This study focuses on the multi-robot navigation problem with unpredictable state transition disturbance. The primary goal is to construct a fully distributed multi-robot navigation method without pre-allocating target positions. To this aim, a reinforcement learning based method is presented, in which a distribution of state transition module is proposed to guarantee adaptiveness when trained policies are applied in physical multi-robot systems. The method incorporates a centralized training but fully distributed execution framework. The former can eliminate non-stationarity of the environment, and the latter enables the robots to collaboratively handle partially observable scenarios. Mean while, the designed reward function can guide the robots to approach not pre-allocated target positions and the nearly optimal trajectories are achieved in continuous environment. After training, the robots make decisions independently, coordinate, and cooperate with each other to determine the next actions from their current positions before arriving in target positions without pre-allocation, in which the trajectories are nearly optimal with partial observation available for each robot. Simulations are performed with increasingly complex environments, such as the addition of static obstacles and randomly moving obstacles. The results show that the robots are able to achieve the primary goal with different state transition disturbance, which demonstrates the feasibility, effectiveness, and robustness. Furthermore, experiments are carried out using our multi-robot system corresponding to the simulation. The experimental results demonstrate the effectiveness and robustness of the proposed navigation method to handle a variety of typical robotic scenarios.

这项研究的重点是具有无法预测的状态转换扰动的多机器人导航问题。主要目标是构造一种完全分布式的多机器人导航方法，而无需预先分配目标位置。为此，提出了一种基于强化学习的方法，其中提出了状态转换模块的分布，以保证在物理多机器人系统中应用经过训练的策略时能够保证自适应性。该方法结合了集中训练但完全分布式的执行框架。前者可以消除环境的不稳定性，而后者可以使机器人协作处理部分可观察的场景。同时，设计的奖励功能可以引导机器人逼近未预先分配的目标位置，并且在连续环境中获得接近最佳的轨迹。训练后，这些机器人独立进行决策，相互协调和协作，以从其当前位置确定下一个动作，然后无需预先分配即可到达目标位置，其中的轨迹几乎是最优的，每个机器人都可以进行部分观察。模拟是在越来越复杂的环境中执行的，例如添加了静态障碍物和随机移动的障碍物。结果表明，该机器人能够在不同的状态转移扰动下达到主要目的，证明了其可行性，有效性和鲁棒性。此外，实验是使用与模拟相对应的多机器人系统进行的。实验结果证明了所提出的导航方法在处理各种典型机器人场景时的有效性和鲁棒性。