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 …

Automatic path planning problem is essential for efficient mission execution by unmanned
aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated …

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic
optimization. However, the RRT* algorithm still suffers from slow convergence rate and large …

Q-learning, a type of reinforcement learning, has gained increasing popularity in
autonomous mobile robot path planning recently, due to its self-learning ability without …

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

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

Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics
applications such as self-driving cars. Existing motion planning methods become ineffective …

The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the
motion planning algorithms for mobile robots. However, the trap space problem, such as …

When mobile robot used in large-scale dynamic environments, it face more challenging
problems in real-time path planning and collision-free path tracking. This paper presents a …

In recent decades, Rapidly-exploring Random Tree star (RRT*) with asymptotic optimality
has attracted much attention in path planning algorithm, but it suffers from slow …