# See Spot Guide: Accessible Interfaces for an Assistive Quadruped Robot

Rayna Hata1 , Narit Trikasemsak2 , Andrea Giudice3, Stacy A. Doore2 1 Rayna
Hata is with the Robotics Institute, Carnegie Mellon University, Pittsburgh,
PA, USA<EMAIL_ADDRESS>2Narit Trikasemsak and Stacy A. Doore are with
the Department of Computer Science, Colby College, Waterville, ME, USA
<EMAIL_ADDRESS>⟨0000-0001-9682-1097 ⟩ ⟨0009-0006-0280-1305 ⟩
⟨0000-0002-8322-6798 ⟩

###### Abstract

While there is no replacement for the learned expertise, devotion, and social
benefits of a guide dog, there are cases in which a robot navigation assistant
could be helpful for individuals with blindness or low vision (BLV). This
study investigated the potential for an industrial agile robot to perform
guided navigation tasks. We developed two interface prototypes that allowed
for spatial information between a human-robot pair: a voice-based app and a
flexible, responsive handle. The participants (n=21) completed simple
navigation tasks and a post-study survey about the prototype functionality and
their trust in the robot. All participants successfully completed the
navigation tasks and demonstrated the interface prototypes were able to pass
spatial information between the human and the robot. Future work will include
expanding the voice-based app to allow the robot to communicate obstacles to
the handler and adding haptic signals to the handle design.

###### Index Terms:

Agile robots, Blind-Low Vision (BLV), Co-design, Value Sensitive Design

##  I Introduction

Guide dogs are one of the most trusted forms of assistive navigation for those
in the blind-low vision (BLV) community. Approximately 20,000 guide dogs help
BLV individuals successfully operate in the world by navigating unfamiliar and
complex surroundings on a daily basis[1]. The benefits of guide dogs have been
well documented with most studies observing that guide dogs provide additional
social support for their handlers in the form of companionship, bonding, and
protection[2]. While working with a highly trained guide dog has many
benefits, there are also significant responsibilities and limitations. This
includes the high costs of training and care, travel restrictions, and a
limited working span of about 6 years. Handlers must also keep applying for
and training with new dogs as life circumstances and assistance needs change
with age[3].

As assistive technologies have advanced in recent years, many researchers are
experimenting with a way to address some of the challenges presented by guide
dogs, not as a replacement but as an additional tool or resource for BLV
individuals who may occasionally require an alternative or temporary
navigation assistant. These types of devices include smart canes [4],
smartphone navigation applications using computer vision systems[5], OSM [6],
GoodMaps[7]), and there also competitions demonstrating the performance of
these systems for everyday tasks and navigation challenges[8].

Robot navigation assistants could be especially useful in indoor settings such
as airports, convention halls, and other public spaces where navigation can be
more difficult than in outdoor settings. While there are several research
teams working on different types of robotic guides, this paper reports on the
results of a study that investigates the potential for using industrial
quadruped robots as navigation assistants. The present study focuses on the
following research questions:

  1. 1.

What are the necessary features for a multisensory interface to facilitate
critical human-robot interactions for blind navigation in a multilevel indoor
environment?

  2. 2.

What human-dog pair navigation behaviors and tasks can be reproduced with an
industrial quadruped robot?

This paper investigates the potential for industrial quadruped robots to
assist humans in executing similar navigation tasks that human-guide dog pairs
perform every day. This study is grounded in a co-design framework[9] and uses
value-sensitive design (VSD) principles and methods[10] to design a
multisensory interface prototype for the use of these robots in blind
navigation tasks. We will first review the foundational skills and behaviors
that must happen in successful human-guide dog pairs, the limitations of real-
time human-dog communication in navigation tasks, and current research on
using robots in blind navigation.Together with a co-author Andrea Giudice, a
BLV Assistive Technology Specialist and guide dog handler, we co-designed and
prototyped two interface prototypes to interact with a commercial industrial
agile robot with the goal of developing an embodied navigation assistant. We
report the preliminary results of a human-robot pair study with sighted
participants and the agile robot operating as a team performing a variety of
assistive navigation and retrieval tasks.

##  II Related Works

###  II-A Blind and Low Vision Navigation

For the 260 million individuals worldwide who have profound vision impairment,
non-visual navigation can be a daily challenge as one must learn to rely on
other sensory information to explore and navigate the world[11]. Most
individuals without the use of functional vision will go through Orientation
and Mobility (O&M) training to learn how to navigate independently and safely.
This includes training on how to determine knowledge of intersection geometry,
the state of the traffic signal based on traffic flow analysis, and awareness
of nearby landmarks to understand surroundings. O&M training helps individuals
with BLV to use spatial reasoning and their navigation tools to navigate to
their destination[12]. However, O&M training is typically focused on learning
a specific location, route, or area, and often requires a BLV navigator to
revisit O&M training when moving to a new location. Common assistive
approaches to non-visual navigation include using white canes, guide dogs,
human assistants, environmental cues, or a combination of approaches depending
on the navigation context[13].

Modern use of guide dogs for non-visual navigation began in Germany during
World War I, where the first guide dogs helped veterans blinded in combat.
U.S. public knowledge of guide dog use has increased since the 1920s after a
popular movement helped to establish the rights of handlers and dogs being
allowed to operate freely in public spaces and the first guide dog training
school in the U.S.(Eustis, 1927). There are approximately 20,000 official
guide dogs in the U.S. with a greater demand than can be met by guide dog
organizations. While guide dog schools in the U.S. provide their services free
of charge to qualified BLV individuals, the cost of breeding, raising, health
care, and ongoing training of the dog and their handler plus ongoing support
ranges from $40,000-60,000[3].

While there is no standard size or breed for a guide dog, the dog must be
appropriately sized and matched with its handler. Size is important because
the height of the dog at the shoulder, plus the length of the harness, must
fit comfortably with the height of the handler. The most common breeds used as
guide dogs tend to be large in size such as German Shepherds (27–40 kg and
558–660 mm high) and Labrador/Golden Retrievers (27-36 kg and 558–635 mm high)
because of their size and weight range.

###  II-B Quadruped Robots for Navigation Assistance

Experimentation with robots as navigation assistants has increased over the
last ten years as robots have become more agile and easier to operate
independently. Many of the current research programs investigating the use of
quadrupedal robots for assistive navigation utilize small to midsize robots to
simulate interactions between handler-dog pairs. In [14] the researchers use a
midsize quadruped robot, a Unitree Go1 model [15], which is equipped with a
custom handle that is able to receive directional commands from the user.
Using the commands, the robot then plans a path around the obstacles to get to
the next point and guides to user. Preliminary tests showed that the system is
functional in most settings. Another study [16], also uses a similar mid-sized
quadruped robot, a Unitree Aliengo[17], as a navigation tool designed for
assisting BLV individuals. This study developed a delayed harness model that
tries to mimic similar interactions that transpire between a human and their
guide dog. Reported results suggested accurate behavior prediction and better
navigation performance with this handle design when testing blindfolded
sighted participants.

[18] implements the use of the Mini-cheetah robot[19] to create a robotics
guide dog navigation system paired with a flexible leash system. The purpose
of the leash is to get rid of the constraints that a rigid leash would bring
in tight corners or narrow hallways. The leash is able to switch between a
taut position and a slack position depending on the situation. They were able
to successfully complete their study on blindfolded users, having them
navigate through cluttered spaces.

While having a slack leash may help improve movement in tight corners or
spaces, the lack of rigidness diminishes the effectiveness of the
communication between the user and the guide dog. Furthermore, in all of the
studies above, the size of the robots is a good deal smaller than the most
common guide dog breeds (Labradors, Retrievers, Shepherds). Smaller to mid-
sized agile robots used in these non-visual navigation studies require a long
handle to reach between the human-robot resulting in awkward positioning when
compared to a typical human-guide dog team, limiting body communication based
on traditional training protocols.

###  II-C Boston Dynamics Spot Explorer

The Boston Dynamics Spot Explorer robot is an industrial quadrupedal, agile
robot developed in 2016, and made commercially available in 2019. The Spot
Explorer robot is considered large among the commercially available quadrupeds
[20]. This means the Spot model is approximately four times the size of the
MIT mini Cheetah and two times the size of the Unitree robots used in the
studies noted above. The Spot Explorer robot houses five stereo cameras and
LIDAR. The standard Spot Explorer robot can move at a quick pace with advanced
mobility and perception features to navigate stairs and rough terrain while
collecting data in real time to avoid obstacles. The robot can be customized
to move beyond its five standard data processing abilities of perception,
computation, autonomy, integration, and manipulation. (See Table I for a
comparison of robots).

TABLE I: Specifications of Small, Medium, and Large Agile Robots for Guided Navigation Robot |  Length (mm) |  Width (mm) |  Height (mm) |  Weight (kg) |  Speed (km/h) |  Load (kg) |  Degrees of Freedom |  Battery Run Time (minutes)
---|---|---|---|---|---|---|---|---
Mini-Cheetah [19] |  480 |  270 |  300 |  9 |  8.8 |  10 |  12 |  120
Unitree Go1 [15] |  588 |  220 |  290 |  12 |  12 |  5 |  12 |  120
Unitree Aliengo [17] |  650 |  310 |  600 |  21 |  5.4 |  10 |  12 |  240
Spot Explorer [20] |  1100 |  500 |  660 |  32 |  5.8 |  31 |  12 |  90

(a) Mini-Cheetah

(b) Unitree Go1

(c) Aliengo

(d) Boston Dynamics Spot

Figure 1: Commercially available quadruped robots used in nonvisual navigation
research from smallest to largest: a) Mini-cheetah, b) Unitree Go 1 and Go 2,
c) Unitree Alien Go, and d) Boston Dynamics Spot Explorer (with arm).

The Spot Explorer models are most frequently used in industrial settings
performing tasks in monitoring gauges, inspecting equipment for failure
points, constructing maps of large factory complexes, and detecting harmful
emissions in chemical and energy plants[21]. Other uses for the Spot Explorer
model that are less common but have a higher public profile have been used in
advertising campaigns[22], fashion runways[23], halftime shows[24], and in
some cases, police surveillance and patrol[25]. To our knowledge, the use of a
Boston Dynamic Spot Explorer model as a navigation assistant is unique to this
study.

##  III Assistive Robot Guide Co-design

The term co-design refers to the method of designing an assistive technology
with individuals who bring their own lived experience to the design,
development, and testing cycles [26]. The principle of co-designing is closely
tied to the disability rights movement philosophy of “nothing without us”. Too
often, assistive technologies are designed independently of the intended user
community, who are asked too late (if at all) to test an engineered solution
created by a team without any first-hand knowledge of lived experience
challenges and concerns that can directly impact individual safety and well-
being. In order to fully incorporate BLV lived experience and handler
expertise, our team began the project by interviewing and then working through
a co-design process with our co-author, a BLV guide dog handler who has
trained and worked with seven guide dogs on a daily basis for over 30 years.

We have also incorporated a set of underlying responsible computing
principles, prospective study methodologies, and design practices known as
value-sensitive design (VSD)[10]. VSD requires that prospective design
projects include working through a formal process to identify and communicate
with direct and indirect stakeholders and their potentially conflicting values
in the prospective design process. In this project, we identify BLV guide dog
handlers as the direct stakeholders and the general public as indirect
stakeholders who must navigate in the same public spaces as the robot
navigation assistant (e.g., retail areas, airports, sidewalks, etc.).

###  III-A Co-design Data Collection

At the beginning of this process, our team spent hours together observing
human-dog interactions, discussing communication cues and signals, body-
movement patterns, and targeted behavior rewards that our co-designer uses to
reinforce good guiding behaviors with her young guide dog. Guide dogs must
learn many skills to help their handler navigate complex indoor and outdoor
environments such as walking at a constant pace and gait, walking backward if
needed, avoiding obstacles, opening doors, navigating stairs, and cutting
through crowds. Through these observations and interviews during the co-design
process, the team discussed core guide dog behaviors in order to compare
standard guide dog behaviors to the robot’s embedded functions and features.
Comparing the basic navigation skills required of a guide dog with the Boston
Dynamics robot’s existing features provides a set of simple measures to test
the robot’s potential to serve as a non-visual navigation assistant (Table
II). Guide dogs also learn to ignore distractions that interfere with their
duties and are trained to ignore compelling objects such as stray balls, food,
other animals, and people who want to pet and talk with them while they are
working. For a robot, these types of distractions do not present a problem.

Finally, while the focus of this paper may be on the viability of BLV human-
robot pairings for navigation, we are in parallel considering the values,
concerns, and safety of the indirect stakeholders (sighted public) in these
spaces. Given the early stage of the interface designs, we wanted to observe
sighted participants in a human-robot pair guiding and voice command tasks to
assess their comfort around the robot and their ability to work with the robot
to complete the protocol. Similar research is being conducted on reported
levels of trust and safety using the same Boston Dynamics Spot Explorer model
in experiments with sighted participants who are not guiding the robot but
instead are directed to walk past the robot in a close contact setting[27].
This study found there was a higher level of trust when the robot was wearing
a designated service animal vest and when a human operator was walking with
the robot.

##  IV  Human-Robot Interaction Prototypes

Our goal was to use the observations and input from our co-design partner in a
preliminary study to assess the industrial robot’s potential as a navigation
assistant and to compare the results to findings from previous studies that
employed smaller robots with the same types of navigation tasks. Our first
challenge was to design two prototype human-robot interaction interfaces that
would allow the human-robot pair to communicate with one another during a set
of simple navigation challenges: 1) a functional handle attached to the robot
that would communicate body movements between the robot and the handler; and
2) a voice-based interface that would allow the handler to give voice commands
to the robot through simple directions.

###  IV-A Prototype Handle Design

Figure 2: Ruffwear Guide Unifly dog harness Figure 3: Standard Guide Dog
Harness

After observing the body placement of our co-design partner next to her guide
dog, we recognized that the Spot Explorer dimensions and camera placements
would require designing and building a modified handle to be placed on the
rear half of the robot’s outer shell. The integrated cameras on the robot are
required for object detection and avoidance during navigation. If the robot
detects an obstacle (i.e., a human handler) blocking one of the cameras the
robot will at first move away in order to avoid a collision. This would be
counterproductive and dangerous if the robot was acting as a guide. The handle
needed to be placed far enough back on the robot to avoid the right rear side
camera but still near enough to the back center to provide clear body signals
for the handler to understand and react in parallel to the robot’s movements.
The design of the prototype handle was modeled off a newer style of
harness[28] that is designed for BLV runners using their guide dogs (Figure
2). While not a typical guide harness system, it is becoming increasingly
popular among guide dog handlers because of the greater freedom of movement
and reactions it allows for the handler’s wrist.

Standard guide harnesses(Figure 3), as well as the Ruffwear harness, require a
strap that would typically be placed around the guide dog’s chest and abdomen.
The robot’s rectangular body structure, size, and cameras would not work with
these harness systems because the straps would block the front and side
cameras. Thus, we designed a custom handle without body strapping that could
be positioned on the back of the robot. The first version of the guide handle
was created using OnShape CAD software and was created using a 3D printer. The
original design of the handle (Figure 6 consisted of two forks connected by a
universal joint (U-joint) and a handle that was attached to the upper fork.
The universal joint created a similar movement as the Ruffwear’s handle,
allowing for a 120-degree turn on both vertical and horizontal movements.
However, after some early testing, we found the 3D printed U-joint was not
strong enough to withstand the continuous or sudden movement interactions
between a human and the robot, snapping after a few rotations.

Version 2.0 of the handle (Figure 6) shared the same basic shape as version
1.0, however, we replaced the 3D-printed u-joint with one made from two metal
bolts. The metal bolts proved to be more durable than the 3D-printed pieces
and allowed for greater freedom of movement of the handler’s wrist. The
printed shaft of the handle was replaced with a short PVC pipe with an elbow
curve at the top. The handle was mounted at the rear to avoid the robot arm
and to help the handler stay out of the right-side rear camera view. During
the next round of pilot tests, the handle proved to be too short causing the
handler to drift into the camera’s view while turning. This created a safety
hazard causing the robot’s actions to become increasingly erratic, putting the
handler in harm’s way. In order to rectify this issue, version 3.0 of the
handle was attached to a longer shaft of PVC pipe (Figure 6). With the
additional length, the handler could remain outside the camera’s view yet
still have their body close enough to the robot to understand and anticipate
its guiding movements. The final user studies were conducted using the third
iteration of the handle design.

Figure 4: Version 1

Figure 5: Version 2

Figure 6: Version 3

###  IV-B Prototype Voice-based Interface Design

Guide dogs and their handlers communicate in a variety of ways. Some of this
communication happens nonverbally with shifting of body positions,
hesitations, longer pauses, and directional movements. In other cases, the
handler gives direct verbal commands such as forward, or orientation (right,
left), or follow-up commands such as “find railing” when the guide dog stops
suddenly to indicate they are approaching a set of stairs. In order to provide
a mechanism for the handler to use voice cues for communication, our team
designed a prototype voice-based smartphone app that would allow the handler
to give the robot a set of basic direction and orientation commands. The app
was designed using Swift and UIKit to interpret the voice command and pass it
to the robot’s network, which would in turn pass the pre-programmed functions
to the robot (Figure 7).

Figure 7: Voice-based and Touchscreen Interface

The voice-based control prototype was designed with a Handler UI and a backend
networked controller. The frontend iOS app provided the handler with an
accessible interface to pass simple verbal commands to the robot. The backend
served as the control system for Spot and managed all interactions with the
robot. In this preliminary study, we used the Raspberry Pi (RPI) to serve as
the backend host and an iPhone 12 to take advantage of Apple’s built-in
accessibility features. The two systems communicate with each other through
Mosquitto (MQTT), a framework developed for IOT applications that has more
recently been applied to robotics. The RPI served as a MQTT broker as it
managed all incoming messages and routed them, while the backend files acted
as a client and subscribed to the broker, even though they were on the same
device. The iPhone also subscribed as a client, meaning both components could
send and receive information (Figure 8).

Figure 8: Voice Interface Network Architecture

The prototype voice-based interactions were designed to use a combination of
finger taps for initiating and sending the commands and voice to give the
commands. The handler spoke the command slowly and clearly, using a short
pause between words to help the app transcribe the commands correctly. The
Apple Speech framework then transcribed the handler’s command and displayed it
on the screen in real time. The interface also had the option of using a
start/send command button to accommodate individuals with residual vision who
might want to control the text size and high-contrast features. If it was a
valid command, the robot performed the action and stopped. For the first
iteration of the prototype, the handler could give the following valid
commands: 1) “ Spot, go forward” three steps forward and stops, 2) “Spot go
backward”: three steps and stops, 3) “Spot go right” or “Spot go left”: three
steps to 90 degrees from its original position.

##  V Study Design and Methods

Our goal was to test both the handle and mobile app prototypes with a group of
sighted participants and our guide dog expert/co-designer to assess: 1) how
effectively the human-robot pair could work to complete a few simple
navigation tasks, 2) how effectively each of the prototype communication
interfaces (i.e., handle and app) worked independently, and 3) the handler’s
reported level of comfort and confidence in the robot’s interactions during
the navigation tasks.

###  V-A Participants

The user study consisted of a convenience sample of 20 sighted undergraduate
students and faculty recruited from a liberal arts college through email
invitations to the campus community. Participant demographics were not
considered an important factor in the analysis, however, slightly more than
half of the participants self-reported as female-identifying and the reported
age of the sighted participants ranged from 18-60 years old. Participants in
the sighted user study were able to walk independently around campus. While
there was a range of reported previous exposure to the Spot Explorer robot,
about half of the sighted participants reported brief encounters with the
agile robot around campus, none of the participants had used the prototype
handle or app or been guided by the robot prior to their participation in the
study. A second single-participant study was conducted with our co-designer, a
BLV guide dog handler, for 36 years. She completed the same practice procedure
and navigation study protocol as the sighted participants. She also completed
an additional outdoor navigation protocol designed specifically for her at her
request. All participants completed a post-study feedback survey. Participants
were study volunteers and were not compensated for their participation in the
study. The user study protocol and post-study survey were reviewed and
approved by the IRB and all participants indicated their formal informed
consent prior to the start of the study.

###  V-B Protocol and Data Collection

The user study protocol was divided into two parts. The first part consisted
of a practice session to acclimate the participant to the robot’s movements
while using the handle and the second part consisted of an autonomous walk
through an indoor environment (i.e., library) where the participant was being
guided by the robot through the use of the prototype handle. The practice
session was designed to teach three separate human-robot pair movements:
walking forward, turning left and right, and ascending and descending a short
flight of stairs. The participants were told they could repeat any of the
actions until they felt comfortable with executing the movements with the
robot, although most participants did not request additional practice time.
The researcher controlled each of the movements during the practice sessions
and the participants were verbally notified of what action the robot would
take before each part of the session.

The navigation tasks were considered completed if a participant was able to
complete the following tasks independently after the brief practice session:
1) the participant could successfully give four commands to the robot via the
voice interface app, 2) the participant could use the handle to let the robot
navigate 100-foot curved auto walk route and back to the origin location, and
3) the human-robot pair could navigate 20 feet to a short flight of stairs,
ascend the four steps, follow the robot as it turned around descend the steps
(i.e., safest way for this type of agile robot to descend) allowing the handle
to pivot 180 degrees in their hand. Participants were also asked to complete a
post-study survey about the handle and voice command app prototype’s
functionality and their levels of trust in the robot interactions during the
trials. Our co-designer completed the same practice session and study protocol
after completing most initial testing sessions with sighted participants. This
was for safety reasons as well as scheduling accommodations.

The navigation tasks were performed using the robot’s auto-walk feature, where
the robot autonomously navigated a pre-planned route with the user. While the
route was pre-planned, the robot needed to make real-time decisions based on
new information such as obstacles or approaching humans in a busy library
setting. The route contained the type of simple indoor paths that a handler-
guide dog pair might need to navigate daily. To complete the multilevel
navigation, the robot needed to lead the participant to the start of a short
flight of stairs (4 steps to a landing area) while maintaining a straight path
and a constant pace.

Approaching the stairs, the robot would stop and signal to the participant
that they were about to go upstairs by lowering its rear down and tipping its
front up so that the participant’s arm would get raised by the attached
handle. After a 3-second pause, the robot would ascend the stairs and then
pivot so that its back would be facing down the stairs that it had just
climbed. Before descending down the stairs, the robot would once again pause
and signal to the user that they are about to go downstairs by raising its
back end and lowering its front end so that the participant’s arm would be
pointed down. After the pause and signaling, the robot resumed moving to
descend the stairs. After reaching the bottom of the stairs, the robot turned
to the right to lead the participant along the rest of the hallway route and
then turned around to move back to the starting point.

###  V-C Post Study Survey

After completing the navigation tasks, participants were asked to fill out a
survey ranking their level of agreement with statements about their
experience. The three questions asked about the handle and the user’s comfort,
another set of four questions asked about the functioning of the voice-based
app, and the last 3 questions asked about whether the user felt comfortable
being around the robot. The Likert scale was anchored from one to five, with
1= strongly disagreeing with the statement and 5=strongly agreeing with the
statement. As this study is exploratory and descriptive in nature, the survey
was designed to be short and only focused on the key prototype features and
participant perceptions related to overall trust and safety interacting with
the robot.

##  VI Results

The user study navigation protocol results were measured by the successful
completion of the navigation protocol after a brief training session. As this
was a preliminary study of the prototypes, there were no measures of accuracy
or time on task, instead, we measured success as if the participants (BLV Co-
designer = 1, Sighted Participants = 20) could complete each navigation task
using the handle prototype and if the participants could complete the voice
command tasks using the voice-based interface (BLV Co-designer = 1, Sighted
Participants = 20).

###  VI-A Co-designer Responses and Feedback

Our team spent hours together observing our co-designer demonstrating human-
dog interactions, discussing communication cues and signals, body-movement
patterns, and targeted behavior rewards used to reinforce good guiding
behaviors with a young guide dog. Guide dogs must learn many skills to help
their handler navigate complex indoor and outdoor environments such as walking
at a constant pace and gait, walking backward if needed, avoiding obstacles,
opening doors, navigating stairs, and cutting through crowds. Through these
observations and interviews during the co-design process, the team identified
core guide dog behaviors in order to compare standard guide dog behaviors to
the robot’s embedded functions and features.

Comparing the basic navigation skills required of a guide dog with the Boston
Dynamics robot’s existing features provides a set of simple measures to test
the robot’s potential to serve as a non-visual navigation assistant (Table
II). Guide dogs also learn to ignore distractions that interfere with their
duties and are trained to ignore compelling objects such as stray balls, food,
other animals, and people who want to pet and talk with them while they are
working. For a robot, these types of distractions do not present a problem.

During the co-design process, we also learned about the potential limitations
of the robot that would need to be adjusted or re-engineered to meet a basic
level of navigation assistant functionality. For example, guide dogs need to
recognize curbs and stairs and avoid hazards from above and below, such as
low-hanging limbs, power lines, and potholes. In addition to listening to
their handler’s commands, guide dogs are trained to reason beyond the commands
to perform acts of intelligent disobedience. This is when the dog understands
something about the environment that the handler does not (e.g., a speeding
car or sudden drop) and this is a critical safety skill for both the handler
and the guide dog. This type of intuition training in unexpected situations
would be difficult to train in a robot.

Our BLV co-designer helped design and test the prototype study protocol and
surveys that were given to the cited participants. Additionally, our co-
designer participated in an autonomous walk test around a large square
sidewalk loop. (Figures 9). She reported that the handle was comfortable, and
simulated the same walking, turning, and stair movements as those with her
guide dog. The preliminary findings of her user test suggest that industrial
agile robots are able to replicate many of the basic tasks that guide dogs
perform.

TABLE II: Comparison of Guide Dog and Spot Explorer Navigation Abilities Basic Guide Behaviors/Tasks | Guide Dog | Spot Explorer
---|---|---
Lead handler in a straight line | X | X
Avoid obstacles and people | X | X
Maintain a consistent pace | X | X
Smooth and sharp turns | X | X
Walking backward (if needed) | X | X
Cross street at crosswalk | X | possible
Object Recognition Tasks |  |
Crosswalks | X | possible
Overhead/Ground obstacles | X | possible
Changes in elevation | X | X
Object retrieval | X | X
Verbal Commands |  |
Halt/Stop | X | X
Forward/Backward | X | X
Right/Left | X | X
Find (retrieval) | X | X
Sit/Down | X | X
Other Training |  |
Intelligent Disobedience | X | possible
Ignoring Distractions | X | possible

Figure 9: Outdoor Field Test

###  VI-B Navigation Task Results with Handle

All of the participants were able to successfully complete: 1) the practice
tasks with assistance, 2) the experimental handle navigation tasks unassisted
without difficulty allowing the robot to guide them through a predetermined
multilevel route, 3) the voice-based interface command tasks. The survey data
assessed the levels of difficulty that participants reported while performing
the protocol tasks.

###  VI-C Handle Prototype Survey

Questions 1-3 asked participants about their experience using the prototype
handle with the robot moving through the navigation tasks. Overall, the
participants reported that they found the handle easy to hold and comfortable,
and they could feel the robot guiding them through the handle (Figure 10).
There was less consistent agreement about the smoothness and the stability of
the handle based on the large number of neutral responses to Question 2.

There was an additional open-response question asking for feedback about the
functionality of the handle and how it could be improved. Most of the quotes
mentioned the need for a better grip on the handle, which may not be
positioned correctly to go down the stairs smoothly.

> ”For a makeshift handle, it was good. Maybe make it more ergonomic for the
> hand and slightly longer so it’s closer to the body.”
>
> ”I feel like when walking down the stairs, the pull was a bit too abrupt and
> strong.”
>
> ”It is a little awkward to hold and there are like dead spots where you
> don’t get any feedback from it at all so you can’t really tell where it’s
> going.”

Questions 4-7 asked about the participant’s perceptions of safety and trust
while being guided by the robot (Figure 11). Overall, participants reported a
high level of trust in the robot’s ability to guide them and that they felt
safe both just being around the robot and operating the robot during the
navigation tasks. However, there were several participants who reported they
had safety concerns in the open response comments.

> “The robot is just a bit clumsy overall. I don’t think Spot was meant for
> guiding people.”
>
> “Going down the stairs is difficult because when it does the bend to signal
> stairs you can’t tell if it’s going to go up or down, especially since you
> are disoriented because you have to rotate (the handle) around.”

Figure 10: Handle Survey Responses Figure 11: Robot Safety Survey Responses

###  VI-D Voice-based Interface Prototype

The results of the voice interface survey suggest that participants reported
they could effectively pass simple movement commands (forward, backward,
right, left) to the robot through the voice interface app.

Figure 12: Voice Interface Functionality

##  VII Discussion

This study investigated the potential application of a commercially available
agile industrial robot in a human-robot pair to perform a set of simple
assistive navigation tasks that would be typical of a handler-guide dog pair.
The next section will discuss the functioning of the two prototypes in the
navigation tasks and preliminary results of the sighted participant study
addressing the research questions motivating this study.

What are the necessary features for a multisensory interface to facilitate
critical human-robot interactions for blind navigation in a multilevel indoor
environment?

We found that all of the participants in our human-robot pairings could
perform similar simple guided navigation tasks as we observed in human-guide
dog teams. The prototype handle proved to be an effective way for participants
to receive information about the robot’s body motions to allow them to
understand and feel comfortable with being led through the study route and
tasks. Participants indicated this was also true as the robot guided them up
and down the short flight of stairs even though the robot needed to pivot
around in order to go down backward correctly. The participants indicated the
handle rotation allowed them to move around the robot as it pivoted on the
landing. This preliminary study did not utilize the wide range of built-in
features or navigation functionality available in this robot, nor did we
implement the extensive object detection models that would allow for complex
autonomous routes to be tested. Instead, we focused our attention on
developing two early prototypes for the human-robot pair to communicate with
one another through nonvisual channels (handle and the app).

Based on the feedback from our co-designer and the results of prototype
usability studies, we argue there several benefits to the futures development
of industrial quadruped robots for human-robot navigation applications. First,
these robots (after further development) may alleviate the long waiting
periods for BLV handlers while they are waiting for an appropirate guide dog
match and allow the handler to learn new routes with more spatial information
provided by the robot’s internal navigation systems before learning new routes
with their guide dog. This pre-journey training done with a human-robot pair
could improve handler confidence when working with a new guide dog that is not
currently possible during the critical transition periods between guide dog.
Furthermore, the capacity for standardized, learned behaviors and consistency
of mobility tasks can be adjusted and re-calibrated as needed. This type of
industrial robot would be able to receive regular software updates, extending
the potential working lifespan to meet the changing needs of the handler over
the natural aging process. The handle prototype was effective for forward
motion navigation tasks and did provide functional communication about the
robot’s guiding movements for the handler to anticipate and react. The early
prototype for the voice-based interface also provided a way for the handler to
communicate basic commands to the robot. The robot reacted to the commands
with the appropriate actions for most of the trials. The ability of the human-
robot pair to communicate through a voice-based interface has the potential to
provide a greater level of spatial information for the handler than is
currently available from a guide dog. The industrial robot used in this study
has the capacity to receive, store, and communicate spatial information in the
same manner as current accessible navigation applications. After further
development, the voice-based app could communicate critical landmarks,
relevant obstacles, and available route affordances to the handler along
unknown routes.

As with any study, our results also identified some challenges to implementing
the necessary behaviors in human-robot guide interactions. Any size
quadrupedal robot, small or large, is battery-operated, and the batteries need
to be charged and replaced. Since we are using a larger and more sophisticated
robot in this study, the battery life was a significant limitation and
required planning to not run out of power during the user studies. The Spot
Explorer model is currently limited to about 90 minutes of operating time
before needing a battery replacement. This would make long trips or unexpected
delays difficult for the handler in the real world. The batteries are about 15
lbs, making them too large to carry on longer walks. Furthermore, if this
robot breaks or malfunctions, the repair would require a specialized engineer.
While this might be a similar situation to guide dogs needing to visit the
veterinarian when they are sick, the logistics of arranging for a ’house call’
from a robotics specialist is currently impractical and would result in a long
waiting time for a handler to get back to a daily routine. Finally, the Spot
Explorer model has built-in safety features such as obstacle avoidance that
currently interfere with effective human-robot navigation in environments
where there may be crowds or other people in close proximity to the handler-
robot pair. The Spot Explorer model is programmed to avoid objects if they get
within approximately 8 inches to it’s many cameras. While it can be customized
to reduce that distance to 10cm, guide dogs are trained to ’part’ crowds of
people for their handler by pushing through and against people if given the
command to do so. The current safety features interfere with the robot’s
ability to adjust to sudden changes in the environment, and it will stop to
avoid injuries to others instead of doing the task that it is given by its
handler.

What human-dog pair navigation behaviors and tasks can be reproduced with an
industrial quadruped robot?

For blind navigation in a multilevel indoor environment, critical human-robot
communication interactions are vital in ensuring safe and effective movement
for individuals with vision loss. The minimally necessary interactions between
a human-robot pair were observed to be understanding of each other’s movement
intentions, the ability to avoid danger and obstacles, and making decisive
decisions for the user. The Spot Explorer model is able to perform such tasks
to varying degrees. With respect to communication between the user and Spot,
there is good one-way communication where Spot is able to guide the user
effectively through the handle. The participants of the user studies were able
to understand Spot’s actions. However, beyond the movements from the handle,
there are minimal forms of communication from Spot to the handler.
Furthermore, Spot cannot take commands from the handler, and all movements
must be controlled manually or have instructions given beforehand.

Guide dogs must make active decisions for the handler in the event that the
handler’s actions may result in harming them; this is known as intelligent
disobedience. For example, if the handler wants to cross a street, but the
guide dog senses that there is a car coming, it will decide not to cross the
street. The Spot Explorer model (or any other robot used in this type of
research) does not currently have the ability to detect and communicate
unknown dangers or react beyond its built-in programming in such a way.
Therefore, the research and development of an agile industrial robot for
navigation assistance still has a long way to go before it is ready for
deployment in a real-world setting. However, based on these early explorations
of commercially available technology, the results of this study suggest this
is a viable research pathway that could present valuable co-design research
opportunities for future versions of these robots. Perhaps with the
cooperation and collaboration of industry partners, this may be a potential
alternative to human-dog teams when situations or environments do not permit
the use of trained guide dogs for safety or health reasons.

Although the Spot Explorer shows potential to be introduced as a robotic guide
dog, future studies must address some limitations to expand the range of
possible navigation behaviors. For example, there is only a one-way voice
communication channel between the Spot Explorer and the handler. As found in
the survey, the user is able to understand Spot’s actions through the handle,
however, the robot does not always understand the handler’s intentions or is
able to take real-time directions beyond the few commands we tested in this
pilot study. In a typical handler-guide dog partnership, there is constant
communication where the guide dog processes directions from the handler such
as finding railings or turning in a certain direction. In response to the
handler, the guide dog will follow directions, or if they notice obstacles,
will lead the user around them. While the Spot robot can communicate with the
user via the handle, it cannot provide more detailed spatial information for
the handler to know why it might be avoiding obstacles.

Due to the various cameras needed for navigation and sensing of the
environment, there are limited positions where a handler can stand before they
interfere with the cameras. Although we worked with our co-designer to adjust
the handle to provide a comfortable position that was within the typical range
for handler-dog pairs, if a handler were to accidentally block a camera on the
side of Spot, it could begin to move erratically to avoid the handler. There
were minimal problems while the handler performed simple tasks such as moving
in a straight line; complex tasks such as pivoting on the stairs or turning
often caused the user to block the cameras. In our user studies, the sighted
participants were able to correct their position as they moved along, and our
co-designer could easily adjust her position but this could prove to be a
difficult or unsafe issue for other BLV handlers.

##  VIII Limitations

While this preliminary study provided valuable information, it had several
limitations. First, we only had 20 sighted participants recruited for the
study. We focused on sighted participants for the interface prototype testing
and for safety testing because we were not confident the robot would reliably
be able to lead or swivel around easily going up or downstairs stairs with
someone holding the handle. The small number of participants makes it
difficult to generalize conclusions. Furthermore, there may be some inherent
bias for the questions regarding trust in robot safety as the participants in
the study were volunteers, and some had seen the robot previously being used
in research on campus. Finally, the majority of our participants were sighted
users who were not familiar with standard guide dog-handler communication
techniques. Therefore, they were unable to provide a full range of feedback on
the effectiveness of the communication through the handle as a part of the
human-robot pair in the same way as our BLV co-designer. In addition, as they
were sighted, they could maneuver themselves around obstacles or step away
from the robot if the robot was trying to avoid obstacles, thus increasing
their sense of control and safety in a set of simple navigation tasks. This
problem is most prominent when Spot transitions from going up to down the
stairs. Spot’s inability to go down the stairs forward requires Spot to pivot
and for the user to switch hands or pivot along with Spot. There were several
instances during the user studies where the user would block Spot’s camera
once while transitioning and Spot would try to move away from the user. As the
user is expected to move with Spot, Spot is unable to move away from the user
to reset its position. There are also several improvements that could be made
to adjust Spot’s pacing and acceleration when going from stop to start. The
robot’s pace would need to change depending on the scenario. Spot’s
acceleration from stopping to start was often sudden. This is due to the power
of the motors and the acceleration required to start and stop this large and
powerful robot. This is something that can be adjusted in future studies.

##  IX Conclusions and Future Work

This research focuses on exploring an industrial agile quadruped robot as a
potential alternative navigation assistant for BLV individuals. We
investigated the larger, industrial Boston Dynamics Spot Explorer model as a
viable option as a navigation assistant and how it might differ from similar
research using smaller agile robots based on its ability to approximate
typical guide dog behaviors and navigation tasks using two nonvisual interface
prototypes for communication between the handler-robot pair. First, we worked
with our co-designer to identify an industrial quadruped robot’s potential
benefits and limitations, and human-robot pair behaviors necessary for simple
blind navigation tasks. We found that the Spot Explorer model could reproduce
simple navigation tasks similar to an experienced guide dog, and the interface
prototypes provided a basic level of communication and interaction between the
handler-robot pair. However, we also identified several significant drawbacks
based on the commercially available robot’s current design and existing
programming. The results of the prototype user studies suggest that there is
evidence of adequate human-robot interactions and that users were able to
understand the robot’s actions through the handle. However, there is potential
for significant improvement in the handle design and the addition of features
such as haptics and switches to provide an immediate stop feature. Future
design work and modifications will include changing the shape of the upper
part of the handle, modifying the attachment structure of the handle to the
robot, integrating object detection and retrieval, and an improved two-way
voice-based interface to provide increased spatial information available to
the handler through the robot’s cameras and sensors.

## Acknowledgement

We would like to thank our labmates at the INSITE Lab at Colby College, the
study participants for their time and feedback, and Toto, our expert guide
dog. We would also like to thank Ed Colp at Boston Dynamics for supporting
this project, without whom this research would not have been possible.

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# Small-scale cosmic ray anisotropy observed by the GRAPES-3 experiment at TeV
energies

M. Chakraborty  Tata Institute of Fundamental Research, Homi Bhabha Road,
Mumbai 400005, India  S. Ahmad  Aligarh Muslim University, Aligarh 202002,
India  A. Chandra  Aligarh Muslim University, Aligarh 202002, India  S.R.
Dugad  Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai
400005, India  U.D. Goswami  Dibrugarh University, Dibrugarh 786004, India
S.K. Gupta  Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai
400005, India  B. Hariharan  Tata Institute of Fundamental Research, Homi
Bhabha Road, Mumbai 400005, India  Y. Hayashi  Graduate School of Science,
Osaka Metropolitan University, Sugimoto, Sumiyoshi, Osaka 558-8585, Japan  P.
Jagadeesan  Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai
400005, India  A. Jain  Tata Institute of Fundamental Research, Homi Bhabha
Road, Mumbai 400005, India  P. Jain  Indian Institute of Technology Kanpur,
Kanpur 208016, India  S. Kawakami  Graduate School of Science, Osaka
Metropolitan University, Sugimoto, Sumiyoshi, Osaka 558-8585, Japan  T. Koi
College of Engineering, Chubu University, Kasugai, Aichi 487-8501, Japan  H.
Kojima  Chubu Astronomical Observatory, Chubu University, Kasugai, Aichi
487-8501, Japan  S. Mahapatra  Utkal University, Bhubaneshwar 751004, India
P.K. Mohanty  Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai
400005, India  R. Moharana  Indian Institute of Technology Jodhpur, Jodhpur
342037, India  Y. Muraki  Institute for Space-Earth Environmental Research,
Nagoya University, Nagoya 464-8601, Japan  P.K. Nayak  Tata Institute of
Fundamental Research, Homi Bhabha Road, Mumbai 400005, India  T. Nonaka
Institute for Cosmic Ray Research, Tokyo University, Kashiwa, Chiba 277-8582,
Japan  T. Nakamura  Faculty of Science and Technology, Kochi University, Kochi
780-8520, Japan  A. Oshima  College of Engineering, Chubu University, Kasugai,
Aichi 487-8501, Japan  B.P. Pant  Indian Institute of Technology Jodhpur,
Jodhpur 342037, India  D. Pattanaik  Tata Institute of Fundamental Research,
Homi Bhabha Road, Mumbai 400005, India  Utkal University, Bhubaneswar 751004,
India  S. Paul  Tata Institute of Fundamental Research, Homi Bhabha Road,
Mumbai 400005, India  G.S. Pradhan  Indian Institute of Technology Indore,
Indore 453552, India  M. Rameez  Tata Institute of Fundamental Research, Homi
Bhabha Road, Mumbai 400005, India  K. Ramesh  Tata Institute of Fundamental
Research, Homi Bhabha Road, Mumbai 400005, India  S. Saha  Indian Institute of
Technology Kanpur, Kanpur 208016, India  R. Sahoo  Indian Institute of
Technology Indore, Indore 453552, India  R. Scaria  Indian Institute of
Technology Indore, Indore 453552, India  S. Shibata  College of Engineering,
Chubu University, Kasugai, Aichi 487-8501, Japan  T. Tabata  College of
Engineering, Chubu University, Kasugai, Aichi 487-8501, Japan  H. Takamaru
College of Engineering, Chubu University, Kasugai, Aichi 487-8501, Japan  K.
Tanaka  Graduate School of Information Sciences, Hiroshima City University,
Hiroshima 731-3194, Japan  F. Varsi  Indian Institute of Technology Kanpur,
Kanpur 208016, India  K. Yamazaki  College of Engineering, Chubu University,
Kasugai, Aichi 487-8501, Japan  M. Zuberi  Tata Institute of Fundamental
Research, Homi Bhabha Road, Mumbai 400005, India P.K. Mohanty<EMAIL_ADDRESS>
###### Abstract

GRAPES-3 is a mid-altitude (2200 m) and near equatorial ($11.4^{\circ}$ North)
air shower array, overlapping in its field of view for cosmic ray observations
with experiments that are located in Northern and Southern hemispheres. We
analyze a sample of $3.7\times 10^{9}$ cosmic ray events collected by the
GRAPES-3 experiment between 1 January 2013 and 31 December 2016 with a median
energy of $\sim 16$ TeV for study of small-scale ($<60^{\circ}$) angular scale
anisotropies. We observed two structures labeled as A and B, deviate from the
expected isotropic distribution of cosmic rays in a statistically significant
manner. Structure ‘A’ spans $50^{\circ}$ to $80^{\circ}$ in the right
ascension and $-15^{\circ}$ to $30^{\circ}$ in the declination coordinate. The
relative excess observed in the structure A is at the level of $(6.5\pm
1.3)\times 10^{-4}$ with a statistical significance of 6.8 standard
deviations. Structure ‘B’ is observed in the right ascension range of
$110^{\circ}$ to $140^{\circ}$. The relative excess observed in this region is
at the level of $(4.9\pm 1.4)\times 10^{-4}$ with a statistical significance
of 4.7 standard deviations. These structures are consistent with those
reported by Milagro, ARGO-YBJ, and HAWC. These observations could provide a
better understanding of the cosmic ray sources, propagation and the magnetic
structures in our Galaxy.

cosmic rays, air showers, anisotropy

##  1 Introduction

Cosmic rays (CRs) are charged particles because of which they undergo
deflections by the randomized magnetic field in the interstellar medium
through which they propagate. This leads to an isotropic distribution of CRs
as observed on the Earth. However, in the past decade, several experiments
located at different latitudes have observed the CRs anisotropy on different
angular scales and energy ranges with an amplitude of $\sim\\!10^{-4}$ to
$10^{-3}$. Large-scale structures with a dominant dipolar component have been
observed in TeV–PeV energy range by several ground based extensive air shower
(EAS) experiments located in the Northern hemisphere such as Tibet-AS$\gamma$
(M. Amenomori et al., 2006, 2017), ARGO-YBJ (B. Bartoli et al., 2018), Milagro
(A. A. Abdo et al., 2009), HAWC (A. U. Abeysekara et al., 2019), EAS-TOP (M.
Aglietta et al., 2009), KASCADE-Grande (M. Ahlers, 2019) and in Southern
hemisphere such as IceCube (M. G. Aartsen et al., 2016). An excess has been
observed within the right ascension ($\alpha$) of $30^{\circ}$ to
$120^{\circ}$ and a large deficit region has been observed within
$150^{\circ}\leq\alpha\leq 250^{\circ}$. Anisotropies at ultra high energies
($\geq\\!10^{18}$ eV) where the interstellar magnetic field plays a relatively
insignificant role for bending CRs, has been reported by the Pierre Auger
Observatory (A. Aab et al., 2018) and the Telescope Array (R. U. Abbasi et
al., 2020). The large-scale anisotropy with a strength of $\sim\\!10^{-3}$ has
been qualitatively explained by standard diffusion models describing
propagation of CRs through the Galaxy following acceleration by supernova
remnants (P. Blasi and E. Amato, 2012; G. Giacinti and G. Sigl, 2012; P.
Mertsch and S. Funk, 2015).

Small-scale anisotropy with angular width less than $60^{\circ}$ was first
reported by the Milagro experiment showing excesses in two regions, namely
region A and B with an excess at the level of $6.0\times 10^{-4}$ and
$4.0\times 10^{-4}$ respectively (A. A. Abdo et al., 2008). Located at
$36^{\circ}$N latitude, the Milagro could observe the upper half of region A
centered at $\alpha\approx 69.4^{\circ}$, $\delta\approx 13.8^{\circ}$,
ranging in $66^{\circ}<\alpha<76^{\circ}$ and $10^{\circ}<\delta<20^{\circ}$.
The region B was reported to be a structure lying within
$15^{\circ}<\delta<50^{\circ}$ with right ascension within
$117^{\circ}<\alpha<141^{\circ}$. The ARGO-YBJ also observed the same regions
along with two additional regions namely ‘3’ and ‘4’ (B. Bartoli et al.,
2013). The region 3 stretches in the Northern hemisphere within
$234^{\circ}\leq\alpha\leq 282^{\circ}$. The region ‘4’ is observed with
$200^{\circ}\leq\alpha\leq 216^{\circ}$ and $24^{\circ}\leq\delta\leq
34^{\circ}$ and is similar to the the region ‘C’ observed by HAWC (A. U.
Abeysekara et al., 2014). IceCube has also observed small-scale anisotropic
structures in the Southern hemisphere by subtracting dipole and quadrapolar
terms from the large-scale maps, and the region B appears to have a continuity
in the Southern hemisphere (M. G. Aartsen et al., 2016). A full sky analysis
of HAWC and IceCube combined also shows the two primary anisotropy structures
in region ‘A’ and ‘B’ in which the region ‘B’ can be seen to extend throughout
the entire declination range (A. U. Abeysekara et al., 2019). GRAPES-3 covers
a declination range from $-23.8^{\circ}\leq\delta\leq 46.6^{\circ}$ with a 56%
sky coverage and an overlapping field of view with the above discussed
experiments. This paper reports the small-scale anisotropy structures and
their features observed by the GRAPES-3 experiment.

##  2 The GRAPES-3 experiment and data set

The GRAPES-3 is a near equatorial and mid-altitude EAS experiment, located in
Ooty, India (11.4∘N, 76.7∘E and 2200 m a.s.l.), designed for cosmic ray and
gamma ray observations at TeV-PeV energies. It consists of an array of 400
plastic scintillator detectors spread over 25,000 m2 area (S. K. Gupta et al.,
2005; P. K. Mohanty et al., 2009) and a tracking muon detector of 560 m2 area
(Y. Hayashi et al., 2005) as shown schematically in Figure 1. The scintillator
detectors are of 1 m2 area each, placed in a hexagonal configuration with 8 m
inter-detector separation. The density of secondary particles and their
relative arrival times in each EAS are recorded by the scintillator detectors.
The EAS parameters such as core location, size and age were obtained by
fitting Nishimura-Kamata-Greisen (NKG) function to the lateral distribution of
particle densities (K. Kamata and J. Nishimura, 1958; K. Greisen, 1960). The
direction of individual EAS in terms of zenith and azimuth angle ($\theta$,
$\phi$) were obtained by a fitting a plane front to the relative arrival time
data recorded on each scintillator detector after correcting the curvature in
the shower front which is observed to be dependent on both size and age
parameter (V.B. Jhansi et al., 2020; D. Pattanaik et al., 2022). About 20$\%$
of the EAS events mainly of low energies could not be reconstructed properly
by the NKG function. Hence, the curvature corrections could not be performed
for these events as the size and age parameters are not reliable. However, the
$\theta$ and $\phi$ for these events were obtained only using a plane fit.
This is not expected to impact the study of anisotropy since the angular scale
of anisotropies relevant to this analysis are much larger than the improvement
in resolution due to curvature corrections. For this analysis, we used 4 years
of EAS data recorded from 1 January 2013 to 31 December 2016 which comprised
$3.9\times 10^{9}$ EAS events with a recorded live time of 1273.1 days. The
EASs with zenith angle less than $60^{\circ}$ were selected. A total of
$3.7\times 10^{9}$ EASs which passed the selection criteria were used for the
analysis. The median energy of the EAS was calculated to be 16.2 TeV based on
energy-size relation using CORSIKA simulation as described in (D. Pattanaik et
al., 2022).

Figure 1: Schematic diagram of the GRAPES-3 experiment with the scintillator
detectors represented by ($\blacksquare$) and the muon detector modules
represented by ($\square$).

##  3 Analysis and results

In this analysis, the reconstructed local direction ($\theta$, $\phi$) of each
EAS event was converted into equatorial coordinate in terms of $\alpha$ and
$\delta$. The events were binned into a map in the celestial sphere using
HEALPix package (K. M. Gorski et al., 2005). We set the parameter nside=64
which divides the sky into 45192 pixels of equal area of about
$(0.92^{\circ})^{2}$ in $\alpha$ and $\delta$. The EAS trigger rate exhibits
variations caused by atmospheric pressure and temperature on a daily time
scale at the level of 3-5$\%$ (M. Zuberi et al., 2017). Further, there are
gaps in the data produced either due to scheduled maintenance activity or
failure of electronics or DAQ system. Fluctuation in the rates due to
abnormality in the detectors or electronics is another effect. It is difficult
to identify and remove all these effects completely from the data. They can
cause non-uniform exposure of events in the Celestial sphere, thereby
producing spurious anisotropies which could be more than a order of magnitude
larger than the genuine anisotropy of astrophysical origin that needs to be
investigated. Therefore, the search for anisotropies in this map cannot be
performed directly. Our search strategy is based on the estimation of a
reference map which retains the anisotropies originated from instrumental or
atmospheric effects while being insensitive to the astrophysical anisotropy.
The reference map was estimated from the data itself using a method called
time-scrambling as described in (D. E. Alexandreas et al., 1993). This method
has been employed by several air shower experiments for successful extraction
of anisotropy (B. Bartoli et al., 2013; M. G. Aartsen et al., 2016). In this
method, the true recorded time of an EAS event is forgotten while assigning
another time to it which is randomly picked up from a sample of recorded
events within a time window of $\Delta t$. The ($\theta$, $\phi$) for the
event is kept same. Thus it changes the $\alpha$ of the event while keeping
$\delta$ unchanged. Thus, the anisotropic structures of astrophysical origin
smaller than an angular scale of $\Delta t\times 15^{\circ}/1\,$hr are
removed. Each recorded event is assigned with 20 random time values from the
recorded time sample within the scrambling time window, and then it is filled
into the reference map ($N_{i}^{ref^{\prime}}$). This essentially means that
the reference map has 20 times more number of events than the data map. This
is done to reduce the statistical fluctuation in the reference map. An average
of all these 20 maps is then calculated to estimate the reference map as,
$N^{ref}_{i}=N_{i}^{ref^{\prime}}/20$. To re-state it, both the reference map
and data map will contain the anisotropies produced by atmospheric and
instrumental effects whereas the reference map will not have the anisotropies
of astrophysical origin. The relative intensity for each pixel is calculated
using, $\delta I=\frac{N^{data}_{i}-N^{ref}_{i}}{N^{ref}_{i}}$ where
$N^{data}_{i}$ and $N^{ref}_{i}$ are the number of events in the i-th pixel of
data and reference map respectively.

(a)

(b)

Figure 2: Anisotropy (top) and significance (bottom) obtained after time-
scrambling with $\Delta t=24\,$ hrs and smoothing radius of $10^{\circ}$. The
large-scale deficit can be seen while small scale structures A and B are
prominently seen.

(a)

(b)

Figure 3: Anisotropy (top) and significance (bottom) observed with a
scrambling window of 4 hrs and a smoothing radius of $10^{\circ}$

First we apply the time-scrambling method on data using a scrambling window of
$\Delta t=24\,$hrs. The stability in zenith and azimuth over long periods of
time allows such a choice. Since in 24 hrs, there will be full coverage of the
celestial sphere, anisotropies up to the largest angular scale present in the
data should show up as they will be destroyed in the reference map. The
relative intensity map does not show any discernible anisotropy due to high
level of statistical fluctuation in the pixels. To improve the capability of
identifying directional features, we employ a smoothing method similar to the
method followed by (A. U. Abeysekara et al., 2014). This approach includes
combining the event counts within individual pixels and adding counts from
neighboring pixels positioned within a chosen smoothing radius of $10^{\circ}$
in this particular study. This approach serves to decrease statistical
fluctuations in nearby pixels, thereby revealing any localized surpluses.
However, it also establishes correlations in event statistics among
neighboring pixels. Essentially, this is analogous to applying a smoothing
operation to the map using a top-hat function of $10^{\circ}$ radius. The
structures that we are trying to observe span over few tens of degrees, so a
choice of a $10^{\circ}$ smoothing radius has ensured that these structures
are not integrated over. The relative intensity map after smoothing is shown
in Figure 2. The pixel-wise significance was calculated using the Li-Ma
prescription (T. P. Li and Y. Q. Ma, 1983) and the significance map is shown
on the bottom of the same figure. The map shows a large-scale deficit region
in the right ascension range of 135∘ to 300∘. The location is consistent with
the observation by Tibet-AS$\gamma$ (M. Amenomori et al., 2017), HAWC (A. U.
Abeysekara et al., 2018) and IceCube (M. G. Aartsen et al., 2016). Two excess
regions with angular scales $\leq$60∘ marked as A and B are more prominent
than the large-scale deficit region. The observed strength of the large-scale
deficit region is significantly less than expected. This is anticipated due to
the attenuation of the reconstructed anisotropy by mid- or low-latitude
detectors whose instantaneous field of view is much smaller than the size of
the large-scale anisotropy. There are methods developed to retrieve them (M.
Ahlers et al., 2016). However, the current work has focused on the study of
small scale anisotropies. Since the structures are of angular size of
$\leq$60∘ as can be seen from Figure 2, we have chosen a time-scrambling
window of 4 hrs that can destroy anisotropy up to angular scale of 60∘ in the
reference map while retaining the large scale anisotropies. In other words,
the relative intensity map will be devoid of the large-scale anisotropies
while being sensitive to the small-scale anisotropy structures as shown in
Figure 3. The two structures A and B are largely consistent with the
observation of other experiments such as Milagro (A. A. Abdo et al., 2008),
HAWC (A. U. Abeysekara et al., 2014) and ARGO-YBJ (B. Bartoli et al., 2013).

Figure 4: 1-d projection of anisotropy showing the variation in number of
events with right ascension

The variation of events as a function of right ascension, obtained from the
unsmoothed maps by summing events over declination bands spanning within
$-18.6^{\circ}$ to $41.4^{\circ}$, is shown in Figure 4. The range is taken
within $\pm$ 30∘ from the latitude of the GRAPES-3 experiment. The plot
clearly illustrates that the variation in the event count, resulting from non-
uniform sky exposure due to detector and atmospheric effects, is mirrored in
the background estimation obtained through the time-scrambling algorithm. The
systematic excesses for regions A and B are also seen in the relative
intensity plot. A zoomed view of the two regions are shown in Figure 5 and
Figure 6. The details are discussed below.

Region A is observed in the right ascension range of $\sim$50∘ to $80^{\circ}$
and declination range of $\sim$-15∘ to 30∘. The maximum relative intensity of
the anisotropy in this region is $(8.9\pm 2.1\pm 0.2)\times 10^{-4}$ at the
pixel centered at ($\alpha=63.9^{\circ}$, $\delta=-7.2^{\circ}$), where the
first error is statistical and the second error is systematic. The calculation
of the systematic error involves conducting an analysis using “anti-sidereal”
time (K. Nagashima et al., 1998; A. A. Abdo et al., 2008). While sidereal time
corresponds to the sky fixed frame, anti-sidereal time represents a non-
physical frame. Analyzing data based on sidereal time allows us to determine
the presence of anisotropy, while analyzing data based on anti-sidereal time
provides insight into the systematic error on sidereal anisotropy resulting
from non-physical effects (R. Abbasi et al., 2010; F. J. M. Farley and J. R.
Storey, 1954). The same time-scrambling analysis with a time window of 4 hrs
was performed but with anti-sidereal time in order to estimate the systematics
and the maximum strength observed in regions A and B are quoted as systematic
errors. The results of anti-sidereal time have been shown in Figure 7 and no
characteristic signal regions can be seen in this case implying that the
systematics caused by non-physical effects are insignificant. The statistical
error dominates as the systematic error is much lesser than statistical error.

Region A appears to be a circular structure with a tail like projection. The
maximum pixel significance observed in this region is 5.8$\sigma$. To
calculate the total significance of this region, the unsmoothed data and
reference maps were used in order to avoid the correlations introduced by
smoothing between the pixels. First, the structure was defined by selecting
those pixels which have a significance of more than 2$\sigma$ as shown in
Figure 5 in bottom. The total number of events in data and reference maps from
the region was obtained by summing up the pixel wise events in the selected
area. The Li-Ma prescription was used to obtain the total significance which
is 6.8$\sigma$. The relative excess number of events in this region is
$(6.5\pm 1.3)\times 10^{-4}$.

Region B is an elongated structure observed within $\sim$110∘ to 140∘ of right
ascension and almost throughout the full declination range (see Figure 6). The
maximum relative intensity observed for this region is $(5.6\pm 1.8\pm
0.1)\times 10^{-4}$ at the pixel centered at
($\alpha=124.5^{\circ},\delta=3.4^{\circ}$) and the significance of the pixel
is 4.4$\sigma$. Similar to the criteria applied for region A, those pixels
which have a significance of more than 2$\sigma$ were selected to define
region B. The overall relative intensity and significance of the region B is
$(4.9\pm 1.4)\times 10^{-4}$ and 4.7$\sigma$, respectively.

The deficit seen around regions A and B are also consistent with the
observations by Milagro, ARGO-YBJ and HAWC. The deficit observed between
regions A and B is the most significant. The deficit structure has a
significance of $3.7\sigma$ and a relative intensity of $-(4.6\pm 1.7)\times
10^{-4}$.

(a)

(b)

Figure 5: Anisotropy (top) and significance (bottom) of Region A as observed
with a scrambling window of 4 hrs. The pixels with higher than 2$\sigma$
significance have been shown in the bottom plot and the region defined has
been used to calculate the significance of the entire structure.

(a)

(b)

Figure 6: Anisotropy (top) and significance (bottom) of Region B as observed
with a scrambling window of 4 hrs, and plotted similar to region A

##  4 Discussion

By analyzing 3.7$\times$109 EAS events collected over a period of 4 years,
GRAPES-3 could significantly observe two excess regions namely A and B. The
region A shows a tail like structure in the Northern hemisphere
($\delta>0^{\circ}$). The shape of the structure is similar to the “region A”
observed by Milagro (A. A. Abdo et al., 2008) and HAWC (A. U. Abeysekara et
al., 2014), and “region 1” reported by ARGO-YBJ (B. Bartoli et al., 2013).
Milagro (at $36^{\circ}$N) observes the part of this structure in the Northern
sky and the observations are continued to the Southern hemisphere by ARGO-YBJ
(at $30^{\circ}$N), HAWC (at $19^{\circ}$N) and GRAPES-3 (at $11.4^{\circ}$N).
GRAPES-3 and HAWC lying closer to the Equator have the advantage of covering
the southern part of region A. Region B is also observed by Milagro, ARGO-YBJ
(referred to as “region 2”) and HAWC as a continuous structure running almost
throughout the entire declination band, similar to observations by GRAPES-3.
The full sky analysis by HAWC and IceCube show that region B continues to the
Southern hemisphere as well (A. U. Abeysekara et al., 2019) running through
the entire declination band. The deficit regions seen around these excesses by
GRAPES-3 are also coincident with observations by Milagro, ARGO-YBJ and HAWC.

(a)

(b)

Figure 7: Anisotropy (top) and significance (bottom) obtained after performing
the same analysis with anti-sidereal time. No characteristic signal region can
be observed.

The highest observed peak relative intensities for region A are $(8.5\pm
0.6\pm 0.8)\times 10^{-4}$ as measured by HAWC (A. U. Abeysekara et al.,
2014), $10.0\times 10^{-4}$ by ARGO-YBJ (B. Bartoli et al., 2013) and $(8.9\pm
2.1\pm 0.3)\times 10^{-4}$ by GRAPES-3. Region A’s peak intensity is situated
in the Southern hemisphere at $\delta=-7.2^{\circ}$ and $-6.3^{\circ}$ for
GRAPES-3 and HAWC respectively. Region B exhibits a peak relative intensity of
$(5.2\pm 0.6\pm 0.7)\times 10^{-4}$ for HAWC, $5.0\times 10^{-4}$ for ARGO-YBJ
and $(5.6\pm 1.8\pm 0.1)\times 10^{-4}$ for GRAPES-3. The peak intensities
have been tabulated in Table 1.

| Region A ($\times 10^{-4}$) | Region B ($\times 10^{-4}$)
---|---|---
ARGO-YBJ | 10.0 | 5.0
HAWC | $(8.5\pm 0.6\pm 0.8)$ | $(5.2\pm 0.6\pm 0.7)$
GRAPES-3 | $(8.9\pm 2.1\pm 0.3)$ | $(5.6\pm 1.8\pm 0.1)$
Table 1: The peak intensities of regions A and B as reported by ARGO-YBJ, HAWC
and GRAPES-3

ARGO-YBJ (B. Bartoli et al., 2013) and HAWC (A. U. Abeysekara et al., 2014)
have also performed an analysis based on energy dependence by partitioning
their datasets into multiple segments, some of which overlap with the median
energy range of GRAPES-3 at about 16 TeV. The relative intensity of region A
observed by GRAPES-3 is $(6.5\pm 1.3)\times 10^{-4}$. ARGO-YBJ divided their
dataset into five segments, with the last two segments having median energies
of 7.3 TeV and 20 TeV, respectively. In this case, the relative intensity of
region A exhibited a flattening around $7.0\times 10^{-4}$ in the last two
energy bins which encompass GRAPES-3’s median energy. When considering HAWC’s
analysis, their dataset was divided into seven segments, and the relative
intensity of region A was assessed for each segment around the peak. It was
reported by HAWC that the relative intensity of region A increases with
energy. When comparing our findings, we use the segment of data having a
median energy of $14.7^{+28.7}_{-9.9}$ TeV, which is the closest approximation
to GRAPES-3’s median energy and a relative intensity of $\sim\\!(22.0\pm
5.0)\times 10^{-4}$ was reported. For region B, the relative intensity of the
observed structure is $(4.9\pm 1.4)\times 10^{-4}$ by GRAPES-3. The average
relative intensity of region B measured by ARGO-YBJ is $3.5\times 10^{-4}$. It
varies in the range of ($3.0-5.0)\times 10^{-4}$ for the last two energy bins
that cover GRAPES-3’s median energy.

There are different explanations for the origin of the small-scale anisotropic
structures. Several models propose that the origin of the hotspots might be
linked to the proximity of supernova explosions, events known for generating
cosmic rays (A.D. Erlykin and A.W. Wolfendale (2006)). According to (M.
Salvati and B. Sacco (2008)), regions A and B could result from a phenomenon
associated with a supernova explosion that gave rise to the Geminga pulsar.
Situated between the observed structures of regions A and B is the Geminga
pulsar ($\alpha,\delta$=$98.47^{\circ},17.77^{\circ}$). Some models suggest
that these structures are a consequence of the turbulent magnetic field within
the cosmic ray scattering length (G. Giacinti and G. Sigl, 2012; M. Ahlers and
P. Mertsch, 2015) or CR scattering by Alfven waves created by turbulent
cascades in local field direction (Malkov et al., 2010). Another set of models
suggest that they are generated due to magnetic reconnections in the
heilosphere (P. Desiati and A. Lazarian, 2012). The model presented in L.
Drury and F. Aharonian (2008) investigates the hypothesis that region A could
potentially be explained by the production of secondary neutrons in the
concentrated tail of interstellar material that forms downstream of the Sun’s
movement through the local interstellar medium (ISM). Some exotic models also
suggest that decay of quark matter in pulsars or the acceleration of
strangelets near molecular clouds are causes for the anisotropy (K. Kotera et
al., 2013; M. Ángeles Pérez-Garcí et al., 2014). Thus, the examination of CR
anisotropy assumes importance in comprehending the magnetic field structure,
propagation, acceleration mechanisms of CRs, and their sources.

##  5 Summary

We have investigated small-scale anisotropy in CR distribution by analyzing
$3.7\times 10^{9}$ events at median energy of $\sim 16$ TeV, collected by the
GRAPES-3 experiment from 1 January 2013 to 31 December 2016 with a live time
of 1273.1 days. Time scrambling method was employed to estimate the background
map. Two small-scale structures namely regions A and B have shown excesses in
the CR flux at the level of ($6.5\pm 1.3)\times 10^{-4}$ and ($4.9\pm
1.4)\times 10^{-4}$, respectively with statistical significance of $6.8\sigma$
and $4.7\sigma$, respectively. These structures are in agreement with previous
results reported by Milagro, ARGO-YBJ, and HAWC.

We thank D.B. Arjunan, V. Jeyakumar, S. Kingston, K. Manjunath, S.
Murugapandian, S. Pandurangan, B. Rajesh, V. Santhoshkumar, M.S. Shareef, C.
Shobana, R. Sureshkumar for their role in efficient running of the experiment.
We would also like to thank Dr. Suvodip Mukherjee from the Department of
Astronomy and Astrophysics in TIFR for his help related to parts of this work.
We would also like to thank Dr. Frank McNully from Mercer University for
discussing concepts related to cosmic ray anisotropy. We acknowledge the
support of the Department of Atomic Energy, Government of India, under Project
Identification No. RTI4002. This work was partially supported by grants from
Chubu University, Japan.

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