Autonomous exploration driven by dual-target points through multi-agent deep reinforcement learning

In this letter, we present an autonomous navigation system driven by dual target points, designed for goal-directed exploration of unknown environments through multi-agent deep reinforcement learning. The system identifies candidate target points from the environment that could serve as potential navigation directions and selects the best two waypoints based on available data. Guided by these waypoints, robots are directed towards global targets. Utilizing multi-agent reinforcement learning, a movement strategy is trained in a simulated environment using LiDAR sensor data and pose information, enabling two robots to move towards the currently selected target points. Experiments demonstrate that our method effectively conducts target point exploration, and the collaborative exploration by two mobile robots significantly enhances the efficiency of global target point discovery.