TY - GEN
T1 - Distributed and Asynchronous Planning and Execution for Multi-agent Systems through Short-Sighted Conflict Resolution
AU - Miyashita, Yuki
AU - Yamauchi, Tomoki
AU - Sugawara, Toshiharu
N1 - Funding Information:
Acknowledgment: This work was partly supported by JSPS KAKENHI Grant Numbers 20H04245 and 17KT0044.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We propose a distributed method for a multi-agent pick-up and delivery problem with fluctuations in agent movement speeds while agents perform planning, detect and resolve conflicts (collisions) between the plans, and execute actions in the plans in a distributed manner. Our study assumes that the robot's movement speed can fluctuate, owing to various factors, thus delaying their scheduled tasks. Such delays can rapidly cause other agent conflicts to cascade and render long-term plans useless. Our proposed method allows each agent's plans to be executed and modified using an advanced short-sighted conflict resolution mechanism. Hence, although an agent attempts to follow its given sequence of actions, it performs each one after carefully checking for any conflict in the next few steps. Our method is fully distributed and works effectively, even when the number of task endpoints, which are the pick-up and delivery locations, is small and the agents are concentrated. We experimentally confirm that our method works efficiently without collisions in environments having agent speed fluctuations and deadlocks using example problems from robot movement in a construction site. Further, we compare the performance of our method with that of the baseline method.
AB - We propose a distributed method for a multi-agent pick-up and delivery problem with fluctuations in agent movement speeds while agents perform planning, detect and resolve conflicts (collisions) between the plans, and execute actions in the plans in a distributed manner. Our study assumes that the robot's movement speed can fluctuate, owing to various factors, thus delaying their scheduled tasks. Such delays can rapidly cause other agent conflicts to cascade and render long-term plans useless. Our proposed method allows each agent's plans to be executed and modified using an advanced short-sighted conflict resolution mechanism. Hence, although an agent attempts to follow its given sequence of actions, it performs each one after carefully checking for any conflict in the next few steps. Our method is fully distributed and works effectively, even when the number of task endpoints, which are the pick-up and delivery locations, is small and the agents are concentrated. We experimentally confirm that our method works efficiently without collisions in environments having agent speed fluctuations and deadlocks using example problems from robot movement in a construction site. Further, we compare the performance of our method with that of the baseline method.
KW - Distributed robotics planning
KW - Multi-agent path planning
KW - Multi-agent pick-up and delivery problem
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U2 - 10.1109/COMPSAC54236.2022.00012
DO - 10.1109/COMPSAC54236.2022.00012
M3 - Conference contribution
AN - SCOPUS:85136914687
T3 - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
SP - 14
EP - 23
BT - Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022
A2 - Va Leong, Hong
A2 - Sarvestani, Sahra Sedigh
A2 - Teranishi, Yuuichi
A2 - Cuzzocrea, Alfredo
A2 - Kashiwazaki, Hiroki
A2 - Towey, Dave
A2 - Yang, Ji-Jiang
A2 - Shahriar, Hossain
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
Y2 - 27 June 2022 through 1 July 2022
ER -