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 …

Motion planning is a fundamental research area in robotics. Sampling-based methods offer
an efficient solution for what is otherwise a rather challenging dilemma of path planning …

We present PRM-RL, a hierarchical method for long-range navigation task completion that
combines sampling-based path planning with reinforcement learning (RL). The RL agents …

Long-range indoor navigation requires guiding robots with noisy sensors and controls
through cluttered environments along paths that span a variety of buildings. We achieve this …

Large-scale conformational changes are essential to link protein structures with their
function at the cell and organism scale, but have been elusive both experimentally and …

The use of mobile robots is growing every day. Path planning algorithms are needed to
allow the coordination of several robots, and make them travel with the least cost and …

Motion planning is still an open problem for many disciplines, eg, robotics, autonomous
driving, due to their need for high computational resources that hinder real-time, efficient …

Sampling efficiency in a highly constrained environment has long been a major challenge
for sampling-based planners. In this work, we propose Rapidly-exploring Random disjointed …

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

Sampling-based planning is the predominant paradigm for motion planning in robotics. Most
sampling-based planners use a global random sampling scheme to guarantee probabilistic …