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

TL;DR

This paper demonstrates a coordinated multi-robot system for nuclear inspection, integrating mapping, localization, and communication to enhance situational awareness in complex, hazardous environments.

Contribution

It presents a novel integration of fleet capabilities for multi-robot missions, showcasing coordinated mapping and control in simulated nuclear inspection scenarios.

Findings

Robots produced a unified map despite different operational goals.

The system enabled remote control of heterogeneous robots in simulated environments.

Insights into operational challenges and adaptability for remote robotic deployments.

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.