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Auteurs principaux: Rassaerts, Lotte, Suichies, Eke, van de Vrande, Bram, Alonso, Marco, Meere, Bas, Chong, Michelle, Torta, Elena
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2410.12659
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author Rassaerts, Lotte
Suichies, Eke
van de Vrande, Bram
Alonso, Marco
Meere, Bas
Chong, Michelle
Torta, Elena
author_facet Rassaerts, Lotte
Suichies, Eke
van de Vrande, Bram
Alonso, Marco
Meere, Bas
Chong, Michelle
Torta, Elena
contents This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.
format Preprint
id arxiv_https___arxiv_org_abs_2410_12659
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Non-Conservative Obstacle Avoidance for Multi-Body Systems Leveraging Convex Hulls and Predicted Closest Points
Rassaerts, Lotte
Suichies, Eke
van de Vrande, Bram
Alonso, Marco
Meere, Bas
Chong, Michelle
Torta, Elena
Robotics
This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.
title Non-Conservative Obstacle Avoidance for Multi-Body Systems Leveraging Convex Hulls and Predicted Closest Points
topic Robotics
url https://arxiv.org/abs/2410.12659