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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

TL;DR

This work addresses real-time motion planning for underactuated forestry cranes operating in cluttered environments by proposing a two-step GPU-accelerated framework. The first stage uses CMA-ES-inspired stochastic optimization with GPU-accelerated collision checking to produce a globally shortest collision-free path, while the second stage refines this path into a dynamically feasible trajectory via direct collocation that enforces system dynamics and hydraulic limits. Key contributions include fast GPU-based collision checking, a global path optimization module, and an efficient trajectory-tracking stage, with Monte Carlo simulations showing near-time (<2 s) trajectory generation and favorable consistency compared to LQR-RRT* and purely optimization-based methods. The framework enables batch trajectory generation and demonstrates substantial speedups without compromising feasibility, making it well-suited for real-time autonomous forestry crane operations in complex environments.

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.

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

TL;DR

This work addresses real-time motion planning for underactuated forestry cranes operating in cluttered environments by proposing a two-step GPU-accelerated framework. The first stage uses CMA-ES-inspired stochastic optimization with GPU-accelerated collision checking to produce a globally shortest collision-free path, while the second stage refines this path into a dynamically feasible trajectory via direct collocation that enforces system dynamics and hydraulic limits. Key contributions include fast GPU-based collision checking, a global path optimization module, and an efficient trajectory-tracking stage, with Monte Carlo simulations showing near-time (<2 s) trajectory generation and favorable consistency compared to LQR-RRT* and purely optimization-based methods. The framework enables batch trajectory generation and demonstrates substantial speedups without compromising feasibility, making it well-suited for real-time autonomous forestry crane operations in complex environments.

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.

Paper Structure

This paper contains 15 sections, 14 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contribution
  4. Mathematical Modeling
  5. Dynamic Model
  6. Method
  7. GPU-Based Collision Checking
  8. Collision-Free Inverse Kinematics
  9. Spline-Based Trajectory Optimization Framework
  10. Stochastic-Based Path Optimization
  11. Trajectory Tracking Optimization
  12. Simulation Results
  13. GPU-based Collision Checking
  14. Monte Carlo Simulations
  15. Conclusion

Figures (4)

  • Figure 1: Forestry crane considered in this work.
  • Figure 2: Illustration of the collision meshes of the crane model.
  • Figure 3: Benchmark environments. (a) Collision with the truck's trunk is considered. (b) Cluttered environment with obstacles.
  • Figure 4: Snapshots of the collision-free motion of the forestry crane in a cluttered environment.