Collision Detection Accelerated: An Optimization Perspective - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Collision Detection Accelerated: An Optimization Perspective


Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem, with the Gilbert, Johnson and Keerthi (GJK) algorithm being the most common approach today. In this work we leverage the fact that collision detection is fundamentally a convex optimization problem. In particular, we establish that the GJK algorithm is a specific sub-case of the well-established Frank-Wolfe (FW) algorithm in convex optimization. We introduce a new collision detection algorithm by adapting recent works linking Nesterov acceleration and Frank-Wolfe methods. We benchmark the proposed accelerated collision detection method on two datasets composed of strictly convex and non-strictly convex shapes. Our results show that our approach significantly reduces the number of iterations to solve collision detection problems compared to the state-of-the-art GJK algorithm, leading to up to two times faster computation times.
Fichier principal
Vignette du fichier
Collision_Detection_Accelerated__An_Optimization_Perspective_RSS22.pdf (1.55 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03662157 , version 1 (09-05-2022)


  • HAL Id : hal-03662157 , version 1


Louis Montaut, Quentin Le Lidec, Vladimir Petrik, Josef Sivic, Justin Carpentier. Collision Detection Accelerated: An Optimization Perspective. RSS 2022 - Robotics: Science and Systems, Jun 2022, New York, United States. ⟨hal-03662157⟩
526 View
407 Download


Gmail Facebook X LinkedIn More