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 …

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 …

This paper presents a novel approach to real-time obstacle avoidance based on Dynamical
Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed …

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 …

In this article, we present a novel nonuniform sampling technique, based on the pipeline of
rapidly exploring random tree (RRT), for efficiently computing high-quality collision-free …

This paper describes a motion planning algorithm for a quadrotor helicopter flying
autonomously without GPS. Without accurate global positioning, the vehicle's ability to …

We present an anytime algorithm for planning paths through high-dimensional, non-uniform
cost search spaces. Our approach works by generating a series of rapidly-exploring random …

We present a new motion planning framework that explicitly considers uncertainty in robot
motion to maximize the probability of avoiding collisions and successfully reaching a goal. In …

This article demonstrates that collision detection-intensive applications such as robotic
motion planning may be accelerated by performing collision checks with a machine learning …