TY - JOUR
T1 - Multi-Agent Coverage Path Planning via Proximity Interaction and Cooperation
AU - Jiao, Lei
AU - Peng, Zhihong
AU - Xi, Lele
AU - Ding, Shuxin
AU - Cui, Jinqiang
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2022/3/15
Y1 - 2022/3/15
N2 - In multi-agent systems, the decision of an agent will be affected by the behaviors of others. Therefore, from the perspective of an agent, the situation is uncertain and random. Inspired by the social behaviors in the biological world, a novel multi-agent coverage path planning algorithm is proposed. Based on the positions of agents, the problem is decoupled, which can effectively reduce the dimension of the decision space. The behavior-guide-point is introduced to guide agents in making decisions, and a new motion mode is presented. To avoid falling into the local optimum, a cooperation mechanism is designed, which can improve the adaptability of the system. Through proximity interaction, the prediction results obtained via the model predictive control (MPC) technology are fused, evaluated, and sorted within the neighborhood, based on which decisions are gained. The proposed algorithm can handle emergencies in unknown environments such as body damage and moving obstacles, and can also be applied to heterogeneous systems. Simulation shows that compared with other algorithms, it has advantages in terms of the makespan and the coverage repetition rate.
AB - In multi-agent systems, the decision of an agent will be affected by the behaviors of others. Therefore, from the perspective of an agent, the situation is uncertain and random. Inspired by the social behaviors in the biological world, a novel multi-agent coverage path planning algorithm is proposed. Based on the positions of agents, the problem is decoupled, which can effectively reduce the dimension of the decision space. The behavior-guide-point is introduced to guide agents in making decisions, and a new motion mode is presented. To avoid falling into the local optimum, a cooperation mechanism is designed, which can improve the adaptability of the system. Through proximity interaction, the prediction results obtained via the model predictive control (MPC) technology are fused, evaluated, and sorted within the neighborhood, based on which decisions are gained. The proposed algorithm can handle emergencies in unknown environments such as body damage and moving obstacles, and can also be applied to heterogeneous systems. Simulation shows that compared with other algorithms, it has advantages in terms of the makespan and the coverage repetition rate.
KW - Multi-agent
KW - adaptive cooperation
KW - coverage path planning
KW - proximity interaction
UR - http://www.scopus.com/inward/record.url?scp=85124752498&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2022.3150098
DO - 10.1109/JSEN.2022.3150098
M3 - Article
AN - SCOPUS:85124752498
SN - 1530-437X
VL - 22
SP - 6196
EP - 6207
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 6
ER -