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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操纵器的多目标六-TOF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:机器人操纵器的最佳轨迹规划始终是机器人研究领域的热点。本文介绍了用于计算工业机器人操纵器的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,最小的行驶时间目标和最小机械能耗。本文建立了基于CIMOA的六-DOF操纵器的轨迹规划模型。通过机器人的机制系统限制,将最小的行驶时间和最小能量作为标准,使用CIMOA计划机器人轨迹。通过遗传算法(GA)和加权目标遗传算法(WGA)与单目标算法的结果进行了比较,表明,通过多目标方法CIMOA的行进时间和消耗能量要少得多,并且联合空间的轨迹很大更顺畅。

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