• Corpus ID: 64152832

Robotic path planning using rapidly-exploring random trees

  title={Robotic path planning using rapidly-exploring random trees},
  author={Fahad Sherwani},
This study concerns the implementation of Rapidly-Exploring Random Trees (RRTs) algorithm for an autonomous robot path planning. RRTs possesses a number of advantages such as relatively simple, suitable for finding a path for a robot with dynamic and physical constraints, the expansion of RRT is heavily biased toward unexplored areas of search space and the number of edges is minimal. However, the planned path by using basic RRT structure might not always be optimal in terms of path length… 

Figures from this paper


Cost based planning with RRT in outdoor environments
The Metric Adaptive RRT (MA-RRT), which integrates planning and fast execution for generating paths over a cost map and can improve upon the quality of the path returned when cost is considered, is presented.
Path Planning Based on Fuzzy Rolling Rapidly-exploring Random Tree for Mobile Robot
The rapidly-exploring random tree(RRT) algorithm is combined with the rolling path planning in the planning, and a novel path planning is proposed, which avoids local minima and enhances the capability of searching an unknown space.
Improved Path Planning Based on Rapidly-Exploring Random Tree for Mobile Robot in Unknown Environment
An improved path planning algorithm is proposed by combining rapidly-exploring random tree (RRT) and rolling path planning and the heuristic evaluation function is introduced into the improved algorithm, so that the exploring random tree can grow in the direction of target point.
A suboptimal path planning algorithm using rapidly-exploring random trees
This paper presents path planning algorithms using Rapidly-exploring Random Trees (RRTs) to generate paths for unmanned air vehicles (UAVs) in real time, given a starting location and a goal location
An obstacle-based rapidly-exploring random tree
A variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively and shows that both workspace obstacle information and C-space information can be used when deciding which direction to grow.
Randomised Rough-Terrain Robot Motion Planning
  • Alan Ettlin, H. Bleuler
  • Computer Science
    2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2006
This work proposes a motion planner based on rapidly exploring random trees (RRTs) which takes into consideration the characteristics of the underground and demonstrates how this concept can be adapted to other domains where a characteristic measure for the desirability of attaining individual configurations can be defined.
A PRM-based motion planner for dynamically changing environments
  • L. Jaillet, T. Siméon
  • Computer Science
    2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)
  • 2004
Simulation results show that this combination of techniques yields to efficient global planner capable of solving with a real-time performance problems in geometrically complex environments with moving obstacles.
RRT-connect: An efficient approach to single-query path planning
  • J. Kuffner, S. LaValle
  • Computer Science
    Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)
  • 2000
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
A method that applies Kalman smoothing to make paths Ck-continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra’s algorithm to efficiently find high-quality paths is presented.
Rapidly-exploring random trees : a new tool for path planning
We introduce the concept of a Rapidly-exploring Random Tree (RRT) as a randomized data structure that is designed for a broad class of path planning problems. While they share many of the bene cial