Research

From 2016 to 2021 I worked on path planning for robot arms, at the KU Leuven research group ACRO. In recent years we’ve seen an increased demand for flexible production. By improving industrial robot motion planning algorithms and focussing on open-source development, I hope that small companies can stay competitive. My specific focus is on Cartesian path planning for applications such as robot welding, deburring, and painting.

From 2014 to 2016 I worked in the beautiful city in Belgium, Ghent, on a project to describe and evaluate the quality of electric bicycles. Electric bicycles an important part of the solution to create livable cities. But when companies or government organizations wanted to buy large quantities of bicycles, they need a systematic way to describe them. I created a simple website for the project, but it turns out the official domain does not exist anymore, so I put it on a Github page. The official description and partners of the project can be found here.

Publications

Sampling-based Tube Following for Redundant, Planar Robotic Manipulators

Jeroen De Maeyer, Mark Versteyhe, Eric Demeester

This paper introduces a global, sampling-based motion planning approach to the tube following problem for redundant robot manipulators. Tube following is a motion planning problem where an under-defined end-effector path is given. We introduce a novel combination of existing task space and redundant configuration space sampling techniques. These techniques are applied to the tube following problem, comparing three different sampling techniques: uniform grid, uniform random and Halton sampling. In addition, an incremental sampling technique is proposed, specifically for tube following problems. Eventually, iterative grid refining is used to locally optimise path cost. Experimental results demonstrate the clear potential of these techniques to achieve fully automatic, collision-free motion planning for tube following with redundant robot manipulators. Our planning software is publicly available.

Cartesian path planning for arc welding robots: Evaluation of the descartes algorithm

Jeroen De Maeyer, Bart Moyaers, Eric Demeester

Many industrial robot applications require fewer task constraints than the robot’s degrees of freedom. For welding robots, for example, rotations of the welding torch around its axis do not negatively impact welding quality. Furthermore, the tool center point’s Cartesian position and desired orientation as a function of time is often determined by the (manufacturing) process. Nevertheless, programming these robots can be time consuming. Reducing or eliminating this programming cost will allow robots to be used for producing small series. Recently, a promising software package for Cartesian path planning with the name Descartes was released by the ROS-Industrial community. To the authors’ knowledge, an in-depth description of this algorithm and an experimental evaluation is lacking in literature. This paper describes the path planning approach used by the Descartes package. Moreover, the software’s performance is evaluated for several key robot welding tasks and the encountered limitations are discussed. In addition, we show that the planner’s performance can be improved by changing the cost function that the planner’s graph search algorithm minimises.

The TGVelo Project: New Quality System for Electric Bicycles

D Callebaut, J De Maeyer, B Rotthier, J Cappelle, E Motoasca

A system to evaluate and score different electric bicycles in an objective way. I presented this at the Scientist for cycling colloquium. This was a small conference filled with interesting people the day before Velo-city 2016 in Taipei.