Real-Time globally optimized path planning in a dynamic environment combing artificial potential field and fuzzy neural network

摘要

This paper explores another way, which combines Artificial Potential Field (APF) and Fuzzy Neural Network (FNN) for the real-time globally optimized path planning in a dynamic environment. This method used Fuzzy Logic to establish the space model, then combined APF and FNN to search for the shortest path and avoid the obstacles smoothly in a dynamic environment. The 3D simulation, which performed in Microsoft Robotics Developer Studio, showed that the method introduced in this paper is effectively, and can be apply to any dynamic environment for real-time globally optimized path planning of mobile robots.