中文摘要：

针对未知环境下的移动机器人系统,研究了使机器人能同时躲避静态和动态障碍物、且快速抵达目标的路径规划问题。首先通过定义一种新的状态敏感度测度,度量状态与目标之间的关联程度,指导机器人对环境的自主探索方向和力度,进而利用强化学习获得机器人的最优行动策略。通过引入状态敏感度测度,提高算法的学习速度、学习性能。最后通过对环境未知、且具有动态障碍物的路径规划任务的实例仿真,验证了所提方法的有效性。

英文摘要：

Aiming at the mobile robot system in an unknown environment,the path planning problem to avoid both static and dynamic barriers and to reach a target quickly is investigated here.A new state sensitivity is defined to measure the relative degree between the state and the objective.It guides a robot to explore the environment with right direction and strength automatically.A reinforcement learning algorithm is adopted to learn the best action policy of a robot.By introducing state sensitivity,the speed and performance of learning algorithm are improved.Simulation results from a path planning task with an unknown environment and dynamic barriers verify the efficiency of the proposed algorithm.