DAF26BX02-NV501 — Resilient Voice-Enabled Artificial Intelligence Assistant for Autonomous Logistics Command and Control in Contested Environments
Award Maximum: $150,000 Period of Performance: 3 months Phase Type: Phase I
OBJECTIVE: The objective of this Phase I effort is to design and demonstrate the feasibility of a secure, edge-deployable, voice-enabled Artificial Intelligence (AI) assistant capable of providing a hands-free Command and Control (C2) of autonomous logistics systems. This Phase I effort will focus on validating natural-language interaction technologies, such as resilient speech-to-text and intent-recognition models optimized for high-noise expeditionary and spaceport environments, and the capability of operating in intermittent or disconnected conditions. The resulting prototype will establish the architecture for integrating voice-based Command and Control into Unmanned Ground Vehicle (UGV) control interfaces, to improve operational efficiency and human-machine teaming in contested environments.
DESCRIPTION: Logistics operations in contested, expeditionary, and spaceport environments present significant challenges for the management of autonomous systems. C2 interfaces rely heavily on graphical user interfaces (GUIs) and persistent network connectivity, both of which are vulnerabilities in Denied, Degraded, Intermittent, and Limited (DDIL) environments. These traditional interfaces often increase operator cognitive burden and limit situational awareness, particularly in high-noise conditions where manual input devices are impractical.
To address this gap, the U.S. Space Force (USSF) seeks the development of a secure, edge-deployable, voice-enabled AI assistant to provide hands-free C2 for autonomous logistics systems, specifically Unmanned Ground Vehicles (UGVs). This capability will allow operators to maintain operational tempo by interacting with autonomous systems through intuitive, natural language-based interfaces that function independently of cloud-based processing. The solution should demonstrate capabilities such as:
Resilient Voice Processing: Speech-to-text and intent-recognition models specifically optimized for high-noise expeditionary environments
Edge-Native Operation: Ability to function in a disconnected or denied, degraded, or disrupted (D3) environment without reliance on persistent external or cloud connectivity
System Interoperability: Compatibility with existing autonomous middleware and UGV control architectures
Secure C2 Implementation: Providing a secure, authenticated voice-command interface that minimizes latency in critical decision-making loops
Ruggedized Performance: Maintaining stability across hardware configurations typically found in forward-deployed or austere spaceport locations
Human-Machine Teaming: Enabling personnel to operate robotic assets hands-free, thereby improving safety and throughput during rapid cargo movements
Proposed solutions should demonstrate the ability to interpret operator intent and execute C2 commands under operationally realistic conditions. This topic seeks technologies that bridge the gap between advanced autonomy and human-centric control, directly supporting the USSF mission objectives for agile and resilient logistics operations in contested environments.
PHASE I: Phase I will establish the technical feasibility of a secure, edge-deployable, voice-enabled AI assistant capable of supporting hands-free C2 of autonomous logistics systems. This effort will focus on defining system architecture, developing natural language processing (NLP) approaches suitable for high-noise environments, and demonstrating core functionality on representative edge computing hardware. Key activities may include:
Workflow & Lexicon Definition: Analyze existing UGV logistics C2 workflows to develop a standardized voice-command lexicon, ensuring accurate interpretation of mission-specific terminology.
Model Development: Design and train resilient speech-to-text (STT) and intent-recognition models optimized for high-noise expeditionary and spaceport acoustic environments.
Edge Deployment Feasibility: Evaluate performance of the AI assistant on representative edge-computing hardware to validate latency, processing requirements, and performance under disconnected or D3 conditions.
System Integration Analysis: Evaluate integration requirements for existing UGV autonomy middleware, identifying necessary Application Programming Interfaces (APIs) and secure communication protocols for C2 execution.
Initial Use Case Validation: Conduct simulated testing using synthetic data to demonstrate the system's ability to correctly interpret commands and trigger appropriate autonomous platform responses.
Deliverables may include:
System Architecture & Design Document: Technical specifications for the edge-deployable AI assistant, including security/authentication frameworks.
Voice-Command Lexicon: Validated list of mission-relevant commands and natural language intents specific to UGV logistics.
Feasibility & Integration Report: Analysis of model performance, edge-compute constraints, and a roadmap for integrating the solution with current UGV control interfaces.
Requirements Validation Summary: Stakeholder feedback and technical findings defining the path to a functioning prototype.
PHASE II: Phase II will advance the voice-enabled AI assistant from a Phase I feasibility prototype to a ruggedized, field-ready capability for hands-free C2 of autonomous logistics platforms. This effort will deliver an integrated system capable of operating on edge-computing hardware in high-noise, contested environments while demonstrating improved human-machine teaming during representative logistics missions. Key activities may include:
Platform Hardening: Develop and test the voice-AI system on ruggedized edge-computing hardware representative of expeditionary logistics platforms, ensuring performance standards are met in simulated austere environments (e.g., extreme temperatures, vibration, and high ambient noise).
System Integration: Integrate the voice-assistant directly with UGV autonomy middleware and operational C2 interfaces (e.g., Tactical Assault Kit (TAK), SpaceC2, and logistics dashboards), ensuring seamless data flow and command execution.
Model Optimization: Refine NLP and intent-recognition models to achieve low-latency interaction (targeting approximately 1–2 seconds response time) ensuring responsive interaction during time-sensitive tactical logistics missions.
Mission-Specific Training: Evolve the command lexicon to support complex, multi-step military logistics workflows, using mission-relevant synthetic and operational datasets.
Testing Exercises: Conduct realistic field trials in representative logistics mission scenarios (e.g., cargo transport at a simulated spaceport or forward operating base) to validate system utility, reliability, and operator safety.
Interoperability Testing: Demonstrate the system's ability to function across heterogeneous autonomous fleets, ensuring command compatibility and interoperable command outputs compatible with multiple UGV control architectures.
Deliverables may include:
Integrated Prototype System: A fully functional, ruggedized voice-AI hardware/software suite ready for operational field testing.
Performance Benchmarking Report: Analysis of system accuracy, latency, and reliability under varying environmental stressors and noise profiles.
Integration Documentation: Technical manuals and API documentation detailing how the system interacts with TAK, Space C2, and various UGV platforms.
Field Trial Report: Evaluation of operational utility, including operator feedback, mission-time improvements, and human-machine teaming metrics.
Transition & Roadmap Plan: A comprehensive strategy for scaling the capability, including security compliance documentation and plans for integration into broader USSF sustainment and other logistics or robotics programs.
PHASE III DUAL USE APPLICATIONS: Following successful Phase II development, the voice-enabled AI assistant will transition into an operational capability for the USSF and broader Joint logistics communities. Phase III will focus on scaling the technology, integrating it with operational systems, and expanding its utility across diverse autonomous platforms. Potential operational applications include:
Logistics C2: Provides warfighters with a secure, hands-free interface for managing UGVs and autonomous cargo systems in expeditionary and contested spaceport operations.
Resilient Sustainment: Enables autonomous mission continuity in DDIL environments where traditional network-dependent interfaces fail.
Human-Machine Teaming Integration: Directly supports USSF and Joint Force initiatives to reduce operator cognitive burden and increase throughput during rapid, high-tempo logistics movements.
Potential commercial applications include:
Industrial Robotics & Logistics: Enhances safety and efficiency for personnel working around heavy autonomous machinery in warehouses, construction sites, and manufacturing hubs.
Remote & Austere Operations: Offers robust, cloud-independent command interfaces for mining, maritime, and oil/gas industries operating in remote locations with limited connectivity.
First Responder & Emergency Services: Provides hands-free control for specialized equipment in chaotic or high-noise environments where manual interaction is not viable.
The technology will transition via integration into active Space Mobility and Sustainment platforms. Program offices (e.g., Space Systems Command) and the Air Force Research Laboratory (AFRL) will facilitate the transition through funding vehicles such as the Strategic Funding Increase (STRATFI) and Tactical Funding Increase (TACFI). Continued efforts will focus on achieving an Authority to Operate (ATO) on U.S. Department of War (DoW) networks, scaling the voice-command library to include cross-platform logistics workflows, and aligning with the "Spaceport of the Future" infrastructure modernization programs.
Technology Readiness Level (TRL) at Phase III Entry: TRL 8-9, following successful field demonstrations and model validation in representative, high-noise operational environments.