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An optimal and efficient hierarchical motion planner for industrial robots with complex constraints
参考中译:针对具有复杂约束的工业机器人的优化高效分层运动规划器

刊名：	Computers and Electrical Engineering
作者：	Longfei Zhang（School of Automation, Central South University） Zeyang Yin（School of Automation, Central South University） Xiaofang Chen（School of Automation, Central South University） Yongfang Xie（School of Automation, Central South University）
刊号：	738C0073
ISSN：	0045-7906
出版年：	2024
年卷期：	2024, vol.119, no.Pt.B
页码：	109521-1--109521-17
总页数：	17
分类号：	TP39
关键词：	Industrial robots；Motion planning；Reinforcement learning；Hierarchical framework；Optimal trajectory
参考中译：	工业机器人;运动规划;强化学习;分层框架;最佳轨迹
语种：	eng
文摘：	This paper investigates the motion planning problem for industrial robots with complex constraints. An optimal and efficient hierarchical motion planner is proposed to obtain high-quality trajectories with low computational effort. First, the motion planning problem is formulated as an optimal control problem incorporating robot kinematics, obstacle-avoidance, and dynamics constraints. Thereafter, a hierarchical framework is constructed within the Markov decision process, consisting of two planners. In the high-level planner, a reinforcement learning-based policy is employed to generate virtual targets for the robot to navigate around obstacles, which can avoid collision detection. Then, in the low-level planner, a global orthogonal collocation method is used to generate time-energy optimal trajectories, considering dynamics, path, and boundary constraints such as joint position, velocity, and torque. Finally, simulation results on a 6-DOF (degree of freedom) and a 7-DOF industrial robot validate that the proposed method can produce high-quality trajectories with improved success rates and computation times compared to existing works.
参考中译：	研究具有复杂约束的工业机器人的运动规划问题。提出了一种优化、高效的分层运动规划器，以低计算量获得高质量的轨迹。首先，将运动规划问题表述为一个结合机器人运动学、障碍物回避和动力学约束的最优控制问题。此后，在马尔科夫决策过程中构建了一个由两个规划者组成的分层框架。在高层规划器中，采用基于强化学习的策略生成虚拟目标，供机器人绕过障碍物导航，从而避免碰撞检测。然后，在低级规划器中，使用全局垂直配置方法来生成时间-能量最优轨迹，同时考虑动力学、路径和边界约束（例如关节位置、速度和扭矩）。最后，对6-DOF（自由度）和7-DOF工业机器人的仿真结果验证了所提出的方法可以生成高质量的轨迹，与现有作品相比，成功率和计算时间有所提高。

An optimal and efficient hierarchical motion planner for industrial robots with complex constraints参考中译:针对具有复杂约束的工业机器人的优化高效分层运动规划器

An optimal and efficient hierarchical motion planner for industrial robots with complex constraints
参考中译:针对具有复杂约束的工业机器人的优化高效分层运动规划器