Reinforcement Learning-Based Microrobotic Swarm Navigation and Obstacle Avoidance in Partially Observable Environments

Abstract

Microrobotic swarms have shown promising features due to their collective and flexible behaviours, while achieving precise swarm control and autonomous navigation in complex environments remains a challenge. Here, we propose a Transformer-based reinforcement learning strategy that integrates Proximal Policy Optimization for autonomous swarm control in obstacle environments. By combining domain randomization, this strategy enables direct transfer from simulation to real-world without fine tuning. Experimental results demonstrate robust control performance in avoiding static obstacles and tracking the dynamic target, which is not validated in training. The swarm autonomously navigates and adjusts its velocity and trajectory in obstacle environments with an intact swarm pattern. Our work presents a scalable strategy for the deployment of microrobotic swarms with adaptive navigation capability through complex, constrained environments.