GPU-Accelerated Motion Planning of an Underactuated Forestry Crane in Cluttered Environments
🤖 AI Summary
This work addresses the challenge of real-time, collision-free, and dynamically feasible motion planning for forestry cranes operating in cluttered environments under hydraulic actuation constraints and underactuated dynamics. We propose a novel two-stage GPU-accelerated framework: first, parallel stochastic optimization rapidly computes a globally shortest collision-free path; second, a nonlinear trajectory optimization—explicitly incorporating hydraulic actuator dynamics and underactuated system constraints—generates fully dynamically feasible trajectories. Our method significantly outperforms RRT-based and conventional optimization approaches: replanning latency is in the millisecond range, trajectory dynamic feasibility achieves 100%, and robustness is maintained even in high-density obstacle scenarios. The core contribution lies in the first integration of GPU-accelerated parallel stochastic search with dynamics-aware trajectory optimization for large-scale hydraulic manipulators, enabling real-time, safe, and physically consistent autonomous motion planning in complex unstructured environments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Fast collision-free motion planning for underactuated forestry cranes.
Addressing hydraulic actuation limits and underactuated joint dynamics.
Improving computation speed and motion feasibility in cluttered environments.
Innovation
Methods, ideas, or system contributions that make the work stand out.
GPU-accelerated stochastic optimization for path planning
Trajectory optimizer for dynamic feasibility
Benchmarked against RRT and optimization methods
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2024-08-01arXiv.orgCitations: 0
