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 …

Path planning is one of the most important process on applications such as navigating
autonomous vehicles, computer graphics, game development, robotics, and protein folding …

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 …

As a variant of rapidly exploring random tree (RRT), RRT* is an important improvement of
sampling-based algorithms. Although it can provide a feasible planning solution with a …

Optimal path planning is the problem of finding a valid sequence of states between a start
and goal that optimizes an objective. Informed path planning algorithms order their search …

Sampling-based motion planners have experienced much success due to their ability to
efficiently and evenly explore the state space. However, for many tasks, it may be more …

The efficiency of sampling-based motion planning brings wide application in autonomous
mobile robots. The conventional rapidly exploring random tree (RRT) algorithm and its …

The inspection-planning problem calls for computing motions for a robot that allow it to
inspect a set of points of interest (POIs) while considering plan quality (eg, plan length). This …

Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its
variants, have been used extensively for motion planning. Control barrier functions (CBFs) …

Sampling-based motion planning algorithms such as RRT* are well-known for their ability to
quickly find an initial solution and then converge to the optimal solution asymptotically as the …