TY - GEN
T1 - Competitive Ant Coverage
T2 - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
AU - Shats, Alon
AU - Amir, Michael
AU - Agmon, Noa
N1 - Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper studies the problem of Competitive Ant Coverage, in which two ant-like robots with very limited capabilities in terms of sensing range, computational power, and knowledge of the world compete in an area coverage task. We examine two variants of the problem that differ in the robot's objective: either being the First to Cover a Cell (FCC), or being the Last to Cover a Cell (LCC). Each robot's goal is to acquire (by visiting first or last, respectively) more cells than the opposing robot, and by that win the game. We examine the problem both theoretically and empirically, and show that the main strategy for dominance revolves around the ability to pursue: in LCC, we wish to pursue the opposing robot, whereas in FCC, we wish to create a scenario wherein the opposing robot pursues us. We find that this ability relies more heavily on knowledge of the opponent's strategy than on the robot's sensing capabilities. Moreover, given the robot's limited capabilities, we find that this knowledge-gap cannot be easily mitigated by learning.
AB - This paper studies the problem of Competitive Ant Coverage, in which two ant-like robots with very limited capabilities in terms of sensing range, computational power, and knowledge of the world compete in an area coverage task. We examine two variants of the problem that differ in the robot's objective: either being the First to Cover a Cell (FCC), or being the Last to Cover a Cell (LCC). Each robot's goal is to acquire (by visiting first or last, respectively) more cells than the opposing robot, and by that win the game. We examine the problem both theoretically and empirically, and show that the main strategy for dominance revolves around the ability to pursue: in LCC, we wish to pursue the opposing robot, whereas in FCC, we wish to create a scenario wherein the opposing robot pursues us. We find that this ability relies more heavily on knowledge of the opponent's strategy than on the robot's sensing capabilities. Moreover, given the robot's limited capabilities, we find that this knowledge-gap cannot be easily mitigated by learning.
UR - http://www.scopus.com/inward/record.url?scp=85182523192&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/iros55552.2023.10342063
DO - https://doi.org/10.1109/iros55552.2023.10342063
M3 - منشور من مؤتمر
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 6733
EP - 6740
BT - 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 October 2023 through 5 October 2023
ER -