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ASAP-MO:Advanced Situational Awareness and Perception for Mission-critical Operations

2 May 2025
Veronica Vannini
William Dubois
Olivier Gamache
Jean-Michel Fortin
Nicolas Samson
Effie Daum
F. Pomerleau
Edith Brotherton
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Abstract

Deploying robotic missions can be challenging due to the complexity of controlling robots with multiple degrees of freedom, fusing diverse sensory inputs, and managing communication delays and interferences. In nuclear inspection, robots can be crucial in assessing environments where human presence is limited, requiring precise teleoperation and coordination. Teleoperation requires extensive training, as operators must process multiple outputs while ensuring safe interaction with critical assets. These challenges are amplified when operating a fleet of heterogeneous robots across multiple environments, as each robot may have distinct control interfaces, sensory systems, and operational constraints. Efficient coordination in such settings remains an open problem. This paper presents a field report on how we integrated robot fleet capabilities - including mapping, localization, and telecommunication - toward a joint mission. We simulated a nuclear inspection scenario for exposed areas, using lights to represent a radiation source. We deployed two Unmanned Ground Vehicles (UGVs) tasked with mapping indoor and outdoor environments while remotely controlled from a single base station. Despite having distinct operational goals, the robots produced a unified map output, demonstrating the feasibility of coordinated multi-robot missions. Our results highlight key operational challenges and provide insights into improving adaptability and situational awareness in remote robotic deployments.

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@article{vannini2025_2505.01547,
  title={ ASAP-MO:Advanced Situational Awareness and Perception for Mission-critical Operations },
  author={ Veronica Vannini and William Dubois and Olivier Gamache and Jean-Michel Fortin and Nicolas Samson and Effie Daum and François Pomerleau and Edith Brotherton },
  journal={arXiv preprint arXiv:2505.01547},
  year={ 2025 }
}
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