摘要
Abstract
To enhance the new-quality public security combat effectiveness,this study proposes a Q-learning-based path planning method for obstacle avoidance in police Unmanned Ground Vehicle(UGV)patrols.A 20×20 grid map with circular and rectangular obstacles is constructed,featuring an 8-directional action space,the ε-greedy strategy,and differentiated reward functions.A target-distance-based Q-table initialization method is introduced to mitigate the computational surge caused by map expansion.Comparative experiments under the same environment(10 independent runs)against A*,SARSA,Artificial Potential Field(APF),and standard Q-learning show that the improved Q-learning achieves an average path length of 29.98±0.88,outperforming SARSA(31.76±4.49),APF(32.63),and standard Q-learning(32.15±1.62).The A* algorithm achieves the shortest path(26.97),but it relies on complete prior map information and lacks online learning capability.The first-iteration steps drop to 45±5,approximately a 96％reduction relative to standard Q-learning and SARSA.The total training steps decrease by approximately 28％and 70％relative to standard Q-learning and SARSA,respectively.These results validate the method's feasibility and effectiveness for complex police UGV patrol path planning.关键词
强化学习/无人车/路径规划/警用/Q值表/仿真Key words
reinforcement learning/unmanned ground vehicle/path planning/policing/Q-table/simulation分类
信息技术与安全科学
