未知で複雑な環境におけるオンラインUAV経路計画のための動的Qプランニング
Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments
Q学習の反復回数を動的に決定する手法を提案し、未知で複雑な環境でのUAVオンライン経路計画において、固定反復回数のQ学習やA*、RRT、PSOより有効で信頼性が高いことを示した。
著者: Lidia Gianne Souza da Rocha, Kenny Anderson Queiroz Caldas, Marco Henrique Terra, Fabio Ramos, Kelen Cristiane Teixeira Vivaldini
分類: cs.RO
原文アブストラクト
Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can generate trajectories in real-time and has demonstrated fast and reliable results. This technique, however, has the disadvantage of defining the iteration number. If this value is not well defined, it will take a long time or not return an optimal trajectory. Therefore, we propose a method to dynamically choose the number of iterations to obtain the best performance of Q-Learning. The proposed method is compared to the Q-Learning algorithm with a fixed number of iterations, A*, Rapid-Exploring Random Tree, and Particle Swarm Optimization. As a result, the proposed Q-learning algorithm demonstrates the efficacy and reliability of online path planning with a dynamic number of iterations to carry out online missions in unknown and complex environments.
