

Objective
Develop and integrate a distributed Artificial Intelligence (AI) technology that can collaboratively command and control a multi-agent Group 1 or Group 2 Unmanned Aerial System (UAS) swarm to defend an area against a numerically superior enemy swarm. AI system must be able to interface with existing, standard platform autonomy and perception systems. System must also not rely on a centralized control node.
This opportunity will be open for both SBIR and STTR participation.
SBIR topic number: ARM26BX04-NV008
STTR topic number: ARM26TX04-NV001
Description
Develop and integrate distributed Artificial Intelligence (AI) technology that can collaboratively control a multi-agent Group 1 or Group 2 Unmanned Aerial System (UAS) swarm to defend an area against a numerically superior attacking enemy swarm. The vast majority of counter-UAS systems are optimized for a 1 vs 1 scenario, in which the interceptor UAS seeks to destroy, degrade, disable, or capture a single enemy UAS. This approach typically relies on a sensor package and kinetic or non-kinetic effector optimized to degrade/destroy a single enemy UAS of a specific class (size, range, speed, etc.). To defend an area against a numerically superior enemy swarm, individual UAS platforms must collaborate to determine the optimal strategy for many individual 1 vs N scenarios. Individual UAS platforms must demonstrate the ability to target a cluster of enemy platforms through AI algorithms and active or passive inter-drone communication for targeting information from other friendly platform perspectives. The UAS platform employed can be an off-the-shelf OEM or custom-built. Key system attributes include:
This effort is not designed to create, design, or deliver a new UAS platform as the end item. Rather, it is meant to develop technology that will leverage the existing capabilities of OEM drone platforms or, if necessary, custom-built drones by the performer. The key deliverable is a suite of AI and other software algorithms that continuously plan and take optimal actions in a decentralized manner. The algorithms run on each individual UAS platform, take advantage of active communications with other friendly platforms when operating in a permissive network environment, but can still operate under a denied or degraded network environment by communicating passively.
Phase I
This topic is accepting Phase I submissions for a cost limit up to $300,000 and a 1-6-month period of performance.
Conduct a feasibility study to assess what is in the art of the possible that satisfies the requirements specified in the above “Objective” and “Description” paragraphs. Propose multiple algorithmic approaches, sensors packages, and assess tradeoffs in performance according to different combinations of such parameters.
Phase II
Develop, install, and demonstrate a prototype system determined to be the most feasible solution during the Phase I feasibility study on a Group 1 or Group 2 Program of Record UAS (or custom-built drone). Simulation may be used for demonstration at very large scale for cost purposes. However, demonstrations of swarm behavior must include hardware for smaller swarms. The primary demonstrable capability is the decentralized execution of AI algorithms that enable active or passive collaboration of a friendly swarm of homogeneous UAS platforms that reduce the combat power of a numerically superior enemy swarm to a maximum degree. Multiple evaluations in hardware demonstrating effective 1-N targeting will be conducted to ensure core capability can scale to larger swarms.
Phase III
Protection of critical infrastructure in CONUS. These are considered soft targets even if they are government facilities. We have recently seen the threat domestically launched drones can have on any structure given how easily unidentified drones have been able to fly unencumbered in CONUS.
Submission Information
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil

References:
Objective
Develop and integrate a distributed Artificial Intelligence (AI) technology that can collaboratively command and control a multi-agent Group 1 or Group 2 Unmanned Aerial System (UAS) swarm to defend an area against a numerically superior enemy swarm. AI system must be able to interface with existing, standard platform autonomy and perception systems. System must also not rely on a centralized control node.
This opportunity will be open for both SBIR and STTR participation.
SBIR topic number: ARM26BX04-NV008
STTR topic number: ARM26TX04-NV001
Description
Develop and integrate distributed Artificial Intelligence (AI) technology that can collaboratively control a multi-agent Group 1 or Group 2 Unmanned Aerial System (UAS) swarm to defend an area against a numerically superior attacking enemy swarm. The vast majority of counter-UAS systems are optimized for a 1 vs 1 scenario, in which the interceptor UAS seeks to destroy, degrade, disable, or capture a single enemy UAS. This approach typically relies on a sensor package and kinetic or non-kinetic effector optimized to degrade/destroy a single enemy UAS of a specific class (size, range, speed, etc.). To defend an area against a numerically superior enemy swarm, individual UAS platforms must collaborate to determine the optimal strategy for many individual 1 vs N scenarios. Individual UAS platforms must demonstrate the ability to target a cluster of enemy platforms through AI algorithms and active or passive inter-drone communication for targeting information from other friendly platform perspectives. The UAS platform employed can be an off-the-shelf OEM or custom-built. Key system attributes include:
This effort is not designed to create, design, or deliver a new UAS platform as the end item. Rather, it is meant to develop technology that will leverage the existing capabilities of OEM drone platforms or, if necessary, custom-built drones by the performer. The key deliverable is a suite of AI and other software algorithms that continuously plan and take optimal actions in a decentralized manner. The algorithms run on each individual UAS platform, take advantage of active communications with other friendly platforms when operating in a permissive network environment, but can still operate under a denied or degraded network environment by communicating passively.
Phase I
This topic is accepting Phase I submissions for a cost limit up to $300,000 and a 1-6-month period of performance.
Conduct a feasibility study to assess what is in the art of the possible that satisfies the requirements specified in the above “Objective” and “Description” paragraphs. Propose multiple algorithmic approaches, sensors packages, and assess tradeoffs in performance according to different combinations of such parameters.
Phase II
Develop, install, and demonstrate a prototype system determined to be the most feasible solution during the Phase I feasibility study on a Group 1 or Group 2 Program of Record UAS (or custom-built drone). Simulation may be used for demonstration at very large scale for cost purposes. However, demonstrations of swarm behavior must include hardware for smaller swarms. The primary demonstrable capability is the decentralized execution of AI algorithms that enable active or passive collaboration of a friendly swarm of homogeneous UAS platforms that reduce the combat power of a numerically superior enemy swarm to a maximum degree. Multiple evaluations in hardware demonstrating effective 1-N targeting will be conducted to ensure core capability can scale to larger swarms.
Phase III
Protection of critical infrastructure in CONUS. These are considered soft targets even if they are government facilities. We have recently seen the threat domestically launched drones can have on any structure given how easily unidentified drones have been able to fly unencumbered in CONUS.
Submission Information
For more information, and to submit your full proposal package, visit the DSIP Portal.
SBIR|STTR Help Desk: usarmy.sbirsttr@army.mil
References:
