Path planning is the core technology of mobile robot decision-making and control and is also a research hotspot in the field of artificial intelligence. Aiming at the problems of slow response speed, long planning path, unsafe factors, and a large number of turns in the conventional path planning algorithm, an improved multiobjective genetic algorithm (IMGA) is proposed to solve static global path planning in this paper. The algorithm uses a heuristic median insertion method to establish the initial population, which improves the feasibility of the initial path and generates a multiobjective fitness function based on three indicators: path length, path security, and path energy consumption, to ensure the quality of the planned path. Then, the selection, crossover, and mutation operators are designed by using the layered method, the single-point crossover method, and the eight-neighborhood-domain single-point mutation method, respectively. Finally, the delete operation is added, to further ensure the efficient operation of the mobile robot. Simulation experiments in the grid environment show that the algorithm can improve the defects of the traditional genetic algorithm (GA), such as slow convergence speed and easy to fall into local optimum. Compared with GA, the optimal path length obtained by planning is shortened by 17%.

中文翻译：

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基于改进多目标遗传算法的移动机器人路径规划

路径规划是移动机器人决策与控制的核心技术，也是人工智能领域的研究热点。针对传统路径规划算法中响应速度慢，规划路径长，不安全因素以及匝数多的问题，提出了一种改进的多目标遗传算法（IMGA）来解决静态全局路径规划问题。该算法采用启发式中值插入法建立初始种群，提高了初始路径的可行性，并基于路径长度，路径安全性和路径能耗三个指标生成了多目标适应度函数，以确保路径质量。计划的路径。然后，使用分层方法设计选择，交叉和变异算子，单点交叉法和八邻域单点突变法。最后，添加删除操作，以进一步确保移动机器人的有效操作。网格环境下的仿真实验表明，该算法可以改善传统遗传算法的收敛速度慢，易于陷入局部最优等缺点。与遗传算法相比，通过规划获得的最佳路径长度缩短了17％。例如收敛速度慢和容易陷入局部最优。与遗传算法相比，通过规划获得的最佳路径长度缩短了17％。例如收敛速度慢和容易陷入局部最优。与遗传算法相比，通过规划获得的最佳路径长度缩短了17％。