Researcher
Robotics & Autonomous Systems,
Royal Military Academy
Address
Avenue De La Renaissance 30, 1000 Brussels, Belgium
Contact Information
Call: –
Email: Alexandre.LaGrappe@mil.be
Alexandre is a robotics researcher at the Robotics & Autonomous Systems unit of the Department of Mechanics of the Belgian Royal Military Academy. His research focuses on developing solutions for heterogeneous robot fleet management in tough environments.
He received his Master’s Degree in Mechatronics Engineering from the Vrije Universiteit Brussel and the Université Libre de Bruxelles in 2019. He then started working as a scientific project leader for the Interuniversity Microelectronics Centre (IMEC). He coordinated research on novel semiconductor manufacturing processes using dry film photoresist.
In 2021, Alexandre joined the Belgian Royal Military Academy to participate in the iMUGS project that aims to develop and deploy a modular, standardized, and open system architecture for manned-unmanned team of robots to support armed forces on the field.
Publications
2025
- A. La Grappe, E. Le Flécher, and G. De Cubber, “Multi-Sensor Multi-Target Tracking for Maritime Surveillance with Autonomous Surface Vehicles Using Belief Propagation," in OCEANS 2025 Brest, 2025, p. 1–8.
[BibTeX] [Abstract] [Download PDF] [DOI]
We present a distributed multi-sensor multi-target tracking algorithm for maritime surveillance using unmanned surface vehicles (USVs) in multi-agent settings. Our approach fuses measurements from radar, Automatic Identification System (AIS), and camera sensors within a Bayesian framework, employing an adaptive particle filtering strategy to jointly estimate the kinematic states and identities of vessels. Our solution incorporates a factorized data association model that integrates cooperative self-reports from AIS with radar and camera measurements, with visual re-identification capability. We evaluate our method using a high-fidelity simulation environment, which generates photorealistic maritime scenarios. Our performance analysis indicates that the integration of camera-based cues improves both the spatial localization and identity consistency, particularly in scenarios with low radar detection probability and non-cooperative targets. Furthermore, the distributed inference framework scales well with the number of USVs, making it well suited for large-scale multi-agent applications. Overall, our work demonstrates that fusing heterogeneous sensor modalities using belief propagation can enhance multi-target tracking performance in congested maritime environments.
@inproceedings{2c491cd7d3c64ae3ae227fdab6a2c80f, title = "Multi-Sensor Multi-Target Tracking for Maritime Surveillance with Autonomous Surface Vehicles Using Belief Propagation", abstract = "We present a distributed multi-sensor multi-target tracking algorithm for maritime surveillance using unmanned surface vehicles (USVs) in multi-agent settings. Our approach fuses measurements from radar, Automatic Identification System (AIS), and camera sensors within a Bayesian framework, employing an adaptive particle filtering strategy to jointly estimate the kinematic states and identities of vessels. Our solution incorporates a factorized data association model that integrates cooperative self-reports from AIS with radar and camera measurements, with visual re-identification capability. We evaluate our method using a high-fidelity simulation environment, which generates photorealistic maritime scenarios. Our performance analysis indicates that the integration of camera-based cues improves both the spatial localization and identity consistency, particularly in scenarios with low radar detection probability and non-cooperative targets. Furthermore, the distributed inference framework scales well with the number of USVs, making it well suited for large-scale multi-agent applications. Overall, our work demonstrates that fusing heterogeneous sensor modalities using belief propagation can enhance multi-target tracking performance in congested maritime environments.", keywords = "Visualization, Target tracking, Radar measurements, Surveillance, Radar, Radar tracking, Cameras, Particle measurements, Sensors, Artificial intelligence, Multi-target tracking, Maritime surveillance, Unmanned Surface Vessels, Distributed sensor fusion, Particle filtering, Belief propagation, Multi-agent robotics", author = "La Grappe, Alexandre and Le Flécher, Emile and De Cubber, Geert", year = "2025", month = jun, day = "19", doi = "10.1109/OCEANS58557.2025.11104349", language = "English", pages = "1--8", booktitle = "OCEANS 2025 Brest", publisher = "Institute of Electrical and Electronics Engineers Inc.", url = "https://ieeexplore.ieee.org/document/11104349", unit= meca-ras, project= MULTIMETER }
2023
- G. De Cubber, E. Le Flécher, A. La Grappe, E. Ghisoni, E. Maroulis, P. Ouendo, D. Hawari, and D. Doroftei, “Dual Use Security Robotics: A Demining, Resupply and Reconnaissance Use Case," in IEEE International Conference on Safety, Security, and Rescue Robotics, 2023.
[BibTeX] [Download PDF]@inproceedings{ssrr2023decubber, title={Dual Use Security Robotics: A Demining, Resupply and Reconnaissance Use Case}, author={De Cubber, Geert and Le Flécher, Emile and La Grappe, Alexandre and Ghisoni, Enzo and Maroulis, Emmanouil and Ouendo, Pierre-Edouard and Hawari, Danial and Doroftei, Daniela}, booktitle={IEEE International Conference on Safety, Security, and Rescue Robotics}, editors ={Kimura, Tetsuya}, publisher = {IEEE}, year = {2023}, vol = {1}, project = {AIDED, iMUGs, CUGS}, location = {Fukushima, Japan}, unit= {meca-ras}, doi = {}, url={https://mecatron.rma.ac.be/pub/2023/SSRR2023-DeCubber.pdf} }
2022
- E. Le Flécher, A. La Grappe, and G. De Cubber, “iMUGS – A ground multi-robot architecture for military Manned-Unmanned Teaming," in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2022.
[BibTeX]@inbook{imugs_le_flecher_la_grappe_de_cubber, place={Kyoto}, title={iMUGS - A ground multi-robot architecture for military Manned-Unmanned Teaming}, booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, publisher={IEEE}, year={2022}, author={Le Flécher, Emile and La Grappe, Alexandre and De Cubber, Geert}, project = {iMUGs}, unit= {meca-ras} }