Asymmetric Collaborative Counter Swarm (ARM26BX04-NV008 SBIR; ARM26TX04-NV001 STTR)

Asymmetric Collaborative Counter Swarm (ARM26BX04-NV008 SBIR; ARM26TX04-NV001 STTR) is sponsored by Department of the Army. This opportunity supports mission-aligned projects and measurable outcomes.

Asymmetric Collaborative Counter Swarm – Army SBIR|STTR Program Artificial Intelligence/Machine Learning, Army SBIR | Army STTR, Phase I Asymmetric Collaborative Counter Swarm Topic Number: ARM26BX04-NV008 (SBIR); ARM26TX04-NV001 (STTR) Application Due Date: 08/19/2026 ​ ​ 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 ​ ​ ​ ​ 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: ​ Must be able to collaborate across a homogeneous set of Group 1 or Group 2 UAS platforms to actively inform each friendly platform (given permissive network environment) of enemy UAS location, velocity, track, etc. ​ Must be able to execute algorithms under extreme SWaP-C constraints with a total compute payload under 2 lbs.

​ Must be able to passively collaborate and achieve similar, but degraded performance within a non-permissive network/communications environment. ​ A single UAS platform must demonstrate the ability to degrade/destroy N enemy UAS within a range of 10 meters through either kinetic or non-kinetic effectors. ​ Friendly UAS swarm must be able to severely degrade the combat power of the enemy to a fraction of X% of its original size.

​ Although 100% degradation of the enemy swarm is ideal, depending on the degree of asymmetry, it may not be realistic. Therefore, a target final enemy combat power goal is achieved from the degree of enemy/friendly asymmetry. As shown in Figure 1, in a scenario where there is no initial asymmetry and our 1 v 1 capabilities are superior, the enemy should retain 0% of its original combat power.

However, if that enemy/friendly initial combat power ratio were 4/1, final enemy combat power might be 75% of its original. The ideal curve is one in which final enemy combat power is 0% regardless of the initial asymmetric combat power advantages the enemy possesses. ​ 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. ​ ​ ​ ​ 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.

​ ​ ​ 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.

​ ​ ​ 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.

​ ​ ​ ​​ For more information, and to submit your full proposal package, visit the DSIP Portal . ​ ​ ​ SBIR|STTR Help Desk: usarmy. sbirsttr@army.

mil https://ieeexplore. ieee. org/abstract/document/9784823?

casa_token=Fbx3MSdQuYwAAAAA:NpovByuBDIXLY9bcZf8CUOjho8l3zA8cDLzz76Mp7Dmnvhb_4hpYJJCZyr9_Ew88BKJaBs0FTg KEYWORDS: asymmetric; collaborative; denied; artificial intelligence; distributed ​ ​ 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 ​ ​ ​ ​ 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: ​ Must be able to collaborate across a homogeneous set of Group 1 or Group 2 UAS platforms to actively inform each friendly platform (given permissive network environment) of enemy UAS location, velocity, track, etc. ​ Must be able to execute algorithms under extreme SWaP-C constraints with a total compute payload under 2 lbs.

​ Must be able to passively collaborate and achieve similar, but degraded performance within a non-permissive network/communications environment. ​ A single UAS platform must demonstrate the ability to degrade/destroy N enemy UAS within a range of 10 meters through either kinetic or non-kinetic effectors. ​ Friendly UAS swarm must be able to severely degrade the combat power of the enemy to a fraction of X% of its original size.

​ Although 100% degradation of the enemy swarm is ideal, depending on the degree of asymmetry, it may not be realistic. Therefore, a target final enemy combat power goal is achieved from the degree of enemy/friendly asymmetry. As shown in Figure 1, in a scenario where there is no initial asymmetry and our 1 v 1 capabilities are superior, the enemy should retain 0% of its original combat power.

However, if that enemy/friendly initial combat power ratio were 4/1, final enemy combat power might be 75% of its original. The ideal curve is one in which final enemy combat power is 0% regardless of the initial asymmetric combat power advantages the enemy possesses. ​ 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. ​ ​ ​ ​ 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.

​ ​ ​ 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.

​ ​ ​ 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.

​ ​ ​ ​​ For more information, and to submit your full proposal package, visit the DSIP Portal . ​ ​ ​ SBIR|STTR Help Desk: usarmy. sbirsttr@army.

mil https://ieeexplore. ieee. org/abstract/document/9784823?

casa_token=Fbx3MSdQuYwAAAAA:NpovByuBDIXLY9bcZf8CUOjho8l3zA8cDLzz76Mp7Dmnvhb_4hpYJJCZyr9_Ew88BKJaBs0FTg KEYWORDS: asymmetric; collaborative; denied; artificial intelligence; distributed Assistant Secretary of the Army for Acquisition, Logistics, and Technology ASA(ALT) releases contract opportunities on an ad-hoc basis to meet Army research and development needs.

Army Futures Command (AFC) releases topics during three specific solicitation periods throughout the fiscal year to address the Army’s current and anticipated war-fighting technology needs. Army STTR follows AFC’s topic release schedule but partners with a university, federally funded research and development center, or a qualified non-profit research institution as part of their contract.

Is the opportunity to establish the scientific, technical, commercial merit and feasibility of your proposed innovation. Is focused on the development, demonstration and delivery of your innovation from Phase I. Represents the commercialization phase of the program in which the company can market their products or services developed in Phase II, either to the government or in the commercial sector.

Allows small businesses to submit to Direct to Phase II applications if they performed the Phase I research through other funding sources. Provides funding to projects that require additional funding during their open Phase II contract. A Phase II Awardee may receive one additional, sequential Phase II award to continue the work of an initial Phase II award.

The sequential Phase II award has the same guideline amounts and limits as an initial Phase II award.

Artificial Intelligence/Machine Learning (supply chain management, logistics coordination, target identifications and simulation) Advanced Materials and Manufacturing (additive manufacturing) Autonomy (unmanned systems, drones, ground vehicle capabilities) Chemical and Biological (detection, defense) Cyber (biometric authentication, secure communications) Electronics (microelectronics, Very-Large-Scale Integration (VLSI)) Electronic Warfare (jamming, spoofing) Human Performance (wearables) Immersive (augmented reality, virtual reality, mixed reality) Network Technologies (antennas, radio frequency, communications systems) Position, Navigation, and Timing (GPS) Power (batteries, generators) Software Modernization (high performance computing, data management and visualization) Sensors (infrared sensing) Weapons Systems (hypersonics, munitions and projectiles, directed energy)