Abstract:
The core of multi-robot collision avoidance is to guide robots to avoid collisions with other robots and obstacles in a dynamic multi-robot environment, which has recentl...Show MoreMetadata
Abstract:
The core of multi-robot collision avoidance is to guide robots to avoid collisions with other robots and obstacles in a dynamic multi-robot environment, which has recently gained increasing interest among the main challenges of robotics. However, the current multi-robot navigation policy neural network exhibits weak position encoding capabilities for spatial environmental features in mapping environment states and robot actions, as well as an inability to recurrently infer information on dynamic environmental features in the temporal dimension, leading to insufficient safety and effectiveness in guiding robot motion. In this paper, we propose a novel spatial-temporal RetNet (STR) that encodes reciprocal collision avoidance states between robots in both spatial and temporal dimensions, aiming to enhance the safety and effectiveness of the policy neural network in guiding robots to accomplish specified tasks. The spatial state encoder module is developed based on parallel RetNet structure, which enhances the ability of the neural network in multi-robot navigation policies to extract reciprocal collision avoidance states between robots in spatial dimensions and overcomes the weak position encoding capability of advanced transformer-based multi-robot navigation policy neural networks. A temporal state encoder is designed by introducing the recurrent RetNet structure. This enhances the multi-robot navigation policy neural network’s ability to encode features in the temporal dimension of multi-robot movements and overcomes the transformer-based multi-robot navigation policy neural network’s inability to recurrently infer information in the time dimension. Simulation experiments were designed to demonstrate that the safety and effectiveness of our proposed method outperform the previous state-of-the-art approaches in guiding the robot to complete the task. Physical experiments illustrate that our policy can be effectively applied to real-world systems Note to Practitioners...
Published in: IEEE Transactions on Automation Science and Engineering ( Early Access )