摘要
针对障碍物已知的全局路径规划问题,提出了一种基于改进克隆选择的移动机器人路径规划方法,详细介绍了该算法的主要设计思想与算法流程。采用栅格法建立机器人工作的环境模型,并对克隆选择算法进行了两处改进:对抗体进行接种疫苗操作,提高了抗体的亲和度,加快了算法收敛速度;引入了抗体浓度的概念,保证了群体在进化过程中的多样性。最后对算法进行了仿真实验,验证了其可行性;并与遗传算法进行对比,结果表明改进克隆选择算法规划出的路径质量更高,收敛速度更快,还能有效地防止早熟收敛与局部收敛。
Aiming at the problem of global path planning under the condition of known obstacle,a method based on improved clone selection for path planning was proposed. The main design ideas and specific steps of this algorithm in detail were also introduced. The environment model for mobile robot was built by using the grid method. And three improvements based on clone selection algorithm were made. A vaccination for the antibody was operated to improve the antibody＇s affinity and to accelerate the convergence rate. The concept of antibody concentration was introduced, which ensured the population diversity in the evolutionary process. Finally,the simulation of the algorithm was made,and it proved that the algorithm was feasible. The results show that the comparison of the simulation experiment between the genetic algorithm and the improved clone selection algorithm,the path has higher quality by the improved clone selection algorithm. The convergences are more quickly. And the premature convergence and the local convergence can also be prevented effectively by the improved clone selection algorithm.
出处
《信息技术》
2017年第1期149-153,共5页
Information Technology
关键词
克隆选择
路径规划
接种疫苗
亲和度
浓度
clone selection
path planning
vaccination
affinity
concentration
