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arXiv · 2503.14160

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

Minh Nhat Vu·Gerald Ebmer·Alexander Watcher·Marc-Philip Ecker·Giang Nguyen·Tobias Glueck
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Abstract

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.

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Minh Nhat Vu, Gerald Ebmer, Alexander Watcher, Marc-Philip Ecker, Giang Nguyen, Tobias Glueck. 2025-03-18. GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments. https://arxiv.org/abs/2503.14160

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