Motion planning is a fundamental problem in autonomous robotics that requires finding a
path to a specified goal that avoids obstacles and takes into account a robot's limitations and …

Optimal motion planning becomes more and more important these days and is critical for
motion planning algorithms developed in the academia to be applicable to real‐world …

Sampling-based algorithms are viewed as practical solutions for high-dimensional motion
planning. Recent progress has taken advantage of random geometric graph theory to show …

We present lower bound tree-RRT (LBT-RRT), a single-query sampling-based motion-
planning algorithm that is asymptotically near-optimal. Namely, the solution extracted from …

We describe a process that constructs robot-specific circuitry for motion planning, capable of
generating motion plans approximately three orders of magnitude faster than existing …

This work describes STABLE SPARSE RRT (SST), an algorithm that (a) provably provides
asymptotic (near-) optimality for kinodynamic planning without access to a steering …

Many path-finding algorithms on graphs such as A* are sped up by using a heuristic function
that gives lower bounds on the cost to reach the goal. Aiming to apply similar techniques to …

The goal of this article is to highlight the benefits of cloud automation for industrial adopters
and some of the research challenges that must be addressed in this process. The focus is on …

Roadmaps constructed by the recently introduced PRM* algorithm for asymptotically-optimal
motion planning encode high-quality paths yet can be extremely dense. We consider the …

This paper introduces a fast weighted streaming spanner algorithm (WSS) that trims edges
from roadmaps generated by robot motion planning algorithms such as Probabilistic …