基于Q-IGA动态拟合贝塞尔曲线的路径规划

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

为在路径规划过程中得到一条适用于实际情况的最优路径,并克服遗传算法自身固有的易收敛于局部最优解和复杂度较高的缺点,提出一种基于Q-IGA(Q-standard Improved Genetic Algorithm)算法动态搜索贝塞尔曲线控制点的路径规划算法.该算法摒弃利用贝塞尔曲线直接拟合最优路径的静态方式,使路径搜索与控制点搜索两个过程同时进行;并且在选择算子中添加一个判断准则,利用Q值检验法剔除相似度较高的解决方案,增强种群的多样性;与此同时,优化适应度函数,加入机器人体积及转弯角度带来的代价,使选择出的路径是一条距离较短且与障碍物保持安全距离的合理路径.仿真结果表明,Q-IGA算法比改进人工势场法和混合遗传算法得到的路径更为合理,可降低机器人耗能,减少搜索时间,更适于实际的工业应用.

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.