Efficient Collision Detection for Long and Slender Robotic Links in Euclidean Distance Fields: Application to a Forestry Crane

TL;DR

This paper introduces a specialized collision detection algorithm for long, slender robotic links like forestry cranes, improving efficiency and accuracy in motion planning within Euclidean distance fields using real-world and simulated data.

Contribution

The paper presents a novel collision detection method tailored for elongated robotic links, eliminating the need for parameter tuning and enhancing computational performance.

Findings

Significant reduction in collision detection computation time.

Effective application demonstrated on real-world forestry crane data.

Improved accuracy over traditional spherical approximation methods.

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.