Authors:
Sofiane Ahmed Ali
;
Eric Vasselin
and
Alain Faure
Affiliation:
GREAH, Le Havre University, France
Keyword(s):
Probabilistic path planner, probabilistic roadmap, Robot Path Planning, Randomized Algorithms, Random
sampling.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Modeling, Simulation and Architectures
;
Robotics and Automation
Abstract:
The probabilistic path planner (PPP) is a general planning scheme that yields fast robot path planners for a wide variety of problems, involving high degree of freedom articulated robots, non holonomic robots, and multiple robots. This paper presents a new probabilistic approach for finding paths through narrow
passages. Our probabilistic planner follows the general framework of probabilistic roadmap (PRM), but to
increase sample density in difficult areas like narrow passages, we define two sampling constraints in order
to get much more points than a classic PRM gets in such areas. We simulate our planner in 2D environments and the simulations results shows good performance for our planner.