基于遗传模拟退火算法的移动机器人静态路径规划

Mobile robot static path planning based on genetic simulated annealing algorithm

采用多边形法描述了机器人的工作环境模型,应用简化编码长度的技术简化了工作路径编码方式.对于基于遗传算法产生初始路径种群后的各路径的适应值进行评价.经过多次交叉、变异,并借助模拟退火中Metropolis算法的随机移动准则制定了高效的温度更新函数,获得了从起始点到目标点的一条全局最优路径.最后在Visual C++环境中通过仿真验证了此算法的可行性和高效性.

The polygon method is used to describe robots＇ working environment.The change of two-dimensional codes into one-dimensional codes is adopted to simplify the encoding path.A initialization population was produced based on genetic algorithm,and the fitness value of each path is evaluated.A efficient temperature updating function was devised through a series crossover and mutation,and by adopting the random moving rule of Metropolis algorithm.A global optimal path was obtained from the starting point to the target point.Finally,the feasibility and efficiency of this algorithm are verified in the Visual C ＋ ＋ environment.