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 …

Self-driving vehicles are a maturing technology with the potential to reshape mobility by
enhancing the safety, accessibility, efficiency, and convenience of automotive transportation …

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 …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

From quadrotors delivering packages in urban areas to robot arms moving in confined
warehouses, motion planning around obstacles is a core challenge in modern robotics …

In this article, we present an optimization-based framework for multicopter trajectory
planning subject to geometrical configuration constraints and user-defined dynamic …

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 article describes motion planning networks (MPNet), a computationally efficient,
learning-based neural planner for solving motion planning problems. MPNet uses neural …

In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on
unifying graph-and sampling-based planning techniques. By recognizing that a set of …

Abstract The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in
motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which …