Shu-Tao Hao

Reinforcement learning-guided multi-objective trajectory planning for obstacle avoidance in robotic manipulators

Robotic manipulators operating in cluttered environments require collision-free trajectories that remain executable under kinematic and dynamic constraints. This paper proposes a reinforcement learning (RL)-guided multi-objective trajectory planning framework, termed RL-MOP-HNE, for a 6-DOF UR5 manipulator. The plannin...