Multi-Goal Path Planning Using Multiple 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...

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...

Multi-Directional Search with Goal Switching for Robot Path Planning

We present a parallel path planning method that is able to automatically handle multiple goal configurations as input. There are two basic approaches, goal switching and bidirectional search, which are combined in the end. Goal switching dynamically selects a favourite goal depending on some distance function. The bi-directional search supports the backward search direction from the goal to the...

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...