Robot path planning in unknown and dynamic environments is one of the hot topics in the field of robot control. The virtual force field (VFF) is an efficient path planning method for robot. However, there are some shortcomings of the traditional VFF based methods, such as the local minimum problem and the higher computational complexity, in dealing with the dynamic obstacle avoidance. In this paper, an improved VFF approach is proposed for the real-time robot path planning, where the environment is unknown and changing. An area ratio parameter is introduced into the proposed VFF based approach, where the size of the robot and obstacles are considered. Furthermore, a fuzzy control module is added, to deal with the problem of obstacle avoidance in dynamic environments, by adjusting the rotation angle of the robot. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.
Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach
