A combined hierarchical reinforcement learning based approach for multi-robot cooperative target searching in complex unknown environments

Author(s):  
Yifan Cai ◽  
Simon X. Yang ◽  
Xin Xu
2021 ◽  
pp. 115795
Author(s):  
Hongwei Tang ◽  
Wei Sun ◽  
Anping Lin ◽  
Min Xue ◽  
Xing Zhang

Author(s):  
Yifan Cai ◽  
Simon X. Yang

Cooperative exploration in unknown environments is fundamentally important in robotics, where the real-time path planning and proper task allocation strategies are the key issues for multi-robot cooperation. In this paper, a PSO-based approach, combined with a fuzzy obstacle avoidance module, is proposed for cooperative robots to accomplish target searching and foraging tasks in unknown environments. The proposed cooperation strategy for a multi-robot system makes use of the potential field function as the fitness function of PSO, while the proposed fuzzy obstacle-avoidance module improves the smoothness of robot trajectory. In the simulation studies, several scenarios with and without the fuzzy module are investigated. The robot trajectory smoothness improvement is demonstrated through the comparative studies.


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