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The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.
Han et al. (Fri,) studied this question.