Efficient motion planning of highly articulated chains using physics-based sampling - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

Efficient motion planning of highly articulated chains using physics-based sampling

Résumé

We present a novel motion planning algorithm that efficiently generates physics-based samples in a kinematically and dynamically constrained space of a highly articulated chain. Similar to prior kinodynamic planning methods, the sampled nodes in our roadmaps are generated based on dynamic simulation. Moreover, we bias these samples by using constraint forces designed to avoid collisions while moving toward the goal configuration. We adaptively reduce the complexity of the state space by determining a subset of joints that contribute most towards the motion and only simulate these joints. Based on these configurations, we compute a valid path that satisfies non-penetration, kinematic, and dynamics constraints. Our approach can be easily combined with a variety of motion planning algorithms including probabilistic roadmaps (PRMs) and rapidly-exploring random trees (RRTs) and applied to articulated robots with hundreds of joints. We demonstrate the performance of our algorithm on several challenging benchmarks.
Fichier principal
Vignette du fichier
ICRA2007.pdf (517.05 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01148374 , version 1 (04-05-2015)

Identifiants

  • HAL Id : hal-01148374 , version 1

Citer

Russell Gayle, Stephane Redon, Avneesh Sud, Ming C Lin, Dinesh Manocha. Efficient motion planning of highly articulated chains using physics-based sampling. Robotics and Automation, 2007 IEEE International Conference on, Apr 2007, Roma, Italy. pp.3319--3326. ⟨hal-01148374⟩
259 Consultations
155 Téléchargements

Partager

Gmail Facebook X LinkedIn More