Cubic Bézier Local Path Planner for Non-holonomic Feasible and Comfortable Path Generation

Guillaume Vailland; Valérie Gouranton; Marie Babel

ICRA 2021poster19 citations

Cubic Bézier Local Path Planner for Non-holonomic Feasible and Comfortable Path Generation

Guillaume Vailland, Valérie Gouranton, Marie Babel

Abstract

In the case of non-holonomic robot navigation, path planning algorithms such as Rapidly-exploring Random Tree (RRT) rarely provide feasible and smooth paths without the need of additional processing. Furthermore, in a transport context like power wheelchair navigation, passenger comfort should be a priority and influence path planning strategy. In this paper, we propose a local path planner which guarantees bounded curvature value and continuous Cubic Bézier piecewise curves connection. To simulate and test this Cubic Bézier local path planner, we developed a new RRT version (CBB-RRT*) which generates on-the fly comfortable path adapted to non-holonomic constraints.

BibTeX

@inproceedings{icra2021_cubicbzierlocalp,
  title = {Cubic Bézier Local Path Planner for Non-holonomic Feasible and Comfortable Path Generation},
  author = {Guillaume Vailland and Valérie Gouranton and Marie Babel},
  booktitle = {ICRA 2021},
  year = {2021}
}