Path Planning for Manipulation Using Experience-Driven Random Trees

Robotic Path Planning using Rapidly exploring Random Trees

Rapidly exploring Random Tree (RRT) path planning methods provide feasible paths between a start and goal point in configuration spaces containing obstacles, sacrificing optimality (eg. Shortest path) for speed. The raw resultant paths are generally jagged and the cost of extending the tree can increase steeply as the number of existing branches grow. This paper provides details of a speed-up m...

Rapidly-Exploring Random Trees: A New Tool for Path Planning

Using random sampling trees for automated planning

Rapidly-exploring random trees (RRTs) are data structures and search algorithms designed to be used in continuous path planning problems. They are one of the most successful state-of-the-art techniques in motion planning, as they offer a great degree of flexibility and reliability. However, their use in other fields in which search is a commonly used approach has not been thoroughly analyzed. I...

Decentralized Path Planning for Multiple Agents in Complex Environments using Rapidly-exploring Random Trees

This thesis presents a novel approach to address the challenge of planning paths for real-world multi-agent systems operating in complex environments. The technique developed, the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMARRT) algorithm, is an extension of the CL-RRT algorithm to the multi-agent case, retaining its ability to plan quickly even with complex constraints. Moreove...

Fast Manipulation Path Planning for Humanoid Robots

|We present an interactive manipulation path planner under development for providing high-level task-based control for humanoid robots. Using a graphical simulation environment, the software is designed to facilitate the automation of object grasping and manipulation tasks. A randomized path planner is used to directly search the free con guration space of the robot for a collision-free manipul...