A New Software Tool for Generating and Visualizing Robot Self-Collision Matrices

TL;DR

This paper presents an interactive, Rust-based visualization tool that enhances the generation and analysis of robot self-collision matrices, supporting multiple shape types and improving efficiency in collision detection workflows.

Contribution

The authors introduce a novel, flexible tool for generating and visualizing self-collision matrices with dynamic inspection and multi-shape support, surpassing limitations of existing static methods.

Findings

Faster and more accurate self-collision queries with diverse shape types.

Enhanced visualization and filtering capabilities for collision matrices.

Improved workflow efficiency in robotics applications.

Abstract

In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in advance, as their configurations are known to always or never result in contact. This information is typically encoded in a self-collision matrix, where each entry (i, j) indicates whether a check should be performed between shape i and shape j. While the MoveIt Setup Assistant is widely used to generate such matrices, current tools are limited by static visualization, lack of proximity support, rigid single-geometry assumptions, and tedious refinement workflows, hindering flexibility and reuse in downstream robotics applications. In this work, we introduce an interactive tool that overcomes these limitations by generating and visualizing self-collision matrices across multiple shape representations, enabling dynamic inspection, filtering, and refinement of shape pairs. Outputs are provided in both JSON and YAML for easy integration. The system is implemented in Rust and uses the Bevy game engine to deliver high-quality visualizations. We demonstrate its effectiveness on multiple robot platforms, showing that matrices generated using diverse shape types yield faster and more accurate self-collision and self-proximity queries.