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Path planning for multi-robot systems in complex dynamic environments is a key issue in autonomous robotics research. In response to the challenges posed by such environments, this paper proposes a dual-layer symmetric path planning algorithm that integrates an improved Glasius bio-inspired neural network (GBNN) and an enhanced dynamic window approach (DWA). This algorithm enables real-time obstacle avoidance for multi-robots in dynamic environments while effectively addressing robot-to-robot conflict issues. First, to address the low global optimization capability of the GBNN algorithm in the first layer, a signal waveform propagation model for single-neuron signals is established, enhancing the global optimization ability of the algorithm. Additionally, a path optimization function is developed to remove redundant points along the path, improving its efficiency. In the second layer, based on the global path, a reward function is introduced into the DWA. The Score function within the DWA algorithm is also modified to enable symmetric path adjustments, effectively reducing detour paths and minimizing the probability of deviation from the planned trajectory while ensuring real-time obstacle avoidance under the condition of maintaining the global path’s optimality. Next, to address conflicts arising from multi-robot encounters, a dynamic priority method based on distance is proposed. Finally, through multi-dimensional comparative experiments, the superiority of the proposed method is validated. Experimental results show that, compared with other algorithms, the improved neural network-DWA algorithm significantly reduces path length and the number of turns. This research contributes to enhancing the efficiency, adaptability, and safety of multi-robot systems.
Teng et al. (Tue,) studied this question.