Intelligent Diagnostics for Radomes

TECHNOLOGY AREAS: Ground Sea; Materials; Air Platform OBJECTIVE: This topic seeks to develop second generation platform for supporting radome repair of multiple radomes, and demonstrate it on modern radome systems. Extend AI defect detection algorithms for advanced radome designs. Include the use of enhanced microwave probe technology that utilizes additional frequencies to improve defect sensitivity and accuracy. DESCRIPTION: A next-generation robotic platform is needed for robotically scanning radomes. The Air Force wishes to utilize a new, modular design paradigm so that the scanning robot is a separate unit from the navigation platform. The awardee will construct a mobile platform that can be either manually repositioned (e.g. mobile cart) or connected to a separate navigation robot. The mobile platform will provide a stable base for the scanning robot so that automated navigation can be easily added at a later date. The awardee will also assemble hardware (robot arm, logic controllers, auxiliary sensors, interface computer, etc.) onto the common mobile platform; integrate AMMP end-of-arm tooling along with auxiliary and safety sensors; program adaptive path for selected radome, including adaptive capabilities to account for robot and/or radome positioning errors; include path capability for full area scans as well as concentrated scans for areas with identified defects; develop updated HMI software and back-end supporting software to ensure ease-of-use by technicians in the depot environment; include data outputs for communicating with Big Data' archive server, as well as repair processes and/or robots for utilizing data to repair ARDS identified defects. Finally, the awardee will harden software to meet Air Force cybersecurity requirements, as appropriate. PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Air Force expects the applicant(s) to demonstrate feasibility by means of a prior Phase I-type effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. The feasibility study shall prove through prior research and development that the applicant has demonstrated mobile robotic manipulator technology with the following capabilities: (1) full 3 degree of freedom (DOF) base with a 6 DOF manipulator; (2) capability to accomplish aerospace-relevant discrete processing tasks; (3) localization, navigation, safety, and control systems needed for single-robot operation. Proof can be provided by direct demonstrations of any or all of the aforementioned capabilities, or through a combination of direct demonstrations and proposed modifications to an existing system. PHASE II: Under the Phase II effort, the awardee(s) shall sufficiently develop the technology in order to conduct a small number of relevant demonstrations in simulation. The final prototype system shall be capable of (1) automated scanning of a selected radome (2) Program adaptive path for selected radome, including adaptive capabilities to account for robot and/or radome positioning errors and path capability for full area scans as well as concentrated scans for areas with identified defects (3) Develop an AI-based classification and regression algorithm for defect detection and thickness determination, and predictive modeling of radome viability for selected radome (4) Investigate methods for fast modeling of band-pass radomes that incorporate additional layer(s) of frequency selective surfaces. Develop strategies for modeling these layers as impedances that can capture their angle dependencies for use in future radome applications. The awardee(s) must identify technology hurdles they are expected to encounter during the development program, as well as potential solutions to mitigate risk to the program. PHASE III DUAL USE APPLICATIONS: The awardee(s) will implement the software and hardware tools developed under previous efforts to validate performance and achievement of objectives in pilot production at a USAF air logistics center. The awardee(s) will pursue commercialization of the various technologies developed in Phase II for transitioning the technology to various defense aerospace OEMs, their supply chain, and the Air Force and broader DoD organic industrial base. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. REFERENCES: 1. Freeman, R.M., Schultz, J.W. (2021) Adaptive Radome Maintenance and Diagnostics with AMMP RF Sensor. TRN: AFRL-RX-WP-TR-2021-0173 (DTIC); KEYWORDS: Virtual twin radome; radome tuning; machine learning radome repair; Defect Tolerance and Tuning Strategies for Complex Radomes; Machine learning for wave identification radomes