Autonomous Leader-Follower UAS Formation for Enhanced Mission Resilience and Reduced Operator Workload

Objective: The objective of this topic is to develop and demonstrate an affordable robust and reliable autonomous leader-follower UAS formation capability, enabling a single First-Person View (FPV) pilot to command and control multiple UAS effectively. This system should incorporate: seamless pilot reassignment in case of lead UAS failure, synchronized terminal guidance capabilities, and an innovative stasis mode for follower units to conserve energy and maintain position. Furthermore, the system must include advanced target designation features, allowing the lead UAS to mark targets for autonomous execution by follower units Description: The increasing complexity and scale of modern military operations demand unmanned aerial systems (UAS) capable of operating autonomously and collaboratively. Current UAS deployments often require dedicated operators for each platform, resulting in high personnel costs and increased cognitive burden on the warfighter. There is a critical need for UAS technologies that can significantly reduce operator workload while enhancing mission effectiveness and resilience, particularly in contested environments where communication and control links may be degraded or disrupted. The ability for a single pilot to effectively manage multiple autonomous UAS in a coordinated formation, with built-in redundancy and adaptive control, represents a significant advancement in UAS capabilities. Offerors are encouraged to explore innovative approaches to autonomous UAS formation control, incorporating advanced AI algorithms, resilient communication networks (e.g., Neuron Mesh Networks), and robust sensor fusion techniques. The proposed solution should address challenges related to maintaining formation integrity, adapting to dynamic environments, and operating in GPS-denied or contested environments. Innovative approaches to stasis mode are encouraged, optimizing power consumption while maintaining situational awareness. Development should include: AI-based Autonomous Control Algorithms: For leader-follower formation flight, obstacle avoidance, and target engagement. Resilient Communication Network: A robust and secure communication network enabling seamless data sharing and control within the UAS formation (potentially leveraging Neuron Mesh Network technologies). Synchronized Terminal Guidance: Algorithms for coordinated approach and landing of multiple UAS at designated targets. Stasis Mode: An energy-efficient mode allowing follower UAS to maintain position and situational awareness while minimizing power consumption. Target Designation System: A user-friendly interface for the pilot to designate targets for autonomous execution by follower units. Pilot Reassignment Capability: A mechanism for automatic and seamless transfer of lead UAS control to a follower unit in case of failure or loss of communication Keywords: Autonomous UAS; Leader-Follower Formation; AI-Powered Control; Resilient Communication; Terminal Guidance; Contested Environments CMMC Level: Level 2 (Self)