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Immune optimization based multi-objective six-DOF trajectory planning for industrial robot manipulators

机译:基于免疫优化的工业机器人多目标六自由度轨迹规划

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Optimal trajectory planning for robot manipulators is always a hot spot in research fields of robotics. This paper presents a Constraint Immune Multi-objective Optimization Algorithm (CIMOA) for computing optimal trajectory of an industrial robot manipulator. The problem has a multi-criterion character in which two objectives, the minimum traveling time objective and minimum mechanical energy consumption objective are considered. In this paper, a model of trajectory planning for six-DOF manipulator based on CIMOA is established. With robot's mechanism system constraints, taking minimum traveling time and minimum energy as criterion, the robot trajectories are planned using CIMOA. Compared the results with single objective algorithm by genetic algorithm (GA) and weighted objective genetic algorithm (WGA), it is shown that, the travelling time and consuming energy by multiple objective method CIMOA are much less, and the trajectory in joint space is much smoother.
机译:机器人操纵器的最优轨迹规划一直是机器人学研究领域的热点。本文提出了一种用于计算工业机器人机械手最佳轨迹的约束免疫多目标优化算法(CIMOA)。该问题具有多标准特征,其中考虑了两个目标,即最小行驶时间目标和最小机械能耗目标。本文建立了基于CIMOA的六自由度机械臂轨迹规划模型。在机器人的机械系统约束下,以最小的行进时间和最小的能量为标准,使用CIMOA计划了机器人的轨迹。将遗传算法(GA)与加权目标遗传算法(WGA)与单目标算法的结果进行比较,结果表明,多目标方法CIMOA的行进时间和消耗的能量要少得多,关节空间的轨迹要大得多。更顺畅

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