Army SBIR

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.

Autonomous Robotic Bridging

A254-12 | Phase I

This topic seeks to develop autonomous drone swarm capability for watercraft operated in a riverine environment. The fielding of autonomous powered floating bridges will enable the Army to conduct unpredictable dispersed river crossings, increase crew survivability by removing the man from the craft, and reduce logistics footprint over the Improved Ribbon Bridge in use today by combining both payload capacity and powertrain into a single craft. The development of an autonomy package for multiple dispersed floating bays to interact separately and jointly is the key technology to bring this capability to the 2040 battlespace.

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Artificial Intelligence for Interoperability

A254-011 | Phase I

The objective of this topic is to apply Large Language Models (LLMs) and/or other Artificial Intelligence (AI) approaches to support and automate warfighter’s system’s integrations. This will pertain to problems with data unification and interoperability regardless of the target system, source system, or data format. It will focus on usage in tactical environments to assist and provide reliable performance, regardless of echelon level.

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Radar Signal Processing Improvements for Probability of Detection

A254-010 | Phase I

Current ground-based radar systems cannot meet the detection needs of evolving longer range threat systems without sacrificing scan times. The Army needs a solution, such as novel signal processing techniques, that can be implemented on existing radars that will provide improvements in probability of detection at longer ranges. Solutions must not impact current scan time requirements.

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Automated Course of Action Generation

A254-005 | Phase I

Automated Course of Action (CoA) recommendation at the Unit of Action. Currently, it takes units at the Battalion (BN) echelon several hours to use the Military Decision-Making Process (MDMP) to generate and vet CoA options.

Leveraging state-of-the-art Artificial Intelligence/Machine Learning (AI/ML) algorithms will speed up this process by an order of magnitude, allowing systematic replanning during the execution phase of operations. This will improve mission success and reduce risk to force in combat operations. It will also enable the mobile, distributed command post concept.

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