Fast Path Planning for Unmanned Aerial Vehicles by Self-Correction Based on Q-Learning
Ziyang Wang, Hongbing Yang, Qingsong Wu, Jiafei Zheng · Journal of Aerospace Information Systems · 2021
This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on learning is proposed to achieve multiobjective optimization by minimizing path lengths and correction times while reducing the complexity of the algorithm and improving the efficiency of path planning. The lower-layer algorithm uses a -learning framework based on the experience playback mechanism and exploration/exploitation mechanism. The rewards and punishments for the -table values in the lower-layer algorithm are innovatively managed with the unit “path.” The upper-layer algorithm is based on the Pareto multiobjective algorithm. The Pareto frontier is continuously updated with the solutions from the lower-layer algorithm, which timely provides the lower-layer algorithm feedback. Finally, simulation experiments are conducted to evaluate the effectiveness of the algorithm, and the results show that the proposed algorithm outperforms particle swarm optimization.