GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments

Autonomous large-scale machine operations require fast, efficient, and collision-free motion planning while addressing unique challenges such as hydraulic actuation limits and underactuated joint dynamics. This paper presents a novel two-step motion planning framework designed for an underactuated forestry crane. The first step employs GPU-accelerated stochastic optimization to rapidly compute a globally shortest collision-free path. The second step refines this path into a dynamically feasible trajectory using a trajectory optimizer that ensures compliance with system dynamics and actuation constraints. The proposed approach is benchmarked against conventional techniques, including RRT-based methods and purely optimization-based approaches. Simulation results demonstrate substantial improvements in computation speed and motion feasibility, making this method highly suitable for complex crane systems.

@article{vu2025_2503.14160,
  title={ GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments },
  author={ Minh Nhat Vu and Gerald Ebmer and Alexander Watcher and Marc-Philip Ecker and Giang Nguyen and Tobias Glueck },
  journal={arXiv preprint arXiv:2503.14160},
  year={ 2025 }
}