Lockheed Martin Skunk Works Tests AI Mission Contingency on Stalker XE and Alta X Drones

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Lockheed Martin’s Skunk Works division has once again demonstrated its reputation for pioneering advanced defense technologies with a recent test focused on mission contingency management powered by artificial intelligence. The exercise involved the Stalker XE Block 25 unmanned aerial vehicle and the Alta X 2.0 drone, a modified platform from Drone Amplified, in a scenario designed to validate AI-driven decision-making when unexpected fuel-related issues arise. The trial underscored the growing importance of autonomous systems in ensuring operational continuity under contested and unpredictable conditions.

Stalker XE Block 25. Photo: Lockheed Martin

During the test, the AI-enabled command and control system analyzed a simulated problem within seconds, generating multiple re-planning options for the operator. Once the operator selected the preferred course of action, the AI reassigned the mission from the Stalker to the Alta X and directed the Stalker to return safely to base. This rapid response allowed the operator to remain focused on the primary mission objectives while the AI handled technical contingencies, highlighting the potential of machine intelligence to reduce cognitive load in complex operations.

The demonstration also showcased the integration of unmanned systems across multiple domains. Data collected by the Stalker UAV was fed into a unified command node that simultaneously managed unmanned ground vehicles operating in Kansas. Additional support from Fulcrum UAVs illustrated the seamless coordination between air and ground assets, reinforcing the concept of unmanned cross-domain operations. The mobile command node proved capable of directing geographically dispersed drone networks even under degraded conditions such as noise interference or limited connectivity.

By enabling autonomous decision-making and rapid data flow between unmanned systems, the AI framework provided a glimpse into the future of battlefield management. The combination of UAVs and UGVs under AI control offers soldiers enhanced security, speed, and confidence to act decisively in contested environments. This capability ensures that forces can maintain initiative and adapt quickly to evolving threats without being hindered by technical setbacks.

Supporting technologies played a critical role in the success of the trial. The STAR.SDK toolkit allowed developers to rapidly create and deploy AI services, linking contingency applications directly to operator interfaces. This integration enabled operators to interact with a chat-based assistant that presented task reallocation options in real time. Meanwhile, STAR.OS ensured interoperability among diverse AI systems, guaranteeing smooth collaboration between aerial and ground platforms. Together, these technologies formed the backbone of the demonstration, proving that modular and scalable AI solutions can be effectively applied to unmanned operations.

The exercise highlighted how AI can transform mission management by shifting routine problem-solving away from human operators and into autonomous systems. Instead of being distracted by technical failures, operators can concentrate on mission-critical objectives while AI handles reallocation, rerouting, and resource management. This approach not only improves efficiency but also enhances survivability in high-risk scenarios where seconds can determine success or failure.

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