避障
运动规划
障碍物
计算机科学
路径(计算)
增强学习
人工智能
避碰
航空学
实时计算
算法
工程类
地理
强化学习
移动机器人
计算机网络
计算机安全
机器人
考古
碰撞
作者
Yongquan Ning,Ni Li,Jiaming Cheng,Ban Wang,Yufei Peng,Ling Qin
出处
期刊:Lecture notes in electrical engineering
日期:2024-01-01
卷期号:: 247-258
标识
DOI:10.1007/978-981-97-1087-4_24
摘要
Nowadays, Unmanned Aerial Vehicles (UAVs) have been widely used in the area of aerial photography, information collection during emergencies, and goods transportation. Most path planning algorithms require a map of the operation area such that an obstacle-free path can be solved. Path planning and obstacle avoidance become very challenging. In this paper, a variable learning rate Q-learning algorithm for path planning and obstacle avoidance problem is proposed. This algorithm can avoid getting stuck in exploration-exploitation dilemmas and local deadlock states, which is often encountered with the classic Q-learning algorithm. This is accomplished by incorporating state-action variables, employing a variable step size update method, implementing an epsilon-greedy strategy, considering a distance weight factor, and introducing randomness to expedite convergence. Finally, the simulation is conducted to show the superior performance of the proposed algorithm in its convergence speed and rewards.
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