End-Effector Obstacle-Avoidance Trajectory Planning for Industrial Robotic Manipulators
TL;DR
The experimental results demonstrate that the proposed Field-guided RRT achieves a better balance among path efficiency, planning time, obstacle-clearance maintenance, and trajectory execution capability compared with conventional RRT-based methods.
Abstract
End-effector obstacle-avoidance trajectory planning is essential for improving the autonomy, safety, and executability of industrial robotic manipulators in constrained workspaces. Conventional Rapidly Exploring Random Tree (RRT) planners provide effective exploration capability but often suffer from stochastic tree expansion, redundant trajectories, and insufficient directional guidance near obstacle regions, which limits planning efficiency and trajectory quality. This study proposes a clearance-field-guided RRT framework with behavior-cloning-assisted refinement for end-effector obstacle-avoidance trajectory planning of industrial robotic manipulators. The proposed method formulates the planning problem in Cartesian space based on an end-effector kinematic model and introduces local clearance-field guidance into the RRT sampling process. Candidate samples are evaluated by considering obstacle clearance, reference-line deviation, and goal distance, enabling the search tree to preferentially expand toward effective traversable regions while maintaining the exploration capability of conventional RRT. Behavior cloning is further introduced as an offline auxiliary strategy to investigate the influence of expert trajectories on local motion-direction learning and trajectory continuity. A Python–Unity joint simulation–verification framework and a physical manipulator experimental platform are established to evaluate the feasibility and practical executability of the generated trajectories. Python is used for offline trajectory generation, expert dataset construction, behavior-cloning training, and performance evaluation, while Unity is employed for three-dimensional manipulator modeling and trajectory reproduction. The experimental results demonstrate that the proposed Field-guided RRT achieves a better balance among path efficiency, planning time, obstacle-clearance maintenance, and trajectory execution capability compared with conventional RRT-based methods. The proposed framework provides an effective solution for collision-free end-effector trajectory planning in industrial applications such as assembly, welding, component placement, and robotic inspection.