Ken Hasselmann – Robotics & Autonomous Systems

Senior Researcher

Robotics & Autonomous Systems,
Royal Military Academy

Address

Avenue De La Renaissance 30, 1000 Brussels, Belgium

Contact Information

Ken Hasselmann is currently a senior researcher in the Robotics & Autonomous Systems unit of the Department of Mechanics at the Belgian Royal Military Academy, working on the European Defence Fund's AIDEDex project.

He obtained his Master's degree in engineering in electronics and embedded systems from the National Polytechnic Institute of Toulouse (INP-ENSEEIHT) and a Master's in strategic management and innovation from Toulouse School of Management (TSM) in 2014.

He obtained his PhD in swarm robotics from IRIDIA, the artificial intelligence lab of the Université libre de Bruxelles (ULB), in 2023, while working on the ERC DEMIURGE project, aiming to advance the state-of-the-art in automatic design methods for robot swarms. His research focuses on swarm robotics, machine learning, automatic design, optimization algorithms, and the application and design of such systems for real-world scenarios.

In addition to his research, Ken has significant teaching experience. Before joining the RMA, he was a teacher-researcher at ECAM Brussels Engineering School, responsible for courses in algorithms, machine learning, and deep learning in the Department of Computer Science and Electronics. During his PhD, he served as a teaching assistant at ULB, covering a wide range of subjects from digital and analog electronics to microprocessor architecture and artificial intelligence.

He is passionate about computer science, embedded systems, and artificial intelligence, strongly believes in Free, Libre, and Open-Source Software, and is dedicated to developing new projects with this philosophy.

Publications

2025

E. Maroulis, D. Hawari, K. Hasselmann, E. {Le Flécher}, and G. {De cubber}, “Experimental Evaluation of Roadmap-Based Map Generation with Continuous-Time Conflict-Based Search for Multi-Agent Pathfinding," IEEE International Conference on Autonomous Robots and Agents, ICARA, p. 380–387, 2025.
[BibTeX] [Abstract] [Download PDF] [DOI]

This article presents an experimental evaluation of a Multi-Agent Pathfinding (MAPF) approach for large-scale robotic fleets operating in diverse outdoor environments. We generated three distinct types of roadmap graphs: Constrained Delaunay Triangulation (CDT), Voronoi Diagram (VD), and Grid-derived from an obstacle file, and assessed their quality using metrics obtained from graph theory. Then, the performance of the Continuous-time Conflict-Based Search (CCBS) algorithm was evaluated across three different environmental maps, considering practical performance metrics including makespan and failure rate. Subsequently, the roadmap generation methods were ranked based on CCBS performance in similar scenarios using the Friedman statistical test. The results indicate that CDT outperforms both VD and Grid maps, even though it does not exhibit the best graph metrics in many environments. CDT’s superior performance is attributed to its enhanced interconnectedness and the availability of multiple pathways, as evidenced by its balanced metrics and structural properties. We show that CDT is the most efficient and reliable roadmap generation technique for multiagent systems under our experimental conditions making it a preferred choice for robust pathfinding.

@article{34774d01cc3341398188fc8353028be2,
title = "Experimental Evaluation of Roadmap-Based Map Generation with Continuous-Time Conflict-Based Search for Multi-Agent Pathfinding",
abstract = "This article presents an experimental evaluation of a Multi-Agent Pathfinding (MAPF) approach for large-scale robotic fleets operating in diverse outdoor environments. We generated three distinct types of roadmap graphs: Constrained Delaunay Triangulation (CDT), Voronoi Diagram (VD), and Grid-derived from an obstacle file, and assessed their quality using metrics obtained from graph theory. Then, the performance of the Continuous-time Conflict-Based Search (CCBS) algorithm was evaluated across three different environmental maps, considering practical performance metrics including makespan and failure rate. Subsequently, the roadmap generation methods were ranked based on CCBS performance in similar scenarios using the Friedman statistical test. The results indicate that CDT outperforms both VD and Grid maps, even though it does not exhibit the best graph metrics in many environments. CDT's superior performance is attributed to its enhanced interconnectedness and the availability of multiple pathways, as evidenced by its balanced metrics and structural properties. We show that CDT is the most efficient and reliable roadmap generation technique for multiagent systems under our experimental conditions making it a preferred choice for robust pathfinding.",
keywords = "Measurement , Automation , Reliability theory , Graph theory , Path planning , Robots , Multi-agent systems",
author = "Emmanouil Maroulis and Danial Hawari and Ken Hasselmann and {Le Fl{\'e}cher}, Emile and {De cubber}, Geert",
year = "2025",
month = may,
day = "5",
doi = "10.1109/ICARA64554.2025.10977707",
language = "English",
pages = "380--387",
journal = "IEEE International Conference on Autonomous Robots and Agents, ICARA",
issn = "2767-7745",
url = "https://ieeexplore.ieee.org/document/10977707",
unit= {meca-ras},
project= {CUGS, ANIMUS, AIDEDEX, CONVOY}
}

2024

M. Kegeleirs, {. G. Ramos, {. L. Herranz, I. Gharbi, J. Szpirer, K. Hasselmann, L. Garattoni, G. Francesca, and M. Birattari, “Leveraging swarm capabilities to assist other systems," in Breaking swarm stereotypes workshop at ICRA 2024, 2024.
[BibTeX]

@inproceedings{462910db3c904e8f8c9e758346727cdf,
title = "Leveraging swarm capabilities to assist other systems",
author = "Miquel Kegeleirs and Ramos, {David Garz{\'o}n} and Herranz, {Guillermo Legarda} and Ilyes Gharbi and Jeanne Szpirer and Ken Hasselmann and Lorenzo Garattoni and Gianpiero Francesca and Mauro Birattari",
year = "2024",
booktitle = "Breaking swarm stereotypes workshop at ICRA 2024",
}