Scout AI - Mission Complete Unmanned Ground Vehicle Systems

Scout proposes to develop an end-to-end autonomous ground vehicle system with integrated Vision Language Models (VLMs) and Retrieval Augmented Generation (RAG) technologies to deliver mission-complete solutions for a variety of payloads, beginning with counter-Unmanned Aerial Systems (cUAS) operations. The system is built on a multi-layered approach, starting with fine-tuning of existing LLMs through imitation learning. Human drivers initially guide the vehicles from waypoint to waypoint, and the system learns by replicating this behavior. Over time, the vehicles are trained to not only navigate complex environments but also manage payloads, such as targeting and neutralizing drones, in increasingly dynamic mission scenarios. A key feature of Scout's approach is its modular open systems architecture (MOSA), which allows for flexible integration of payloads and subsystems. The system incorporates multimodal data, combining satellite imagery, camera-based depth perception, and FPV (first-person video) data for spatial awareness and battlefield context generation. These inputs enable the AI agents onboard to operate autonomously in tasks like route planning, threat identification, and drone targeting. In a typical mission, one vehicle may serve as an ISR (Intelligence, Surveillance, and Reconnaissance) unit while another engages the threat using a counter-drone payload. Each vehicle communicates with the others via a multi-node, loosely coupled network to share mission data and operational context. However, since each AI agent operates at the edge, the system is robust enough to operate independently in case of communication outages or contested environments. The proposed system supports full mission autonomy, including mobility and payload integration. Scout’s payload integration framework is designed to meet the needs of various mission profiles, including semi-autonomous operation where remote human operators on the loop can authorize or control payload deployment when needed. The system's autonomy allows it to complete missions in challenging environments, including large paved areas, grassy fields, forests with clear trails, and open desert terrain. The incorporation of a human-machine integrated formations (H-MIF) application ensures that human operators remain in or on the loop for critical decision-making, such as authorizing the use of lethal payloads, while the vehicle performs the majority of the mission autonomously. The architecture is designed to be scalable and adaptable to evolving mission requirements, allowing for future enhancements such as advanced signal processing and algorithms for terrain adaptation. Scout's use of standardized APIs, evaluation pipelines, and prompt discovery methodologies enables the system to be rapidly tested and iteratively improved. Through this approach, Scout aims to deliver a reliable, attritable vehicle system that can operate effectively in both military and civilian contexts.