Toward Safe Distributed Multi-Robot Navigation Coupled With Variational Bayesian Model

Lin Chen

doi:10.1109/TASE.2023.3346049

Toward Safe Distributed Multi-Robot Navigation Coupled With Variational Bayesian Model

2024-10-01

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DOI：10.1109/TASE.2023.3346049

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Designing a safe and effective collision avoidance policy for multiple robots is essential in decentralized scenarios, where each robot is responsible for generating its own paths, to ensure their safe operation. Recently, the utilization of reinforcement learning to develop decentralized policies that enable multiple robots to move cooperatively and accomplish tasks has yielded positive outcomes. However, the presence of exploration unsafe actions during the reinforcement learning training process results in inadequate safety. We seek to enhance the safety of distributed multi-robot navigation policies and propose a new imitation learning framework based on the variational Bayesian model, which enables robots to learn safe actions by anticipating the subsequent state they are expected to reach. In addition, a new policy neural network structure for multi-robot navigation is proposed by introducing the transformer structure, which encodes the significance of nearby robots in relation to their forthcoming conditions. Experiments demonstrated that our policy can more safely guide robots to navigate in multi-robot environments under conditions of limited information, outperforming the state-of-the-art RL-RVO method in terms of success rate.

Keywords:

Robots

Collision avoidance

Navigation

Robot kinematics

Task analysis

Safety

Robot sensing systems

Multi-robot systems

imitation learning

collision avoidance

safe navigation

variational Bayesian methods

Toward Safe Distributed Multi-Robot Navigation Coupled With Variational Bayesian Model

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