Sampling-based motion planning is one of the fundamental paradigms to generate robot
motions, and a cornerstone of robotics research. This comparative review provides an up-to …

A defining feature of sampling-based motion planning is the reliance on an implicit
representation of the state space, which is enabled by a set of probing samples …

This paper presents some of the recent improvements in sampling-based robot motion
planning. Emphasis is placed on work that brings motion-planning algorithms closer to …

Why are probabilistic roadmap (PRM) planners “probabilistic”? This paper tries to establish
the probabilistic foundations of PRM planning and reexamines previous work in this context …

Sampling-based motion planning techniques have emerged as an efficient algorithmic
paradigm for solving complex motion planning problems. These approaches use a set of …

This paper is concerned with motion planning for non-linear robotic systems operating in
constrained environments. A method for computing high-quality trajectories is proposed …

This paper presents a kinodynamic motion planner, ie, Kinodynamic Motion Planning by
Interior-Exterior Cell Exploration (KPIECE), which is specifically designed for systems with …

We consider the problem of leveraging prior experience to generate roadmaps in sampling-
based motion planning. A desirable roadmap is one that is sparse, allowing for fast search …

The probabilistic roadmap (PRM) planner is a popular method for robot motion planning
problems with many degrees of freedom. However, it has been shown that the method …

Earlier work has shown that reusing experience from prior motion planning problems can
improve the efficiency of similar, future motion planning queries. However, for robots with …