A RDA-Based Deep Reinforcement Learning Approach for Autonomous Motion Planning of UAV in Dynamic Unknown Environments. (March 2020) - WAN, Kaifang; GAO, Xiaoguang; HU, Zijian; ZHANG, Wei
Abstract: Autonomous motion planning (AMP) in dynamic unknown environments emerges as an urgent requirement with the prosperity of unmanned aerial vehicle (UAV). In this paper, we present a DRL-based planning framework to address the AMP problem, which is applicable in both military and civilian fields. To maintain learning efficiency, a novel reward difference amplifying (RDA) scheme is proposed to reshape the conventional reward functions and is introduced into state-of-the-art DRLs to constructs novel DRL algorithms for the planner's learning. Different from conventional motion planning approaches, our DRL-based methods provide an end-to-end control for UAV, which directly maps the raw sensory measurements into high-level control signals. The training and testing experiments demonstrate that our RDA scheme makes great contributions to the performance improvement and provides the UAV good adaptability to dynamic environments.