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Energy-Aware Autonomy for Air Vehicles


OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Trusted AI and Autonomy;Integrated Sensing and Cyber The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: This topic seeks to develop, mature, apply and advance innovative ideas to control, manage and extend the electrical power and thermal capabilities of an unmanned air vehicle (UAV) through energy aware autonomous algorithms. The effort can focus across a broad range of flight phases or focus on just a few. Flight phases that the proposal could investigate but are not limited to include 1) pre-flight planning, 2) ground operations, 3) terminal area operations, 4) climbing, 5) cruising, 6) mission area, 7) return, or 8) post-flight. Potential energy aware autonomous algorithms may include, but are not limited to, 1) path planning (offline and online), 2) task planning for single vehicle level and/or multi-vehicles, 3) energy contingency management (e.g., causing the electrical system to prioritize actuation energy needs due to sudden wind gusts while landing.), 4) prediction of electrical, thermal, and fuel usage throughout flight phases, 5) optimal formation flying for energy improvements, 6) intelligent electrical startup, and 7) energy run-time assurance. These algorithms could include various artificial intelligent methods, though other methods could be considered. DESCRIPTION: In the military space, growing mission demands (i.e., electronic warfare, advanced sensing, etc.) along with the practical constraints imposed on the size, weight, and power of the energy subsystems of an autonomous vehicle limit the vehicle executing its mission. In the commercial space, optimization of flight paths and energy usage for long endurance package delivery will be needed. Further, vehicles such as electric vertical take-off and landing air taxis require management of energy resources to ensure passengers can safely get to where they need to go in a climate-controlled environment while minimizing fuel usage. In the current state of the art of vehicle power and thermal management, the energy subsystems have only limited communication with autonomy system/pilot and vehicle systems. As a result of this limited information sharing a strategy should be developed to integrate the capabilities of various electrical power and thermal subsystems of the vehicle with appropriate autonomy algorithms to ensure the vehicle can meet its goals. There are two levels of autonomy that could be investigated within this work. The first is a level of autonomy at the vehicle level in which sharing between the mission systems, the air vehicle systems, the autonomous algorithms, and the energy systems (including electrical, thermal, and engine systems) would occur to improve overall capability of a single vehicle. The second level of autonomy exists at the battlefield or mission space. It will integrate the power and thermal performances of each air vehicle across the fleet of multiple UAVs computing trajectory and task assignments based on a operator’s intent. In so doing, energy is managed across the battlefield allowing the operator to choose the asset most likely to complete the mission. For example, the pre-flight mission task could include planning for a fleet of UAVs that considers the total energy of each vehicle and its capabilities in the fleet for individual task assignment. Similarly, in-flight contingency planning will involve flight paths and alternate landing sites (down range landing or return to launch site landing) suitable for each vehicle. In the commercial realm, choosing an appropriate taxi to fulfill a customer need before depleting its energy capabilities would be important. PHASE I: Proposals should include description of work previously done in this area along with a description of which portions of the problem the effort will be focused on. The focus of Phase I should be on development of use cases. The use cases should include relevant benefits that will be achieved, the information that will need to be exchanged between other system(s), and generally describe behaviors that will occur. Discussions with various stakeholders should occur to ascertain the feasibility of the use cases. PHASE II: Proposals should include further development and maturation of Phase I results for the energy aware algorithms. During Phase II, based on the use cases developed in Phase I, algorithms should be developed and implemented to demonstrate the efficacy of the algorithms. Working with AFRL, relevant models of systems could be incorporated with those algorithms to evaluate the benefits outlined in the use cases. PHASE III DUAL USE APPLICATIONS: Commercial applications could include use of the energy aware autonomy algorithms for a UAV that is assigned delivery package. Flight routes could be computed according to its available electric power and thermal capabilities to ensure delivery success, public safety, and residential restrictions. Other applications include air taxis or electric Vertical Takeoff and Landing (eVTOL) aircraft that will want to optimize energy usage. Phase III military applications could include use of energy aware autonomy algorithms on unmanned vehicles with relatively large payloads to extend mission capabilities. REFERENCES: 1. Manyam, Satyanarayana G., David W. Casbeer, Swaroop Darbha, Isaac E. Weintraub, and Krishna Kalyanam. "Path Planning and Energy Management of Hybrid Air Vehicles for Urban Air Mobility." IEEE Robotics and Automation Letters 7, no. 4 (2022): 10176-10183. KEYWORDS: Air vehicle; autonomy; energy management; thermal management; electrical power management; artificial intelligence; energy aware autonomy
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