Genetic algorithm based path planning and dynamic obstacle avoidance of mobile robots

Author(s):  
Woong-Gie Han ◽  
Seung-Min Baek ◽  
Tae-Yong Kuc
2011 ◽  
Vol 217-218 ◽  
pp. 1775-1780
Author(s):  
Hui Ying Dong ◽  
Yang Song ◽  
Yu Zhao

This paper is about dynamic obstacle avoidance. Delaunay Graph is used for modeling the working space, an approximate shortest path of mobile robot is determined by using floyd algorithm. Path can be found easily with genetic algorithm. Then genetic algorithm is used for obtaining the optimum path. It may meet which dynamic obstacle when robot follows optimum path. so it should avoid it. Results of simulation show that this path planning method is simple and realized easily.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jianjun Ni ◽  
Wenbo Wu ◽  
Jinrong Shen ◽  
Xinnan Fan

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.


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