Collision Detection Accelerated: An Optimization Perspective

Louis, Montaut,; Lidec, Quentin Le; Petrík, Vladimír; Šivic, Josef; Carpentier, Justin

doi:10.15607/rss.2022.xviii.039

Cited by 11 publications

(15 citation statements)

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“…Remarkably, the shapes are in collision if and only if the origin lies inside the Minkowski difference, i.e., 0 ∈ D, as shown in the seminal work of [10] and revisited in [12]. In summary, the separation vector is obtained by projecting the origin onto the surface of the Minkowski difference.…”

Section: Derivativesmentioning

confidence: 99%

“…Collision detection is a very old topic within the robotics community. It consists in determining whether a pair of shapes are in collision or not, and in finding the contact point locations [1], [10], [11], [12]. To lower the computational burden, the shapes are often assumed to be convex or decomposed as a collection of convex meshes [13].…”

Section: Problem Statementmentioning

confidence: 99%

“…To lower the computational burden, the shapes are often assumed to be convex or decomposed as a collection of convex meshes [13]. Such convexity assumptions have led to highly efficient, generic and robust algorithms to solve collision detection problems, most of which belong to the family of the Gilbert-Johnson-Keerthi (GJK) algorithms [10], [11], [14], [12].…”

Section: Problem Statementmentioning

confidence: 99%

“…The GJK paradigm for collision detection. To handle the problem of collision detection, we put ourselves in the paradigm of GJK [10], [12], to which EPA [11] also belongs. In this paradigm, the focus is set on computing the separation vector between A 1 and A 2 .…”

Section: Derivativesmentioning

confidence: 99%

“…( 10) is directly linked to the Frank-Wolf duality-gap, a convergence criteria which allows to measure the progress towards an optimal solution. We refer to [12] for a complete analysis. Eq.…”

Section: Derivativesmentioning

confidence: 99%

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Differentiable Collision Detection: a Randomized Smoothing Approach

Louis¹,

Lidec²,

Bambade³

et al. 2022

Preprint

Self Cite

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Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications including differentiable simulation.

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Section: Derivativesmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

Section: Derivativesmentioning

confidence: 99%

Section: Derivativesmentioning

confidence: 99%

See 3 more Smart Citations

Differentiable Collision Detection: a Randomized Smoothing Approach

Louis¹,

Lidec²,

Bambade³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Reference-Free Model Predictive Control for Quadrupedal Locomotion

Lunardi,

Corbères,

Mastalli

et al. 2024

IEEE Access

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Full-dynamics model predictive control (MPC) has recently been applied to quadrupedal locomotion in semi-unstructured environments. These advances have been fueled by the availability of efficient trajectory optimization (TO) algorithms and inexpensive computational power. The main advantages of full-dynamics MPC are (i) enabling complex locomotion manoeuvres, (ii) considering actuation limits, and (iii) improving robot stability. However, to make the TO problem sufficiently simple to be solved at run time, reference swing foot trajectories are usually tracked in the MPC formulation. These trajectories are often computed independently of the motion of the joints, limiting the approach generality and capability. To address this limitation, we present a full-dynamics MPC formulation that does not require reference swing-foot trajectories, featuring a novel cost function targeting swing foot motion and considering environmental information. Removing the need for reference swing foot trajectories, our approach can also automatically adjust footstep locations, as long as the contact surfaces are predefined. We have validated our MPC formulation through simulations and experiments on the ANYmal B robot. Our approach has similar computational efficiency to state-of-the-art formulations, while displaying superior push-recovery capabilities on various terrains.

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Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm

Zhu,

Luo

2024

IEEE Access

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The complexity of the environment limits the accuracy of the traditional Adaptive Monte Carlo Localization(AMCL) algorithm, which also suffers from high computational effort and particle degradation due to laser model limitations. To address these issues, an optimized AMCL algorithm with a bounding box is proposed. The AMCL algorithm is first parameterized and initialized to the particle swarm. During the particle iteration process, collision detection is performed on the bounding box. If a collision occurs, the particle filter is not updated and its particle weight is set to 1. If there is no collision, the particle filter is updated normally and the particle weight is set to 0. Then, the particles are resampled and updated based on the measurement data and motion model. After experimental verification, this method's self-localization trajectory is closer to the actual path, and the measurement error fluctuation is smaller. The RVIZ simulation experiments revealed that the overall positioning time was optimized by 18.25% compared to the original AMCL, and by 9.28% compared to the improved AMCL. The optimization algorithm effectively improved the positioning accuracy and robustness of the system.INDEX TEMS adaptive monte carlo localization; bounding box; collision algorithm; particle filter; selflocalization

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Collision Detection Accelerated: An Optimization Perspective

Cited by 11 publications

References 0 publications

Differentiable Collision Detection: a Randomized Smoothing Approach

Differentiable Collision Detection: a Randomized Smoothing Approach

Reference-Free Model Predictive Control for Quadrupedal Locomotion

Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm

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