ARGUS: A Framework for Risk-Aware Path Planning in Tactical UGV Operations

TL;DR

ARGUS is a dynamic, risk-aware path planning framework for UGVs that integrates battlefield data and command priorities to generate and adapt optimal trajectories in real-time, enhancing safety and effectiveness.

Contribution

This work introduces ARGUS, a novel framework that combines geospatial, threat, and mission data for real-time, risk-aware path planning in tactical UGV operations.

Findings

Successfully demonstrated in a military exercise with the Portuguese Army

Generated trajectories that balance mission objectives and threat risks

Enabled real-time plan adaptation to unforeseen events

Abstract

This thesis presents the development of ARGUS, a framework for mission planning for Unmanned Ground Vehicles (UGVs) in tactical environments. The system is designed to translate battlefield complexity and the commander's intent into executable action plans. To this end, ARGUS employs a processing pipeline that takes as input geospatial terrain data, military intelligence on existing threats and their probable locations, and mission priorities defined by the commander. Through a set of integrated modules, the framework processes this information to generate optimized trajectories that balance mission objectives against the risks posed by threats and terrain characteristics. A fundamental capability of ARGUS is its dynamic nature, which allows it to adapt plans in real-time in response to unforeseen events, reflecting the fluid nature of the modern battlefield. The system's interoperability were validated in a practical exercise with the Portuguese Army, where it was successfully demonstrated that the routes generated by the model can be integrated and utilized by UGV control systems. The result is a decision support tool that not only produces an optimal trajectory but also provides the necessary insights for its execution, thereby contributing to greater effectiveness and safety in the employment of autonomous ground systems.