$ radio contours located in
the region of low brightness. In Figure 13b, we have shown the spatial
connection of the two components, which is superimposed by the surface density
map of the stars in the S193 complex (see Section 3.1). The stellar
clustering, i.e., [BDS20003]57, and almost all the YSOs are located in the
interaction zone P1.

To check whether there is a connection between these two components in
velocity space, we have also obtained the position-velocity (PV) map of 12CO
and 13CO along multiple lines. These lines are marked over the moment-2 map of
12CO (Figure 12c) and 13CO (Figure 13d), most of these lines are passing
through interaction zone P1 and P2. We obtained the PV map by choosing the
width of the slices as 1′, which are shown in Figure 14. Most of the PV maps
obtained using 12CO (Figure 14a) 13CO (Figure 14b) show a clear signature of
velocity gradient where velocity is increasing while going from east to west
direction. No apparent signature suggests that two groupings in velocity space
are separated with a diffuse emission.

Figure 14:  PV maps obtained using the 12CO(J =3$-$2) (a), and 13CO(J =1$-$0)
(b) data, these maps are obtained along the different lines (white lines in
Figure 12d) as discussed in Section 3.6.4.

####  3.6.5 Feedback of massive stars in the S193 complex

We also examined the effect of feedback from the massive stars in the S193
complex by calculating total feedback pressure. Total feedback pressure
consists of the stellar wind pressure (Pwind), radiation pressure (Prad), and
the H ii region pressure (P${}_{H\,{\sc ii}}$) (Bressert et al., 2012;
Dewangan et al., 2017b), which are given by the following equations (see for
details, Bressert et al., 2012).

$P_{rad}=L_{bol}/4\pi cD_{s}^{2};$ (9)

$P_{H\,{\sc ii}}=\mu m_{H}c_{s}^{2}\,\left(\sqrt{3N_{UV}\over
4\pi\,\alpha_{B}\,D_{s}^{3}}\right);$ (10)

$P_{wind}=\dot{M}_{w}V_{w}/4\pi D_{s}^{2};$ (11)

In the equations mentioned above, Lbol denotes the bolometric luminosity of
the ionizing source, $D_{s}$ is the distance at which pressure has to be
calculated, mH is the hydrogen atom mass, cs is the sound speed in the
photoionised region (=11 km s-1; Bisbas et al., 2009). NUV in the above
equation denotes the Lyman continuum flux, ‘$\alpha_{B}$’ is the radiative
recombination coefficient, and $\dot{M}_{w}$, Vw is the mass-loss rate, and
the wind velocity of the ionizing source, respectively.

Since there is ambiguity regarding the spectral type of the ionizing sources
in the S193 complex, we are considering both the lower and the upper limits of
the spectral type for pressure calculation. As a lower limit, we consider
B2.5V as the spectral type of all the sources. B2.5V is the reported spectral
type for the S193-1 and S192-1; for S194-1, it can also be treated as the
lower limit. We are adopting $L_{bol}$= 5012 L⊙ (Panagia, 1973), $\dot{M}_{w}$
$\approx$ 10-10 M⊙ yr-1, and Vw $\approx$ 700 km s-1 (Kobulnicky et al.,
2019), and NUV = 7.763 $\times$ 1044 (Panagia, 1973) for the B2.5V spectral
type. We obtained the total pressure ($P$= $P_{H\,{\sc ii}}$+$P_{rad}$\+
$P_{wind}$) for the S192, S193, and S194 H ii regions as 1.91 $\times$ 10-12
dynes cm-2, 1.37 $\times$ 10-12 dynes cm-2, and 4.08 $\times$ 10-12 dynes
cm-2, respectively. If we add these values to get the pressure due to all
three H ii regions at the position of the central cluster [BDS2003]57
($P_{all}$= $P_{192}$+$P_{193}$+$P_{194}$), it comes out to be $\sim$ 7.36
$\times$ 10-12 dynes cm-2. For the upper limit, we consider the spectral type
as B0.5, which is suggested by our radio calculation (cf. Section 3.6.2). For
a B0.5 star, we adopted the values of $L_{bol}$= 19952 L⊙, $\dot{M}_{w}$
$\approx$ 2.5$\times$ 10-9 M⊙ yr-1, Vw $\approx$ 1000 km s-1 (Dewangan et al.,
2017a), and NUV = 7.763 $\times$ 1044 (Panagia, 1973). We obtained the total
pressure ($P$= $P_{H\,{\sc ii}}$+$P_{rad}$\+ $P_{wind}$) for the S192, S193
and S194 H ii regions as 2.78 $\times$ 10-11 dynes cm-2, 2.03 $\times$ 10-11
dynes cm-2, and 5.6 $\times$ 10-11 dynes cm-2, respectively. The pressure due
to all three H II regions comes out to be $\sim$ 1.06 $\times$ 10-10 dynes
cm-2. Here it is worthwhile to note that the distance ($D_{s}$) is estimated
without taking into consideration of the projection effects. Thus, the
estimated distance represents its lower limit and it will lead to the upper
limit in the estimated pressure values.

##  4 Discussion

The S193 complex has complex and diverse morphological features like young
star clusters, three H ii regions, a few massive stars, and several molecular
clumps. Figure 9 shows the distribution of the massive stars, dust and gas,
PDRs, and ionised gas along with the YSOs in the S193 complex. The presence of
YSOs indicates the ongoing recent star formation activity in the region. In
this section, we have discussed the implications of the observed morphological
features in the S193 complex and investigated the star formation scenario.

Most YSOs in the S193 complex are distributed toward the central region
populating the young star cluster [BDS2003]57. The other young star cluster
T162 is associated with the H ii region S193 and contains a massive star
(S193-1). The new clustering identified in the south-west direction (‘SEC’)
has few YSOs. We have estimated the MF slope for the central cluster
[BDS2003]57 from our deep NIR data and found a change of MF slope from the
high to low mass end with a turn-off at around 1.5 M⊙. This truncation of MF
slope at a bit higher mass bins has often been noticed in other star-forming
regions (SFRs) under the influence of massive OB-type stars (Pandey et al.,
2020; Pandey et al., 2008; Sharma et al., 2017; Sharma et al., 2007; Jose et
al., 2008). The higher-mass stars mostly follow the Salpeter MF value, i.e.,
$\Gamma$=-1.35 (Salpeter, 1955). At lower masses, the IMF is less constrained
but appears to flatten below 1 M⊙ and exhibits fewer stars of the lowest
masses (Luhman et al., 2016; Lim et al., 2015; Chabrier, 2003; Kroupa, 2002).
While the higher-mass domain is thought to be mostly formed through
fragmentation and/or accretion onto the protostellar core (e.g., Bonnell &
Bate, 2006; Padoan & Nordlund, 2002) in the low-mass and substellar regime,
additional physics is likely to play an important role. The density, velocity
fields, chemical composition, tidal forces in the natal molecular clouds, and
photo-erosion in the radiation field of massive stars in the vicinity can lead
to different star formation processes and consequently, some variation in the
characteristic mass (turn-off point) of the IMF (Bate, 2009; Bate & Bonnell,
2005; Whitworth & Zinnecker, 2004; Padoan & Nordlund, 2002).

Figure 15:  Two colour-composite image produced using CGPS 1420 MHz radio
continuum image (red) and 12CO(J =3$-$2) moment-0 map (green). In this figure,
massive stars S193-1 and S193-2 are shown with the star symbol, while
candidate massive star S194-1 is shown with the plus symbol. The black box
represents the cluster’s core region [BDS2003]57, white circle are the YSOs,
and blue lines connect the cluster with the massive stars. The $Spitzer$ ratio
map (see Figure 9c) enclosed within a white box is shown in the right panel.

Each cluster identified in the S193 complex has an associated IRAS source. We
saw the presence of cold gas and dust clumps at the location of [BDS2003]57
and SEC in the $Herschel$ and molecular maps. The [BDS2003]57 cluster is
associated with the peak in the $Herschel$ 500 $\mu$m emission and traced with
a relatively low temperature ($\sim$14 K) in the $Herschel$ temperature map.
We have also seen molecular line emission (a bright region in the IRAC ratio
map) from the [BDS2003]57. By analysing the 12CO(J =3$-$2) data, we have
identified the presence of two velocity components in the S193 complex at
velocities around -45 kms-1 and -48 kms-1. Two spatially overlapping zones of
clouds, P1 and P2 (see Figure 12a), are observed, which are peaking at the
intensity maps of the 12CO(J =3$-$2), 13CO(J =1$-$0) emission. Zone P1 has a
young open cluster [BDS2003]57, YSOs, a cold gas clump, a massive molecular
clump (C1). We also saw three more molecular clumps (C3, C5, and C6) lying
inside this overlapping zone P1. Zone P2 also have two massive $Herschel$
clumps (C2, C7; see Section 3.6.3) along with a YSO. Though, in the 12CO and
13CO intensity map (see Figure 12a, 12b), the space near the massive stars
seems to be almost devoid of gas but are in very close proximity of
overlapping zones P1 and P2. The massive stars evolve faster than the low mass
stars, affecting their natal environment very soon. In that way, the space
near the massive stars does not always give a picture of the initial physical
conditions. We show a blue dashed line in Figure 13b, which we can consider as
the axis of two clouds colliding. This axis passes through the overlapping
zones P1 and P2 and covers the positions of massive stars in the S193 complex.
Few cavities are visible along this axis around massive stars (cf. Figure
13b), filled with ionised gas and surrounded by the PDRs, dust, and gas. These
findings lead us to explore the CCC process in the S193 complex, which
strongly favours the massive dense core, young clusters, and YSOs formation at
the overlapped region of the two interacting clouds (Torii et al., 2017; Fukui
et al., 2018a; Dewangan et al., 2019; Fukui et al., 2021). Many authors list
the observational signatures of the CCC (Torii et al., 2017; Dewangan et al.,
2019). An important signature of CCC is the existence of a compressed layer of
gas at the junction of two clouds, known as the bridge-like feature (Haworth
et al., 2015a, b; Torii et al., 2017; Dewangan et al., 2019). The bridge
feature shows the connection of two clouds in velocity space, which is
observationally seen as groupings in the PV maps separated by diffuse emission
(Haworth et al., 2015a, b). We have shown PV maps along different lines in our
12CO and 13CO maps to investigate the bridge feature. The 12CO(J =3$-$2)
transition is known to be optically thin as compared to other 12CO transitions
such as J=1-0 and J=2-1. It is a very useful tracer of medium density (104 at
20K) material, while 13CO is a tracer of the dense gas. In all of the PV maps
(except x1 and x2; see Figure 14) for 12CO (a) and 13CO (b), one can see the
velocity gradient from east to west but cannot see any significant feature
suggesting the velocity connection of two clouds. Another important
observational signature of the CCC is the spatial fit of enhanced intensity
and depressed region (Haworth et al., 2015b; Maity et al., 2022). Intensity
enhancement is termed ‘key’ while intensity depressed feature is known as
‘key-hole.’ In Figure 13a, where we have plotted two velocity components with
red and green colours, this kind of distribution is not observable. However,
the spatial connection of two velocity components with massive stars and
clustering found at the interface suggests the CCC in this region; other
signatures of CCC are not observed with the existing data sets. The
possibility of not seeing the observational signature of CCC could be that
feedback from massive stars, might be responsible for diminishing these
important features as three evolved H ii regions are distributed towards the
S193 complex (Maity et al., 2022). Another possibility suggesting the
influence of feedback from the massive stars played a role in forming the
central cluster [BDS2003]57 is discussed in the following paragraph.

The S193 complex contains a few massive stars, and feedback from them (intense
UV radiation, stellar wind) could also be the reason behind the observed
velocity gradient in the S193 complex. The massive stars in the S193 complex
strongly affect their surroundings, evident with the PDR structures and the
cavities formed in the cloud. Interestingly the central cluster [BDS2003]57 is
surrounded by the H ii regions S192 (from the south), S193 (from the north-
east), and S194 (from the north-west), as we can see radio emissions from the
H ii regions surrounding the cluster (see Figure 15). To investigate the
effect of feedback from the massive stars in the cluster, we have shown two
colour composite image of the S193 complex made using CGPS 1.42 GHz radio
continuum image (red) and the 12CO(J =3$-$2) moment-0 map (green) in Figure
15. Let us take a close look at the central region hosting the cluster. It is
visible that this region shows curved morphology from all three sides where
radio continuum emission interacts with the molecular cloud (shown with
magenta arrows in Figure 15). This signature strongly suggests the compression
of the cloud due to the interaction with the radio emission; interestingly, we
have also seen PDR structures at the interaction zone, further strengthening
this idea. This central region also hosts molecular clumps, dust
condensations, YSOs, and the cluster. The compression of the molecular cloud
from the sides of all three H ii regions could be responsible for the
formation of clumps and, subsequently, the cluster [BDS2003]57, which we can
attribute to the positive feedback of the massive stars. To quantitatively
examine this scenario, we also calculated the pressure of the massive star at
the position of the cluster [BDS2003]57; the pressure calculations had been
given as one of the main arguments supporting the feedback scenario (Pandey et
al., 2020). Considering the ambiguity regarding the spectral type of the
massive stars in the region, we have calculated the upper and lower limit of
the total pressure due to all three H ii regions at the position of
[BDS2003]57. The lower limit of the total pressure (7.36 $\times$ 10-12 dynes
cm-2) is similar to the value of a cool molecular cloud
($P_{MC}$$\sim$10-11–10-12 dynes cm-2 for a temperature $\sim$20 K and
particle density $\sim$103–104 cm-3) (see Table 7.3 of Dyson & Williams,
1980a). However, an upper limit of the total pressure (1.06 $\times$ 10-10
dynes cm-2) is greater than the value of a typical cloud. If taken into
consideration, the upper limit of the pressure suggests that the massive
stars’ feedback has triggered the cloud’s collapse; however, a detailed
spectroscopic analysis of the massive stars would shed greater light in this
regard. In our analysis, we found that the dynamical age of the H ii regions
S192, S193, and S194, for ambient density n0=103(104) cm-3 are 0.6 (1.9) Myr,
0.6 (2.0) Myr, 0.4 (1.4) Myr, respectively. The dynamical age of these H ii
regions considering the ambient density of n0=104 cm-3 seems to be high enough
to trigger the formation of the YSOs which are considered to have an average
age of 0.46 (Class I) and 1-3 Myr (Class II) (Evans et al., 2009). Since we
are proposing that the star formation in the central cluster [BDS2003]57 is
happening due to the compression of existing material from the surrounding H
ii regions. In this case, considering the ambient density at the higher end is
a good approximation and suits the triggered star formation scenario.

##  5 Summary and conclusions

In the paper, we aimed to understand the formation of massive stars and young
star clusters in the S193 complex. We probed the physical environment around
the S193 complex by carefully examining the multiwavelength infrared data and
the molecular line data. We present observational findings and conclusions
drawn from the above studies as follows.

  * •

We have identified three clustering of stars in the S193 complex. The
clustering contains previously identified young open clusters and a new
grouping of stars in the south-west direction. The membership of the
identified clustering is constrained using the Gaia-DR3 data, and the clusters
seem connected in the proper-motion space. The distance of the S193 complex is
estimated as 4.84 kpc from the PM data.

  * •

The distribution of dust in the S193 complex is traced using the MIR and FIR
images. The dust is distributed in arc-type structures around the H ii
regions. The PDRs are also traced around the H ii regions, indicating the
significant heating/impact of the massive stars. We also traced Br$\alpha$
emission from the H ii regions, whereas a prominent hydrogen line emission
towards the cluster [BDS2003]57 is observed.

  * •

Using the observed NIR data and the MIR data from the $Spitzer$, we identified
27 YSOs in the S193 complex. Out of the total YSOs, two are Class i, and
twenty five are Class ii objects. Most of the YSOs are distributed towards the
cluster [BDS2003]57. We have also identified 16 molecular clumps in the S193
complex using the $Herschel$ column density map. The most massive clump is
seen towards the central cluster, [BDS2003]57, accompanying several YSOs. This
clump is relatively cold and traced with a temperature $\sim$14 K in the
$Herschel$ temperature map.

  * •

Using our observed NIR data, we have also calculated the slope of the MF for
the central cluster [BDS2003]57. We found a break in the slope of the MF at
1.5 M⊙. In the mass range of $3>M_{\odot}>1.5$ it is found to be $-2.46\pm
0.01$ while in the mass range of $1.5>M_{\odot}>0.3$, it is found as $+0.64\pm
0.20$.

  * •

The distribution of the ionised gas in the S193 complex is examined using the
H$\alpha$ and the $CGPS$ 1420 MHz images. Using radio flux, we further
constrained the spectral type (B0.5-B1) of the ionizing sources for H ii
regions S192, S193, and S194.

  * •

We traced two velocity components towards the S193 complex with velocities
[-43,-46] and [-47,-50] km/s. Two prominent spatially overlapping zones, P1
and P2 peaking in the intensity map were traced towards the center and right-
above the H ii region S193 complex. The overlapping zone P1 accompanies the
[BDS2003]57 clustering, cold gas clump, YSOs.

  * •

We explored the CCC scenario in the S193 complex and it appears that the cause
of the formation of a new generation of stars in this region is not the CCC
process but the feedback from the massive stars.

  * •

We have investigated feedback’s possible role in forming the central cluster
[BDS2003]57 by calculating the total pressure and inspecting the physical
environment around the central clump. It suggests that the massive stars could
have played a role in forming the central cluster, although a detailed
spectroscopic and age analysis is needed to shed more light on this aspect.

## Acknowledgments

We thank the anonymous referee for the constructive and valuable comments that
helped to improve the manuscript. We thank the staff at the 3.6m DOT,
Devasthal (ARIES) and IR astronomy group at TIFR, for their cooperation during
TANSPEC observations. We also acknowledge the TIFR Near Infrared Spectrometer
and Imager mounted on 2 m HCT using which we have made NIR observations. This
research made use of the data from the Milky Way Imaging Scroll Paint- ing
(MWISP) project, which is a multi-line survey in 12CO/13CO/C18O along the
northern galactic plane with PMO$-$13.7m telescope. We are grateful to all the
members of the MWISP working group, particularly the staff members at
PMO-13.7m telescope, for their long-term support. MWISP was sponsored by
National Key R&D Program of China with grant 2017YFA0402701 and by CAS Key
Research Program of Frontier Sciences with grant QYZDJ-SSW-SLH047. D.K.O.
acknowledges the support of the Department of Atomic Energy, Government of
India, under Project Identification No. RTI 4002.

## Data availability

The data underlying this article will be shared on reasonable request to the
corresponding author.

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Table 1:  Statistics of the NIR observations

Band | Number of | Detection |
---|---|---|---
| sources | Limit (mag) |
$J$ | 1341b\+ 168c | 18.70b+20.347c |
$H$ | 1838b\+ 204c | 18.44b+20.149c |
$K$ | 1721b\+ 185c | 17.79b+18.319c |

b: data from $TIRSPEC$ ($10^{\prime}\times 10^{\prime}$ FOV);
c: data from $TANSPEC$ ($1^{\prime}\times 1^{\prime}$ FOV);

Table 2:  Details of the archival data set used in the present study. Survey | Details
---|---
The INT Photometric H$\alpha$ Survey of the Northern Galactic Plane (IPHAS) | (H$\alpha$; resolution $\sim$1′′; Drew et al., 2005)
The Pan-STARRS1 Surveys (PS1) | (0.4-1.2 $\mu$m; Chambers et al., 2016)
Canadian Galactic Plane Survey ($CGPS$) | (1.42 GHz; resolution $\sim$1′$\times$1′csc$\delta$; Taylor et al., 2003)
NRAO VLA Sky Survey | (NVSS; $\lambda$ =21 cm; resolution $\sim$46′′; Condon et al., 1998)
Herschel infrared Galactic Plane Survey (Hi-Gal) | ($\lambda$ =70 - 500 $\mu$m; resolution$\sim$5.8 -46′′; Molinari et al., 2010a)
Warm-Spitzer GLIMPSE360 Survey | ($\lambda$ =3.6 -4.5 $\mu$m; resolution $\sim$2′′; Benjamin et al., 2005)
$JCMT$ Science Archive (12CO(J =3$-$2)) | (345GHz; resolution $\sim$14′′; PROJECT = ‘M08BH15’; Buckle et al., 2009)
Milky Way Imaging Scroll Painting (13CO(J =1$-$0)) | (MWISP, resolution $\sim$55′′; Su et al., 2019)
Two Micron All Sky Survey | (2MASS, $\lambda$ =1.1 - 2.2 $\mu$m; resolution $\sim$2.5′′; Skrutskie et al., 2006)
Wide-field Infrared Survey Explorer | (WISE, $\lambda$ =3.6 - 22 $\mu$m; resolution $\sim$6 -12′′; Wright et al., 2010)
Table 3:  Sample of 80 stars identified as a member of the S193 complex. The complete table is available in the electronic form only. ID | $\alpha_{(2000)}$ | $\delta_{(2000)}$ | Parallax$\pm\sigma~{}~{}~{}~{}~{}$ | $\mu_{\alpha}\pm\sigma$ | $\mu_{\delta}\pm\sigma$ | $G$ | $G_{BP}-G_{RP}$ | Probability
---|---|---|---|---|---|---|---|---
| $(degrees)$ | $(degrees)$ | (mas) | (mas/yr) | (mas/yr) | (mag) | (mag) | (Percentage)
1 | 41.885990 | 61.909958 | $0.065\pm 0.037$ | $-0.345\pm 0.016$ | $0.139\pm 0.035$ | 15.842 | 1.262 | 100
2 | 41.916260 | 61.923225 | $0.262\pm 0.257$ | $-0.397\pm 0.112$ | $0.153\pm 0.225$ | 19.020 | 1.616 | 98
3 | 41.906441 | 61.927036 | $0.444\pm 0.340$ | $-0.198\pm 0.137$ | $0.416\pm 0.314$ | 19.438 | 2.344 | 93
4 | 41.916271 | 61.928421 | $0.211\pm 0.133$ | $-0.328\pm 0.055$ | $0.034\pm 0.117$ | 18.057 | 1.333 | 99
Table 4:  Clumps identified in the S193 complex using the $Herschel$ column density map. Center coordinates, radius (column 3) and mass (column 5) corresponding to each clump are tabulated below. ID | $\alpha_{(2000)}$ | $\delta_{(2000)}$ | Radius | Mass
---|---|---|---|---
| $(^{h}:^{m}:^{s})$ | $(^{o}:^{\prime}:^{\prime\prime)}$ | (pc) | (M⊙)
1 | 02:47:27.1 | 61:56:43 | 1.5 | 1141.9
2 | 02:47:41.5 | 62:00:13 | 1.8 | 912.2
3 | 02:47:49.2 | 61:55:43 | 1.2 | 342.7
4 | 02:47:16.0 | 61:56:19 | 1.3 | 444.6
5 | 02:47:27.9 | 61:58:43 | 0.9 | 227.5
6 | 02:47:36.4 | 61:56:49 | 1.0 | 268.2
7 | 02:47:55.2 | 62:00:19 | 1.5 | 527.8
8 | 02:47:35.6 | 61:57:49 | 0.9 | 200.9
9 | 02:47:49.1 | 61:54:37 | 1.3 | 334.0
10 | 02:47:30.5 | 61:59:31 | 1.2 | 332.1
11 | 02:47:35.5 | 61:53:01 | 1.5 | 431.5
12 | 02:47:16.8 | 61:58:55 | 0.9 | 174.1
13 | 02:47:33.0 | 61:54:43 | 1.0 | 200.5
14 | 02:47:16.8 | 61:59:43 | 0.9 | 193.7
15 | 02:47:24.5 | 61:54:31 | 0.9 | 161.7
16 | 02:47:20.3 | 61:55:01 | 1.1 | 224.6


# Robot Grasping and Manipulation: A Prospective

Claudio Zito1
1 Technology Innovation Institute (TII), Abu Dhabi, United Arab Emirates
Claudio.Zitotii.ae

“A simple handshake would give them away”. This is how Anthony Hopkins’
fictional character, Dr Robert Ford, summarises a particular flaw of the 2016
science-fiction _Westworld_ ’s hosts. In the storyline, Westworld is a
futuristic theme park and the hosts are autonomous robots engineered to be
indistinguishable from the human guests, except for their hands that have not
been perfected yet. In another classic science-fiction saga, scientists unlock
the secrets of full synthetic intelligence, Skynet, by reverse engineering a
futuristic hand.

In both storylines, reality inspires fiction on one crucial point: designing
hands and reproducing robust and reliable manipulation actions is one of the
biggest challenges in robotics.

Solving this problem would lead us to a new, improved era of autonomy. A
century ago, the third industrial revolution brought robots into the assembly
lines, changing our way of working forever. The next revolution has already
started by bringing us artificial intelligence (AI) assistants, enhancing our
quality of life in our jobs and everyday lives–even combating worldwide
pandemics [1].

However, for robots, stepping outside the assembly lines requires a different
set of skills that synthetic intelligence still lacks. Such skills are closely
entangled with the notion of embodiment. A concept in which intelligence is
not only centralised at the brain level but distributed along the body and
influenced by its environment.

The field of robot grasping and manipulation has seen an exponential growth of
attention from the research community in the last two decades. Researchers
have made significant progress in many different areas that feed the
perception, planning, and control loop so crucial for these tasks. The renewed
interest from the general public, industries, and government agencies has
contributed to developing new applications and case scenarios-from simple
pick-and-place to handling packages or assembly of mechanical components.
Nevertheless, the field has not grown evenly; some challenges received or are
still receiving a great deal of attention, while others remain unsolved and
unpopular. Yet, others have vanished or changed in their core due to newly
available technologies or solution models.

Reliable grasping and manipulation in real-world applications is still out of
reach due to several reasons. At a mechatronic level, simple end-effectors,
such as parallel grippers or vacuum cups, eliminate model complexity and
redundancy at the cost of strong limitations in the way they can grasp or
manipulate the object in-hand. Anthropomorphic end-effectors may provide
essential features for manipulation, such as movable thumbs or rolling
fingers, but the control complexity and the lack of adequate sensing make
these devices still impractical. At an algorithmic level, the robotic
manipulation pipeline requires many components that preclude platform
independence, and the robustness and resilience of the models are challenged
by even minimal changes in the setup or environmental conditions.

Encoding any conceivable setup or condition that a robot may face is not a
viable solution. However, there is enough evidence that biological brains do
not use this tactic either but work as Bayesian machines whose priors are
combinations of model-based and data-driven experience. Hence, generative
models (GMs) such as kernel density estimation (KDE) or deep learning (DL)
have become well-established tools in robotics. GMs attempt to learn the true
distribution of the data from sampled observations. When faced with previously
unseen data, they rely on learned features to find common patterns and compute
a set of valid candidate solutions. However, training GMs for robot
manipulation needs physical interaction data, which is hard to generate.

A significant amount of work has been dedicated to robot perception and their
ability to deal with unstructured environments, especially extreme ones, e.g.,
[2]. Since depth cameras and high-precision tactile sensors have become widely
accessible, robot perception fast-tracked unlocking potentially game-changer
solutions, e.g. [3]. Nevertheless, any technology has its weaknesses;
occlusions and shiny or translucent materials yield an incomplete scene
reconstruction, and tactile information may be noisy. Rather than attempt to
eliminate the source of uncertainty, robots need to learn how to deal with it.
In [4], a deep learning framework is presented for a simulated robot drummer.
Audio, video and proprioception sensing data are collected to retrieve the
missing information from the other inputs when a modality is faulty.

Robots should also use perception uncertainty as an indicator to modify their
behaviour. High uncertainty should lead to more conservative strategies.
Imagine reaching into the fridge to grasp a milk bottle that you can only
partially see and how this would affect your reaching strategy. Robots can
also achieve this by integrating perception uncertainty into their motion
planner, as in [5, 6, 7, 8, 9].

Another example of dealing with uncertain perception is provided by the
humanoid robots Vito and Boris, respectively developed by the Centro Piaggio
at the University of Pisa and the Intelligent Robotic Lab at the University of
Birmingham under the European FP7 grant PaCMan [10]. In [11], the robots
outsmart in-hand self-occlusions and vision-driven uncertainty of the object
to be manipulated by again combining visual clues and clever tactile
exploration of the object surface.

One of the breakthroughs in grasping and manipulating novel objects was due to
contact-based formulations [12]. Representation of contacts via establishing a
set of relations between (vision-extrapolated) geometrical features on the
object’s surface and the robot’s links have proven to be a powerful solution.
Geometrical features are typically extrapolated around the contact points in a
paradigm called learning from demonstration (LfD). A teacher is required to
present a feasible and robust contact to the robot, but from the geometrical
features enough statistic is acquired to learn contact densities in a one-shot
fashion as generative contact models [13, 14]. Since it is natural to assume
that many objects share many local similarities in terms of geometrical
features, these models tend to generalise very well within and across object
categories. Task-dependent constraints can be added in the formulation as
optimisation procedures, but this requires a good knowledge of the task and,
often, ad-hoc solutions. Very recently, a contact-based formulation has also
been successfully applied for the first time to the problem of aerial grasping
[15]. Although it should be considered a seminal work, the proposed framework
extends the one-shot learning paradigm enabling unmanned aerial vehicles with
cable-suspended passive grippers to compute the attach points on novel
payloads for aerial transportation with no need for handcrafted task-dependent
features.

Generative contact models have also been investigated for tasks beyond
grasping. From a young age, humans learn an internal model of how the world
works from data-driven experience and curiosity-driven interaction. This
internal model plays a crucial role in predicting the outcome of an
interaction, even in novel contexts. In [16, 17], the contact-based
formulation enables us to learn an internal model for predicting push motions
of previously unseen objects, while in [18] a planner uses black-box motion
predictors to move objects to the desired configurations. Although the theory
behind motion prediction is well-established, the existing methods in the
literature are not yet in use in industrial applications. No robotic system
can, for example, insert a box of various products onto an over-the-head store
shelf by exploiting push operations and the relative contacts and forces
generated [19].

Of growing interest, it is also the field of physical human-robot interaction
(pHRI), where a human operates with a robot to accomplish manipulative tasks
[20]. Remote pHRI is crucial to guarantee the safety of a human operator in
extreme and dangerous tasks, such as underwater maintenance, nuclear waste
disposal, or rescue missions [21]. At the same time, wearable robotics, such
as prostheses or exoskeletons, have the potential of fully restoring the
functionalities of missing body parts or providing super-human capabilities to
users [22]. Unfortunately, the dexterous capabilities of conventional
interfaces for such devices fall a long way short of those we are used to when
physically operating with our hands. Additionally, long and tedious training
sessions are required for training the users to control the system
proficiently. To make more intuitive and accessible interfaces, we need to
reliably estimate the user’s intention from biological and behavioural clues
and map this into appropriate motion commands for the robotic counterpart. In
[23, 24], an AI assistant for remote teleoperation proactively responds to the
user’s motion intentions in a predict-then-blend fashion. The system perceives
a cluttered scene and, on the fly, predicts candidate grasps for the visible
objects and, for each grasp, computes a feasible motion plan. The user
commands the robot towards the desired object via a simple interface, e.g., a
keyboard or a joystick. The robot attempts to follow the user’s wanted motion
by latching on to the more similar pre-planned trajectory.

AI has the potential to revolutionise pHRIs by making them user-friendly and
intuitive. For example, activation patterns of muscles can be learned through
surface electromyography (sEMG) signals to control prosthetic devices.
However, since the biological signal is affected over time due to fatigue,
electrode displacement, or sweat, no machine learning system can reliably
generate the intended control for a long period of time. Paradigms to overcome
this problem have been proposed in the literature, but mostly rely on
detecting features that are generally not desired, such as oscillatory
behaviour or high accelerations. It remains unclear whether these models could
detect a shift in sEMG patterns, resulting in plausible predictions but
producing the wrong hand configuration. In [25], a GM-based failure detection
was presented that instead spots every instance where the interpretation does
not match the user’s intention by incorporating situational context
information. Yet, this approach works in a lab where calibrated cameras
virtually reconstruct the scene to extrapolate the context. To see this
working outside the lab, we would need a more portable technology for 3d
reconstruction, which is not ready yet but conceived in the _Mission
Impossible_ ’s smart glasses.

The next decade will be even more exciting for this field. All over the world,
roboticists have started moving away from highly-engineered solutions by
embracing more flexible and reliable approaches [26]. At the current state,
grasping with imperfect perception is still one of the main issues that slows
progress and it will require both research and engineering work [27]. Objects
with challenging shapes and surfaces can be dealt with but with tailor
solutions to known objects, such as model-based 3D reconstructions and by
design specific end-effectors. Hardware and software integration is still
tedious and time-consuming, but multiple efforts have been made to alleviate
it, e.g. ROS, MoveIT, and we will witness a further increment of such tools in
the next years to come. At this pace, it is safe to assume that robust and
precise grasping will be consolidated for many different scenarios and
applications and we will witness advanced robot pick-and-place in the
agricultural industry and delivery services. Beyond pick-and-place tasks, many
of the current solutions will fall apart. Grasping for manipulation purposes
needs planning while considering task-dependent constrains. Many of these
constrains are hard to encode and on-the-fly generation of contacts yields to
unreliable solutions even for known objects. This will remain a hard challenge
for the next decade on which many researchers will focus their attention. In
the time of autonomous racing cars, dynamics of a high-speed moving vehicles
can be legitimately approximated for optimal control, yet making predictions
for manipulation tasks has not received the same attention from the community.
Available solutions generate plans to merely avoid collisions assuming quasi-
static dynamics by listing an handful of possible outcomes matched against a
set of predefined motion strategies. Finite enumeration of states is not an
ample strategy for such tasks, and the community will need to investigate new
routes for more sustainable solutions. In-hand manipulation is still at its
dawn. Clever designs of tools and end-effectors can achieve specific in-hand
manipulation, but without an adequate sensory feedback and clever control
strategies this problem remains one of the most challenging tasks a robot can
face. Finally, in the last decade we have seen an increasing interest towards
pHRI and its applications. Exoskeletons and prosthetic devices are getting
smarter and a large amount of effort has been, and will be, dedicated to
investigate more intuitive interfaces. Augmented and virtual reality
technology will play a major role in this field with exciting new immersive
solutions.

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