基于遗传算法的码垛机器人路径规划应用

Path Planning Application of Palletizing Robot Based on Genetic Algorithms

中文关键词: 码垛机器人  遗传算法  路径规划  适应度函数

英文关键词: palletizing robot  genetic algorithms  path planning  fitness function

中文摘要:

      目的 为了改进传统遗传算法在码垛机器人路径规划中可能出现的局部陷阱和过早收敛问题，以及机器人的能耗和路线平滑性问题，提出一种改进的遗传算法机器人路径规划方法。方法 针对传统遗传算法存在的问题，分别对种群初始化、适应度函数、选择算子、交叉算子、变异算子的算法和方式进行调整和改进，对优秀算法进行融合。针对基本遗传算法主要着重于路径最短，从而忽视了机器人的能耗及路径平滑性等问题，设计一种综合考虑距离和转弯次数控制的适应度函数，最后将改进的算法应用于码垛机器人的路径规划中。结果 仿真结果表明，相较于基本遗传算法，提出的算法搜索到的路径质量更高，不仅距离更短，同时转弯次数远远小于其他算法，路径更为平滑，验证了该算法的有效性。 结论 基于该算法的码垛机器人路径在兼顾距离最优的同时，路线更加平滑。由于减少了转向次数，机器人的能耗更低，同时仿真结果表明，该算法的实时性也较好。

英文摘要:

      The work aims to propose an improved genetic algorithm for robot path planning, in order to improve the local traps and premature convergence of the traditional genetic algorithm in the path planning of palletizing robot, as well as the energy consumption and path smoothness of the robot. Firstly, aiming at the problems of traditional genetic algorithm, the algorithms and methods of population initialization, fitness function, selection operator, crossover operator and mutation operator were adjusted and improved, and the excellent algorithms were fused. Aiming at the problem that the basic genetic algorithm mainly focused on the shortest path and thus ignored the energy consumption and path smoothness of the robot, a fitness function which took into full account the control of distance and turning times was proposed. Finally, the improved algorithm was applied to the path planning of the palletizing robot. The simulation results showed that, compared with the basic genetic algorithm, the proposed algorithm could find better path quality. Not only the distance was shorter, but also the turning times were much less than other algorithms, and the path was smoother, which proved the effectiveness of the algorithm. The path of palletizing robot based on the proposed algorithm is smoother while taking into account the optimal distance. Because of the reduction of turning times, the energy consumption of the robot is lower. At the same time, the simulation results show that the real-time performance of the algorithm is better.