SBIR / STTR #SBIR / STTR - Machine Learning (ML) for Breach Routing

Army Solicitation: Machine Learning Algorithms for Minefield Breach Routing

The U.S. Army SBIR program is inviting proposals to develop advanced machine learning (ML) algorithms for the Explosive Breacher (EB) system, aiming to modernize minefield breaching operations. - Government Buyer: - U.S. Army SBIR Program - Products/Services Requested: - Development of a prototype AI/ML algorithm for threat detection and breach routing in minefields - Phase I: Demonstrate feasibility of an AI detection method, analyze minefield data, and train an ML algorithm - Future phases: Mature the algorithm, integrate with detection systems, and pursue commercialization - Unique/Notable Requirements: - Focus on artificial intelligence and machine learning for obstacle identification and efficient firing plan generation - Solution must address capability gaps from evolving adversarial technology and outdated legacy equipment (e.g., MICLIC) - No specific OEMs or vendors are named; open to innovative technology providers - Estimated contract value up to $250,000 for Phase I - Place of performance is likely at Army or federal facilities, with the contracting office in Arlington, VA

Description

This solicitation from the Army SBIR program seeks development of machine learning algorithms to improve the Explosive Breacher (EB) system by identifying obstacle locations within minefields and creating efficient firing plans for breaching. The goal is to modernize breaching techniques to counter evolving adversarial technology and aging equipment, enabling safer and faster navigation through minefields. Phase I focuses on developing a prototype AI detection method for threat detection, with subsequent phases aimed at maturation, integration, and commercialization of the technology.

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