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 …

Path planning is a very important step for mobile smart vehicles in complex environments.
Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been …

To efficiently solve challenges related to motion-planning problems with dynamics, this
paper proposes treating motion planning not just as a search problem in a continuous space …

We present a novel optimization-based retraction algorithm to improve the performance of
sample-based planners in narrow passages for 3D rigid robots. The retraction step is …

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 …

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 …

We present the exploring/exploiting tree (EET) algorithm for motion planning. The EET
planner deliberately trades probabilistic completeness for computational efficiency. This …

Current state-of-the-art motion planners rely on samplingbased planning to explore the
problem space for a solution. However, sampling valid configurations in narrow or cluttered …

In this paper we describe a new approach to sampling-based motion planning with
Probabilistic Roadmap Planner (PRM) methods. Our aim is to compute good quality …

This paper presents the Discrete Search Lead-ing continuous eXploration (DSLX) planner, a
multi-resolution approach to motion planning that is suitable for challenging problems …