基于免疫遗传算法的多机器人环境探索

Multi-robot environment exploration based on immune genetic algorithm

针对多机器人环境探索中的任务分配和路径规划问题,将环境中所有待探索的任务点根据短距离优先策略分配至个体机器人,利用改进的免疫遗传算法对机器人分配到的任务点进行优化探索,提出了带有初始任务点优化的路径规划方法,使机器人能够不重复并且高效地遍历工作环境中的所有探索点.通过建立多机器人仿真实验系统,随机产生环境中的任务点和机器人等数据信息,并在此条件下对本文方法进行实验验证.结果表明,本文方法能够有效地实现多机器人环境探索问题.

Aiming at the task allocation and path planning problems in the multi-robot environment exploration,the task points to be explored in the environment were allocated to the individual robot according to the short distance-prior strategy,and the task points assigned to the individual robot were optimized and explored with an improved immune genetic algorithm. A path planning method with the optimization of initial task points was proposed,which could make the robots traverse efficiently all exploration points in the work environment without the repetition. Through establishing a multi-robot simulation test system,such data information as the task points and robots in the experiment was randomly generated. Under above-mentioned condition,the proposed method was verified with the experiments. The results showthat the proposed method can effectively realize the multi-robot environment exploration.