多障碍环境中基于增强式学习的势场优化和机器人路径规划

Potential Field Optimization and Robot Path Planning in Multi-Obstacle Environment Based on Reinforcement Learning

该文把增强式学习方法应用于多障碍环境中机器人路径规划 ,并将增强式学习和路径规划相结合 ,通过工作空间势场的自适应优化学习 ,实现机器人的全局路径规划 ,即得到从任何初始位置开始的最优路径。与传统的人工势场方法相比 ,该方法避免了势场中局部极小点所引起的陷阱区域 ,并且所得到的路径具有最优特性。计算机仿真实验结果表明 。

In this paper the reinforcement learning to robot path planning in complex environment of multiple obstacles is applied. An adaptive control strategy learning method is proposed. Reinforcement learning is an unsupervised learning method based on the reactive and feedback mechanism. In this paper the reinforcement learning and path planning are combined together. The optimal path from any initial position is obtained by optimizing the global potential field and control rules. Compared with traditional artificial potential field method, this method avoids irrelevant local minimal points, which can make the robot vibrate in a small local area. Furthermore, the path found is optimal. The computer simulation experiment result shows that this learning method can efficiently solve the robot path planning problem in multi obstacle environment.