Motion Planning for Car-like Robots using a Probabilistic Learning Approach (1995)
| Venue: | Int. J. of Robotics Research |
| Citations: | 16 - 3 self |
BibTeX
@ARTICLE{Svestka95motionplanning,
author = {Petr Svestka and Mark H. Overmars and Mark H. Overmars and Mark H. Overmars},
title = {Motion Planning for Car-like Robots using a Probabilistic Learning Approach},
journal = {Int. J. of Robotics Research},
year = {1995},
volume = {16}
}
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Abstract
In this paper a recently developed learning approach for robot motion planning is extended and applied to two types of car-like robots: normal ones and robots which can only move forwards. In this learning approach the motion planning process is split into two phases: the learning phase and the query phase. In the learning phase a probabilistic roadmap is incrementally constructed in configuration space. This roadmap is an undirected graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collision-free paths between the nodes. These simple motions are computed using a fast local method. In the query phase this roadmap can be used to find paths between different pairs of configurations. The approach can be applied to normal car-like robots (with nonholonomic constraints) by using suitable local methods, which compute paths feasible for the robots. Application to car-like robots which can move only forwards demands a more fundamental a...
