RRT*-Based Obstacle Avoidance Motion Planning for Underactuated 7-DOF Overhead Crane

Seven degrees of freedom (7-DOF) overhead cranes are challenging to control due to their underactuated design, complex double pendulum dynamics, and variable cable lengths, which complicate precise positioning, swing suppression, and obstacle avoidance. This paper introduces an RRT-based motion planning strategy that optimizes transport time while ensuring full-state constraints. The approach develops a dynamic model accounting for cable-length variations and double pendulum effects, then formulates motion planning as a constrained optimization problem. A modified RRT^* algorithm with adaptive sampling handles the high-dimensional state space, adhering to actuator and swing limits. The method combines auxiliary signal design with sampling-based planning to generate collision-free payload trajectories. Simulation results demonstrate $\mathbf{5 1. 7 \%}$ faster transportation time compared to conventional methods, near-zero payload swing, and reliable obstacle avoidance in complex 3D environments.