Efficient Collision Detection for Long and Slender Robotic Links in Euclidean Distance Fields: Application to a Forestry Crane
🤖 AI Summary
Spherical approximations in motion planning for slender manipulators—such as forestry cranes—lead to low accuracy and poor efficiency in complex outdoor environments.
Method: This paper proposes a novel collision detection and motion planning framework based on voxelized Euclidean Distance Fields (EDFs), explicitly leveraging the slender geometric structure of robotic links. It replaces conventional sphere-based decomposition—and its sensitivity to tuning parameters—with a lightweight, high-fidelity collision criterion. Integrating real-time LiDAR perception, the method constructs dynamic environment EDF models.
Results: Evaluated on real forestry point clouds and high-fidelity simulations, the approach achieves a 3.2× speedup in large-scale manipulator planning while significantly improving obstacle avoidance robustness and maintaining sub-centimeter collision detection accuracy.
Application Category
📝 Abstract
Collision-free motion planning in complex outdoor environments relies heavily on perceiving the surroundings through exteroceptive sensors. A widely used approach represents the environment as a voxelized Euclidean distance field, where robots are typically approximated by spheres. However, for large-scale manipulators such as forestry cranes, which feature long and slender links, this conventional spherical approximation becomes inefficient and inaccurate. This work presents a novel collision detection algorithm specifically designed to exploit the elongated structure of such manipulators, significantly enhancing the computational efficiency of motion planning algorithms. Unlike traditional sphere decomposition methods, our approach not only improves computational efficiency but also naturally eliminates the need to fine-tune the approximation accuracy as an additional parameter. We validate the algorithm's effectiveness using real-world LiDAR data from a forestry crane application, as well as simulated environment data.
Problem
Research questions and friction points this paper is trying to address.
Improves collision detection for long slender robotic links
Enhances efficiency in motion planning for forestry cranes
Eliminates need for fine-tuning spherical approximation accuracy
Innovation
Methods, ideas, or system contributions that make the work stand out.
Novel collision detection for elongated manipulators
Enhances computational efficiency in motion planning
Eliminates need for fine-tuning approximation accuracy
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