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Open AccessProceedings Article

A probabilistic learning approach to motion planning

Mark H. Overmars, +1 more
- pp 19-37
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This article is published in Workshop on the Algorithmic Foundations of Robotics.The article was published on 1995-05-12 and is currently open access. It has received 160 citations till now. The article focuses on the topics: Probabilistic logic & Motion planning.

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Journal ArticleDOI

Probabilistic roadmaps for path planning in high-dimensional configuration spaces

Lydia E. Kavraki, +3 more
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Book

Principles of Robot Motion: Theory, Algorithms, and Implementations

Howie Choset, +1 more
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Proceedings ArticleDOI

Real-time motion planning for agile autonomous vehicles

Emilio Frazzoli, +2 more
TL;DR: This paper proposes a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles that addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning.
Proceedings ArticleDOI

Analysis of probabilistic roadmaps for path planning

Lydia E. Kavraki, +2 more
TL;DR: This work provides an analysis of a path planning method which uses probabilistic roadmaps and provides estimates for N, the principal parameter of the method, in order to achieve failure probability within prescribed bounds.
Proceedings ArticleDOI

A randomized roadmap method for path and manipulation planning

Nancy M. Amato, +1 more
TL;DR: A new randomized roadmap method for motion planning for many DOF robots that can be used to obtain high quality roadmaps even when C-space is crowded, with the main novelty in the authors' approach is that roadmap candidate points are chosen on C-obstacle surfaces.