Continuous collision detection of pairs of robot motions under velocity uncertainty
Journal article, 2021

In automotive manufacturing, production systems typically involve multiple robots and today are being individualized by utilizing the concept of digital twins. Therefore, the robot programs need to be verified for each individual product. A crucial aspect is to avoid collisions between robots by velocity tuning: this involves an efficient analysis of pairs of robot paths and determining if swept volumes of (sub) paths are disjoint. In general, velocity uncertain motions require disjoint sweep volumes to be safe. We optimize a clearance lower bounding function to provide new sample points for clearance computations. Due to the computational cost of each distance query, our sampling strategy aims to maximize the information gained at each query. The algorithm terminates when robot paths are verified to be disjoint or a collision is detected. Our approach for disjoint paths is inspired by the technique for continuous collision detection known as \textit{conservative advancement}. Our tests indicate that the proposed sampling method is reliable and computationally much faster than creating and intersecting octrees representing the swept volumes.

Continuous Collision Detection

Multi-Robot Systems

Computational Geometry

Motion and Path Planning


Edvin Åblad

Fraunhofer-Chalmers Centre

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Domenico Spensieri

Fraunhofer-Chalmers Centre

Chalmers, Industrial and Materials Science, Product Development

Robert Bohlin

Fraunhofer-Chalmers Centre

Ann-Brith Strömberg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

IEEE Transactions on Robotics

1552-3098 (ISSN)

Vol. In Press

Smart Assembly 4.0

Swedish Foundation for Strategic Research (SSF) (RIT15-0025), 2016-05-01 -- 2021-06-30.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Computational Mathematics


Driving Forces

Sustainable development

Areas of Advance



Basic sciences



Related datasets

DOI: 10.1109/TRO.2021.3050011

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