摘要
针对遗传算法易陷入局部最优的不足,在标准遗传算法基础上加入了三个新的操作-复原、重构和录优操作,使改进后的遗传算法收敛于全局最优,并在此基础上以路边约束、动态避障和路径最短作为适应度函数,提出了动态避障的路径规划方法。通过实验仿真验证了算法的有效性、准确性和实时性,并与基于以往的遗传算法的路径规划方法进行比较,结果表明本文提出的方法在产生的路径长度和算法运行时间上都具有更优的性能。
Starting from the disadvantage and two research results of the convergence of the previous genetic algorithm, three operations-restoration, reconstruction and recording the better are added to the standard genetic algorithm to make the algorithm converge to a global optimum without the change of the search randomicity. A path planning method is proposed using the fitness of the roadside constraint, dynamic obstacle avoidance and the shortest distance based on the modified genetic algorithm. The simulation results showed that the proposed method is effective, correct and highly real-time. Furthermore, compared with the path planning method based on previous genetic algorithm from experiments, the proposed one has much better performance in the less time required and the shorter distance travelled.
出处
《传感技术学报》
EI
CAS
CSCD
北大核心
2006年第2期520-524,共5页
Chinese Journal of Sensors and Actuators
关键词
遗传算法
随机性
动态避障
路径规划
genetic algorithm
randomicity
dynamic obstacle avoidance
path planning
