TY - GEN
T1 - How far should i watch? Quantifying the effect of various observational capabilities on long-range situational awareness in multi-robot teams
AU - Kang, Sehyeok
AU - Choi, Taeyeong
AU - Pavlic, Theodore P.
N1 - Funding Information:
Supported in part by NSF grants PHY-1505048 and SES-1735579. 1Data and models used are available at https://github.com/PavlicLab/ ACSOS2020 ReTLo Extension.git
PY - 2020/8
Y1 - 2020/8
N2 - In our previous work, we showed that individual robots within a multi-robot team can gain long-distance situational awareness from passive observations of a single nearby neighbor without any explicit robot-to-robot communication. However, that prior work was developed only in simulation, and performance was not measured for real robot teams in physical space with realistic hardware limitations. Toward this end, we studied the performance of these methods in real robot scenarios with methods using more sophisticated techniques in machine learning to mitigate practical implementation problems. In this study, we further extend that work by characterizing the effects of changing history length and sensor range. Rather than finding that increasing history length and sensor range always yield better estimation performance, we find that the optimal history length and sensor range varies depending on the distance between the estimating robot and the robot being estimated. For estimation problems where the estimation target is nearby, longer histories actually degrade performance, and so sensor ranges could be increased instead. Conversely, for farther targets, history length is as valuable or more valuable than sensor range. Thus, just as optimal shutter speed varies with light availability and speed of the subject, passive situational awareness in multi-robot teams is best achieved with different strategies depending on proximity to locations of interest. All studies use the teams of Thymio II physical, two-wheeled robots in laboratory environments 1.1Data and models used are available at https://github.com/PavlicLab/ACSOS2020_ReTLo_Extension.git
AB - In our previous work, we showed that individual robots within a multi-robot team can gain long-distance situational awareness from passive observations of a single nearby neighbor without any explicit robot-to-robot communication. However, that prior work was developed only in simulation, and performance was not measured for real robot teams in physical space with realistic hardware limitations. Toward this end, we studied the performance of these methods in real robot scenarios with methods using more sophisticated techniques in machine learning to mitigate practical implementation problems. In this study, we further extend that work by characterizing the effects of changing history length and sensor range. Rather than finding that increasing history length and sensor range always yield better estimation performance, we find that the optimal history length and sensor range varies depending on the distance between the estimating robot and the robot being estimated. For estimation problems where the estimation target is nearby, longer histories actually degrade performance, and so sensor ranges could be increased instead. Conversely, for farther targets, history length is as valuable or more valuable than sensor range. Thus, just as optimal shutter speed varies with light availability and speed of the subject, passive situational awareness in multi-robot teams is best achieved with different strategies depending on proximity to locations of interest. All studies use the teams of Thymio II physical, two-wheeled robots in laboratory environments 1.1Data and models used are available at https://github.com/PavlicLab/ACSOS2020_ReTLo_Extension.git
KW - artificial intelligence
KW - machine learning
KW - multi-robot system
UR - http://www.scopus.com/inward/record.url?scp=85092743217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092743217&partnerID=8YFLogxK
U2 - 10.1109/ACSOS49614.2020.00036
DO - 10.1109/ACSOS49614.2020.00036
M3 - Conference contribution
AN - SCOPUS:85092743217
T3 - Proceedings - 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2020
SP - 146
EP - 152
BT - Proceedings - 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2020
A2 - El-Araby, Esam
A2 - Tomforde, Sven
A2 - Wood, Timothy
A2 - Kumar, Pradeep
A2 - Raibulet, Claudia
A2 - Petri, Ioan
A2 - Valentini, Gabriele
A2 - Nelson, Phyllis
A2 - Porter, Barry
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2020
Y2 - 17 August 2020 through 21 August 2020
ER -