Abstract
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.
Highlights
Path planning is one of the important issues in the field of robot navigation
The robot will encounter the local minimum problem by the traditional artificial potential field or virtual force field methods [15]; the methods based on grid map require a high completeness of the environmental information [16]; the fuzzy rule base of fuzzy logic methods is often incomplete [9]; the computation of neural network methods is complex, and the traditional neural network methods need a learning process, which cannot meet the requirements of real-time applications sometimes [17, 18]
An improved virtual force field (VFF) approach is proposed to deal with the path planning problem in unknown and dynamic environments
Summary
Path planning is one of the important issues in the field of robot navigation. The goal of path planning is to find an optimal or suboptimal path from the starting position to the target position, which has been widely used in intelligent transportation system, aerospace, military reconnaissance, family services, underwater exploration, and so forth [1,2,3,4,5,6]. Lei and Li [20] proposed a method of behavior-based control for mobile robot path planning in unknown environments using fuzzy logic. Ni and Yang [22] presented a bioinspired neural network for real-time path planning for multirobot cooperative hunting in unknown environments Those methods introduced above have some advantages; much research on path planning focused on algorithms, and few considered real-world problems, such as the size of obstacles and robots. An improved virtual force field (VFF) approach is proposed to deal with the path planning problem in unknown and dynamic environments. An area ratio parameter is introduced into the traditional VFF based method, considering the size of obstacles and robots in real-world applications.
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