TY - GEN
T1 - A Fast, Accurate, and Scalable Probabilistic Sample-Based Approach for Counting Swarm Size
AU - Wang, Hanlin
AU - Rubenstein, Michael
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - This paper describes a distributed algorithm for computing the number of robots in a swarm, only requiring communication with neighboring robots. The algorithm can adjust the estimated count when the number of robots in the swarm changes, such as the addition or removal of robots. Probabilistic guarantees are given, which show the accuracy of this method, and the trade-off between accuracy, speed, and adaptability to changing numbers. The proposed approach is demonstrated in simulation as well as a real swarm of robots.
AB - This paper describes a distributed algorithm for computing the number of robots in a swarm, only requiring communication with neighboring robots. The algorithm can adjust the estimated count when the number of robots in the swarm changes, such as the addition or removal of robots. Probabilistic guarantees are given, which show the accuracy of this method, and the trade-off between accuracy, speed, and adaptability to changing numbers. The proposed approach is demonstrated in simulation as well as a real swarm of robots.
UR - http://www.scopus.com/inward/record.url?scp=85092713214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092713214&partnerID=8YFLogxK
U2 - 10.1109/ICRA40945.2020.9196529
DO - 10.1109/ICRA40945.2020.9196529
M3 - Conference contribution
AN - SCOPUS:85092713214
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 7180
EP - 7185
BT - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Y2 - 31 May 2020 through 31 August 2020
ER -