TY - GEN
T1 - Multi-robot containment and disablement
AU - Maymon, Yuval
AU - Agmon, Noa
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - This paper presents the multi-robot containment and disablement (CAD) problem. In this problem, a team of (ground or aerial) robots are engaged in a cooperative task of swarm containment and disablement (for example, locust swarm). Each team member is equipped with a tool that can both detect and disable the swarm individuals. The swarm is active in a given physical location, and the goal of the robots is twofold: to contain the swarm members such that the individuals will be prevented from expanding further beyond this area (this is referred to as perfect enclosure), and to fully disable the locust by reducing the size of the contained area (while preserving the perfect enclosure). We determine the minimal number of robots necessary to ensure perfect enclosure, and a placement of the robots about the contained area such that they will be able to guarantee perfect enclosure, as well as a distributed area reduction protocol maintaining perfect enclosure. We then suggest algorithms for handling the case in which there are not enough robots to guarantee perfect enclosure, and describe their performance based on rigorous experiments in the TeamBots simulator.
AB - This paper presents the multi-robot containment and disablement (CAD) problem. In this problem, a team of (ground or aerial) robots are engaged in a cooperative task of swarm containment and disablement (for example, locust swarm). Each team member is equipped with a tool that can both detect and disable the swarm individuals. The swarm is active in a given physical location, and the goal of the robots is twofold: to contain the swarm members such that the individuals will be prevented from expanding further beyond this area (this is referred to as perfect enclosure), and to fully disable the locust by reducing the size of the contained area (while preserving the perfect enclosure). We determine the minimal number of robots necessary to ensure perfect enclosure, and a placement of the robots about the contained area such that they will be able to guarantee perfect enclosure, as well as a distributed area reduction protocol maintaining perfect enclosure. We then suggest algorithms for handling the case in which there are not enough robots to guarantee perfect enclosure, and describe their performance based on rigorous experiments in the TeamBots simulator.
UR - http://www.scopus.com/inward/record.url?scp=85102395503&partnerID=8YFLogxK
U2 - 10.1109/iros45743.2020.9341073
DO - 10.1109/iros45743.2020.9341073
M3 - منشور من مؤتمر
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 11724
EP - 11731
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -