Q-IGA-based Path Planning with Dynamically Fitted Bezier Curve

Xu Yan, Cui Yuanyuan

Abstract

In path planning，in order to obtain an optimal path suitable for actual situation and overcome the inherent shortcomings of genetic algorithm，such as easy converging to local optimal and high complexity，a Q-standard Improved Genetic Algorithm  (Q-IGA) based path planning algorithm with dynamically fitted Bezier curve is proposed in this paper. The proposed algorithm replaces the static fitting method of directly using Bezier curve in order to simultaneously search the path and control points of Bezier curve. What's more，an additional judgment criterion based on Q value is added into selection operator，which can eliminate the solutions with high similarity and enhance the diversity of the population. At the same time，the proposed method optimizes the fitness function by taking robot volume and turning angle into consideration，so that the selected path is not only short but also a reasonable path which keeps a safe distance from the obstacles. Simulation results show that the paths produced by Q-IGA algorithm are more reasonable than those produced by improved artificial potential field algorithm and hybrid genetic algorithm. As it can reduce the search time and the energy consumption of the robot，the proposed method is more suitable for practical industrial applications.

 

 

Keywords:    mobile robot,  path planning,  Q-IGA,  Bezier curve

 

 


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References


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