Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment
This addresses the challenge of applying robotic systems to social tasks by enhancing exploration capabilities, though it appears incremental as it builds on existing learning-based methods.
The paper tackles the problem of automatic exploration in unknown environments for robotics by proposing a deep reinforcement learning decision algorithm that improves learning efficiency and adaptability, with successful transfer from simulation to a physical robot.
This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to this problem via stacking decision rules is impossible to cover various environments and sensor properties. Learning based control methods are adaptive for these scenarios. However, these methods are damaged by low learning efficiency and awkward transferability from simulation to reality. In this paper, we construct a general exploration framework via decomposing the exploration process into the decision, planning, and mapping modules, which increases the modularity of the robotic system. Based on this framework, we propose a deep reinforcement learning based decision algorithm which uses a deep neural network to learning exploration strategy from the partial map. The results show that this proposed algorithm has better learning efficiency and adaptability for unknown environments. In addition, we conduct the experiments on the physical robot, and the results suggest that the learned policy can be well transfered from simulation to the real robot.