基于改进遗传算法的多建筑机器人任务分配策略
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天津理工大学 机械工程学院

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TP242.6

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国家自然科学基金联合(U1813222)


Multi-construction robot task allocation strategy based on improved genetic algorithm
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School of Mechanical Engineering, Tianjin University of Technology

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    摘要:

    为优化多建筑机器人任务分配问题,建立了以机器人行进距离最短为目标的数学模型,将机器人电量作为约束,运用遗传算法对模型进行求解。为加快算法的寻优能力和收敛速度,首先利用改良圈策略优化初始种群,其次引入自适应交叉变异算子以及二次变异对个体进行调整,最后采用精英保留策略保证优良个体不会被破坏。在两组不同机器人数量及任务数量情况下进行任务分配仿真实验,仿真结果表明,改进遗传算法相比于基本遗传算法求解出的路径长度缩短了3.7%和12.5%,验证了改进算法的有效性。

    Abstract:

    To optimize the task allocation problem of multi-construction robots, a mathematical model is established with the goal of minimizing the shortest robot travel distance. The robot's electric power is used as a constraint, and a genetic algorithm is employed to solve the model. To enhance the optimization capability and convergence speed of the algorithm, several strategies are implemented. First, the improved circle strategy optimizes the initial population. Then, the adaptive crossover mutation operator and secondary mutation are introduced to adjust individuals. Finally, the elite retention strategy ensures that excellent individuals are preserved.Task allocation simulation experiments were conducted under two groups with different numbers of robots and tasks. The simulation results show that the path length solved by the improved genetic algorithm is shortened by 3.7% and 12.5% compared with the basic genetic algorithm, verifying the effectiveness of the improved algorithm.

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刘凯超,孙启湲,刘振忠. 基于改进遗传算法的多建筑机器人任务分配策略[J]. 科学技术与工程, , ():

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  • 收稿日期:2024-05-13
  • 最后修改日期:2024-05-27
  • 录用日期:2024-05-28
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