This repository implements and analytically evaluates two foundational probabilistic sample-based motion planning algorithms: RRT (Rapidly-exploring Random Tree) and its optimizing successor, RRT*.

Path Planning: It's based on path constraints (such as obstacles), planning the optimal path sequence for the robot to travel without conflict between the start and goal. Trajectory planning: It plans ...

Abstract: Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial ...

In a study published in Robot Learning journal, researchers propose a new learning-based path planning framework that allows mobile robots to navigate safely and efficiently using a Transformer model.

Abstract: Real-time autonomous navigation in an urban setting demands efficient perception and path planning. Conventional software-centric methods are unable to satisfy real-time and low-power ...