GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917959730987008 |
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| author | Vu, Minh Nhat Ebmer, Gerald Watcher, Alexander Ecker, Marc-Philip Nguyen, Giang Glueck, Tobias |
| author_facet | Vu, Minh Nhat Ebmer, Gerald Watcher, Alexander Ecker, Marc-Philip Nguyen, Giang Glueck, Tobias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments Vu, Minh Nhat Ebmer, Gerald Watcher, Alexander Ecker, Marc-Philip Nguyen, Giang Glueck, Tobias Robotics 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. |
| title | GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.14160 |