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 …

Anytime almost-surely asymptotically optimal planners, such as RRT*, incrementally find
paths to every state in the search domain. This is inefficient once an initial solution is found …

Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the
rapidly exploring random tree (RRT) algorithms, represent one of the most successful …

Robotics is concerned with the generation of computer-controlled motions of physical
objects in a wide variety of settings. Because physical objects define spatial distributions in 3 …

Discovering high-quality paths for multirobot problems can be achieved, in principle, by
exploring the composite space of all robots. For instance, sampling-based algorithms that …

We develop a new analysis of sampling-based motion planning in Euclidean space with
uniform random sampling, which significantly improves upon the celebrated result of …

We study fundamental theoretical aspects of probabilistic roadmaps (PRM) in the finite time
(non-asymptotic) regime. In particular, we investigate how completeness and optimality …

This paper aims to find a reliable, collision-free path in a dynamic environment for highly
maneuverable unmanned combat air vehicles (UCAVs). Given the real-time nature of the …

We present a general approach for constructing proofs of motion planning infeasibility.
Effective high-dimensional motion planners, such as sampling-based methods, are limited to …

Navigating uncontrolled dynamic environments is a major challenge in robotics. Success
requires solving many different technical problems and path planning is a key component of …