Reinforcement learning-guided multi-objective trajectory planning for obstacle avoidance in robotic manipulators
This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator, which simultaneously minimizes path length, energy consumption, and execution time while satisfying collision-avoidance, kinematic, and dynamic constraints.
Zhen-Long Zhao, Shu-Tao Hao, Bi-Hao Jin et al.
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