Obstacle-avoidance trajectory planning method for excavators in confined spaces based on improved RRT* algorithm.

As the operation mode of construction machinery is being transformed from traditional manual operation to unmanned autonomous operation, a trajectory planning scheme for obstacle avoidance is proposed in this paper to meet the requirements of unmanned autonomous operation of excavators in complex environments and confined spaces. First, a simulation model is established to represent the working mechanism of the excavator and its surrounding obstacle environment. Next, considering the spatial constraints and environmental obstacles present during excavation and swing-loading operations, an optimal trajectory planning strategy is developed for autonomous excavator operation under restricted conditions. The proposed approach enhances the conventional RRT* algorithm through the incorporation of an environmental parameter-based heuristic search and an adaptive goal-biased strategy featuring dynamic step size adjustment. These improvements collectively augment path search efficiency and trajectory quality. For trajectory optimization, a quintic Non-Uniform Rational B-Spline (NURBS) curve is employed to plan the motion path of the bucket tip. The method concurrently optimizes operation duration and motion smoothness, producing a multi-objective optimal trajectory. Results show the enhanced RRT* reduces path length, iteration count, and computation time by 3.65%, 64.15%, and 67.9%, while improving trajectory smoothness by 33.4%. The approach reliably generates collision-free, smooth, and energy-efficient trajectories, ensuring high efficiency and mechanical reliability for autonomous excavator operations.