Sensors, Vol. 20, Pages 5630: UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy

Sensors, Vol. 20, Pages 5630: UAV Autonomous Tracking and Landing Based on Deep Reinforcement Learning Strategy Sensors doi: 10.3390/s20195630 Authors: Jingyi Xie Xiaodong Peng Haijiao Wang Wenlong Niu Xiao Zheng Unmanned aerial vehicle (UAV) autonomous tracking and landing is playing an increasingly important role in military and civil applications. In particular, machine learning has been successfully introduced to robotics-related tasks. A novel UAV autonomous tracking and landing approach based on a deep reinforcement learning strategy is presented in this paper, with the aim of dealing with the UAV motion control problem in an unpredictable and harsh environment. Instead of building a prior model and inferring the landing actions based on heuristic rules, a model-free method based on a partially observable Markov decision process (POMDP) is proposed. In the POMDP model, the UAV automatically learns the landing maneuver by an end-to-end neural network, which combines the Deep Deterministic Policy Gradients (DDPG) algorithm and heuristic rules. A Modular Open Robots Simulation Engine (MORSE)-based reinforcement learning framework is designed and validated with a continuous UAV tracking and landing task on a randomly moving platform in high sensor noise and intermittent measurements. The simulation results show that when the moving platform is moving in different trajectories, the average landing success rate of the proposed algorithm is about 10% higher tha...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research