Nancy M. Amato

TL;DR: This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.

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.

TL;DR: A new method of sampling the configuration space in which randomly generated configurations are retracted onto the medial axis of the free space is proposed, and it is shown that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it.

TL;DR: FIRM is introduced as an abstract framework, a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods and the so-called SLQG-FIRM, a concrete instantiation of FIRM that focuses on kinematic systems and then extends to dynamical systems by sampling in the equilibrium space.

TL;DR: A new local planning method is proposed, called rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments and includes recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods.