Master Thesis

Parallel AtlasRRT

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  • If you are interested in the proposal, please contact with the supervisors.


Modern computers are typically equipped with many computing cores. This offers the possibility of significantly accelerating algorithms originally developed for single-CPU machines. The Rapidly-exploring Random Tree (RRT) path planner is one of such algorithms. In the past few years there has been a interest in parallelising the RRTs construction (see [1],[2], or [3], for instance). The CuikSuite implements the AtlasRRT, a RRT specially designed for systems with closed kinematic chains that, up to now, does not exploit parallelism.


The objective of this project is to parallelise the AtlasRRT algorithm.


This project encompasses the following steps:

  • Review the literature to analyse the existing parallel RRT approaches.
  • Propose a parallel version of the AtlasRRT, exploiting the particular properties of this algorithm.
  • Implement the proposed parallel AtlasRRT.
  • Implement some of the existing parallel RRTs (adapted to the closed-loop case) for comparison purposes.
  • Test the new algorithms on the examples included in the CuikSuite.


This project is ideal for master students with

  • Some notions on path planning.
  • Good programming skills in C/C++

The project can also be developed as a "Directed Research Work" (18 credits master).

The work is under the scope of the following projects:

  • CUIK++: An Extension of Branch-and-Prune Techniques for Motion Analysis and Synthesis of Complex Robotic Systems (web)