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

TL;DR

ARGUS addresses the challenge of risk-aware path planning for autonomous ground systems in sensor-rich, threat-lardened theaters by translating commander intent into executable, multi-objective trajectories. The framework unifies geospatial terrain data, probabilistic threat priors, and formation-aware risk into a single probabilistic cost surface and provides three mission-driven planning modes. Its core contribution, the APULSE algorithm, delivers near-optimal solutions for time-constrained planning on large graphs with scalable performance, validated against state-of-the-art solvers and demonstrated in a real field exercise with interoperability to mission-control systems. The work advances autonomous military planning by enabling safer, faster, and more adaptable route generation that accounts for detection risk, terrain, and dynamic battlefield updates. Practically, ARGUS offers a decision-support tool that preserves human intent while enhancing operational safety and effectiveness of autonomous ground systems.

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.

Figures (8)

• Figure 1: Conceptual diagram of the ARGUS planning framework, showing the integration of geospatial data, military intelligence, and the commander’s intent to produce actionable, risk-aware paths for .
• Figure 2: ARGUS system architecture and data flow from ingestion to mission-ready path. The modular design separates data processing, risk modeling, and planning into distinct interoperable stages.
• Figure 3: Workflow of the APULSE algorithm, showing the two main phases: heuristic pre-computation and the guided search loop with multi-stage pruning mechanisms.
• Figure 4: Illustration of the time-bucketing mechanism. The continuous time axis $g_t(v)$ is divided into discrete intervals of width $\Delta T$. For each state $(v,b)$ only the path with minimum cumulative log-risk $g_\ell^{\text{best}}(v,b)$ is stored, pruning dominated alternatives.
• Figure 5: Primary 2D visualization outputs. The risk map (a) explains the survivability trade-offs of the computed path, while the land-cover map (b) provides context for its mobility and feasibility.