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One of the fundamental tasks robots have to perform is planning their motions while avoiding collisions with obstacles in the environment. This is the central topic of the thesis. We restrict ourselves to motion planning for two- and three-dimensional rigid bodies and articulated robots moving in static and known virtual environments.

List of publications

Creating Small Roadmaps for Solving Motion Planning Problems.

Creating Small Roadmaps for Solving Motion Planning Problems.

Ph.D. thesis

The thesis deals with comparing and analyzing sampling-based motion planning techniques, in particular variants of the Probabilistic Roadmap Method (PRM). In addition, quality aspects of paths and roadmaps are investigated.

Ph.D. thesis of Roland Geraerts.

Ph.D. thesis of Roland Geraerts.

Comparative studies and analyses

We conduct a comparative study of different techniques that are used in the Probabilistic Roadmap Method. In addition, we give a reachability analysis for sampling based planners which leads to a better understanding of the success of the planners.

Different sampling strategies and a 3D reachability region.

High-quality paths

We present algorithms that increase the quality of a path. That is, we focus on decreasing the path length and increasing the clearance along a path. The techniques can be applied to a broad range of robots which may reside in arbitrary high-dimensional configuration spaces.

Techniques that improve the path clearance and path length.

High-quality roadmaps

The Reachability Roadmap Method is a new and efficient algorithm that creates small roadmaps for two- and three-dimensional problems. The algorithm ensures that a path can always be found if one exists. We extend the algorithm such that short paths and high-clearance paths can be extracted from the roadmap in real-time.

Techniques that improve the path clearance of a roadmap graph.

Experimental research

In robotics research, it is often difficult to compare and evaluate techniques experimentally. We identify these difficulties and provides solutions based on our work during the last four years in the field of sampling-based motion planning.


We study techniques for combining path planning with motion synthesis.

Combining Path Planners and Motion Graphs