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

Marc-Philip Ecker, Bernhard Bischof, Minh Nhat Vu, Christoph Fröhlich, Tobias Glück, Wolfgang Kemmetmüller

TL;DR

The paper addresses the computational burden of collision checking for long slender robotic links in Euclidean distance field maps used for outdoor, sensor-based planning. It introduces uni- and bi-directional search strategies that leverage the capsule representation of slender links to skip large free intervals, along with safe-distance handling. Real and simulated experiments on a forestry crane demonstrate 40-80% reductions in collision checks and 20-60% gains in planning time compared to fixed-sphere decompositions and standard libraries, with robustness across link length and radius. The work eliminates the need for tuning sphere-derived accuracy and shows clear practical benefits for large-scale manipulation in robotics hardware-in-the-loop scenarios.

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.

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

TL;DR

The paper addresses the computational burden of collision checking for long slender robotic links in Euclidean distance field maps used for outdoor, sensor-based planning. It introduces uni- and bi-directional search strategies that leverage the capsule representation of slender links to skip large free intervals, along with safe-distance handling. Real and simulated experiments on a forestry crane demonstrate 40-80% reductions in collision checks and 20-60% gains in planning time compared to fixed-sphere decompositions and standard libraries, with robustness across link length and radius. The work eliminates the need for tuning sphere-derived accuracy and shows clear practical benefits for large-scale manipulation in robotics hardware-in-the-loop scenarios.

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.

Paper Structure

This paper contains 18 sections, 2 theorems, 9 equations, 8 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Contribution
  4. Collision Detection for Long Slender Links
  5. Formal Problem Statement
  6. Uni-Directional Search
  7. Bi-Directional Search
  8. Consideration of Safety Distances
  9. Results
  10. Experimental Setup
  11. Monte Carlo Evaluation
  12. Approximation accuracy
  13. Performance
  14. Effect of link length
  15. Effect of link radius and safety distance
  16. ...and 3 more sections

Key Result

Lemma 1

Given the distance $d(\alpha_i)$ at $\mathbf{l}(\alpha_i)$. Then all points $\mathbf{p}\in\mathcal{B}(\alpha_i)$ are collision-free, where

Figures (8)

  • Figure 1: Top: forestry crane considered for the evaluation. Bottom: occupied voxels of the environment map and collision spheres.
  • Figure 2: Uni-directional collision detection for long and slender links in EDF maps.
  • Figure 3: Bi-directional collision detection for long slender links in EDF maps.
  • Figure 4: Gazebo simulation environment.
  • Figure 5: Top: different sphere separation distances, 20cm - 50cm. Bottom: sample trajectories for the motion planning benchmark.
  • ...and 3 more figures

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Proposition 1
  • proof