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针对工业机器人应用中常见的一类涉及离散多路径组合优化的任务规划问题进行了研究,通过将其转化为非对称哈密顿图表示,采用统一的开环式广义旅行商问题的框架进行建模和求解,由此建立了相应的代价矩阵和目标函数,在此基础上提出了一种新的具有多染色体结构的遗传算法来寻找问题的全局最优解.通过采用不同的染色体分别表示路径的顺序和方向,改进了传统遗传算法容易陷入局部最优值的缺陷,提高了算法的搜索能力和收敛速度.仿真研究中通过与传统TSP问题遗传求解算法的比较,证明了本方法的有效性和可行性.
Aiming at the problem of mission planning of discrete multi-path combinatorial optimization which is common in industrial robots, this paper studies the problem of mission planning by using the unified open-loop generalized traveling salesman problem framework by transforming it into an asymmetric Hamilton graph representation And the corresponding cost matrix and objective function are established. Based on this, a new genetic algorithm with multi-chromosome structure is proposed to find the global optimal solution of the problem. By using different chromosomes to represent the path Order and direction to improve the traditional genetic algorithm easily fall into the local optimal value of the defect and improve the search ability of the algorithm and the convergence rate in the simulation study with the traditional genetic algorithm TSP problem compared to prove the effectiveness of the method and feasibility.