School of Mechanical and Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Math Biosci Eng. 2023 Jan;20(2):2776-2792. doi: 10.3934/mbe.2023130. Epub 2022 Nov 30.
For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a trajectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and convergence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.
针对六自由度工业机械臂的低效轨迹规划问题，提出了一种基于改进多宇宙算法（IMVO）的时间、能量和冲击优化的轨迹规划算法。多宇宙算法在解决单目标约束优化问题时比其他算法具有更好的鲁棒性和收敛精度。然而，它也存在收敛速度慢、容易陷入局部最优等缺点。本文提出了一种改进虫洞概率曲线、自适应参数调整和种群突变融合的方法，以提高收敛速度和全局搜索能力。在本文中，我们修改了 MVO 以进行多目标优化，从而得出帕累托解集。然后，我们通过加权方法构建目标函数，并使用 IMVO 对其进行优化。结果表明，该算法提高了六自由度机械臂在特定约束下的轨迹操作的实时性，并改善了机械臂轨迹规划中的最优时间、能量消耗和冲击问题。
