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 …

During the last decade, sampling-based algorithms for path planning have gained
considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of …

Abstract Rapidly-exploring Random Tree star (RRT*) has recently gained immense
popularity in the motion planning community as it provides a probabilistically complete and …

Rapidly Exploring Random Tree (RRT) is one of the quickest and the most efficient obstacle
free path finding algorithm. However, it cannot guarantee finding the most optimal path. A …

Abstract Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of
Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free …

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 …

The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees
(RRT) has gained the attention of many researchers due to their computational efficiency …

This study presents a new perspective for autonomous mobile robots path searching by
proposing a biasing direction towards causal entropy maximisation during random tree …

We present lower bound tree-RRT (LBT-RRT), a single-query sampling-based motion-
planning algorithm that is asymptotically near-optimal. Namely, the solution extracted from …

Path planning in robotics often requires finding high-quality solutions to continuously valued
and/or high-dimensional problems. These problems are challenging and most planning …