Your browser doesn't support javascript.
loading
RRT*-based Path Planning for Continuum Arms.
Meng, Brandon H; Godage, Isuru S; Kanj, Iyad.
Afiliación
  • Meng BH; School of Computing, DePaul University, Chicago, IL 60604, USA.
  • Godage IS; School of Computing, DePaul University, Chicago, IL 60604, USA.
  • Kanj I; School of Computing, DePaul University, Chicago, IL 60604, USA.
IEEE Robot Autom Lett ; 7(3): 6830-6837, 2022 Jul.
Article en En | MEDLINE | ID: mdl-36532612
ABSTRACT
Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this paper, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IEEE Robot Autom Lett Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: IEEE Robot Autom Lett Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos