A multi-robot system for the detection of explosive devices
<|reference_start|>A multi-robot system for the detection of explosive devices: In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful consideration of multiple aspects related to the perception, mobility, and collaboration capabilities of the system. In the framework of a European challenge, the Artificial Intelligence for Detection of Explosive Devices - eXtended (AIDEDeX) project proposes to design a heterogeneous multi-robot system with advanced sensor fusion algorithms. This system is specifically designed to detect and classify improvised explosive devices, explosive ordnances, and landmines. This project integrates specialised sensors, including electromagnetic induction, ground penetrating radar, X-Ray backscatter imaging, Raman spectrometers, and multimodal cameras, to achieve comprehensive threat identification and localisation. The proposed system comprises a fleet of unmanned ground vehicles and unmanned aerial vehicles. This article details the operational phases of the AIDEDeX system, from rapid terrain exploration using unmanned aerial vehicles to specialised detection and classification by unmanned ground vehicles equipped with a robotic manipulator. Initially focusing on a centralised approach, the project will also explore the potential of a decentralised control architecture, taking inspiration from swarm robotics to provide a robust, adaptable, and scalable solution for explosive detection.<|reference_end|>
@article{hasselmann2024a,
    title={A multi-robot system for the detection of explosive devices},
    author={Ken Hasselmann, Mario Malizia, Rafael Caballero, Fabio Polisano,
  Shashank Govindaraj, Jakob Stigler, Oleksii Ilchenko, Milan Bajic, Geert De
  Cubber},
    journal={IEEE ICRA Workshop on Field Robotics 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14167},
    primaryClass={cs.RO}
}