Recent years have seen a dramatic rise in the popularity of autonomous mobile robots
(AMRs) due to their practicality and potential uses in the modern world. Path planning is …

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 …

Rapidly-exploring random trees (RRTs) are popular in motion planning because they find
solutions efficiently to single-query problems. Optimal RRTs (RRT* s) extend RRTs to the …

Path planning is one of the most essential part in autonomous navigation. Most existing
works suppose that the environment is static and fixed. However, path planning is widely …

Conventional visual bridge inspection methods are time consuming and may put the
inspector in dangerous situations. Unmanned Aerial Vehicles (UAVs) have been recently …

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 …

Optimal path planning refers to find the collision free, shortest, and smooth route between
start and goal positions. This task is essential in many robotic applications such as …

Sampling based planning algorithm such as RRT and RRT* are extensively used in recent
years for path planning of mobile robots. They are probabilistic complete algorithms and …

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 …

Abstract Rapidly-exploring Random Tree Star (RRT*) algorithm and its variants based on
random sampling can provide a collision-free and asymptotic optimal solution for many path …