Unmanned Underwater-Vehicle (UUV) Mission Sensitive Energy Usage Optimization Using Automated Intelligent Services

Navy SBIR 21.1 - Topic N211-047
NAVSEA - Naval Sea Systems Command - Mr. Dean Putnam - dean.r.putnam@navy.mil
Opens: January 14, 2021 - Closes: February 18, 2021 (12:00pm EDT)

N211-047 TITLE: Unmanned Underwater-Vehicle (UUV) Mission Sensitive Energy Usage Optimization Using Automated Intelligent Services

RT&L FOCUS AREA(S): Autonomy

TECHNOLOGY AREA(S): Ground / Sea Vehicles

OBJECTIVE: Develop decision-making algorithms and planning software that can recommend UUV mission plans satisfying operator-defined mission goals and priorities by proposing joint UUV-path plans and sensor-usage schedules that optimize the UUV’s energy-usage efficiency over an entire mission.

DESCRIPTION: Unmanned Underwater Vehicles (UUVs) are energy-constrained platforms that execute complex missions in dynamic, and often unpredictable, environments. The advent of advanced sensing payloads and the Navy’s interest to extend the operational lifetime of UUVs demand advanced, dynamic, UUV mission-planning tools that go beyond path-planning optimization and "static" mission objectives alone. In particular, there is a need to optimize UUV mission plans based on prioritized objectives with respect to path plans, sensor usage, and energy consumption while ensuring that prioritized mission objectives continue to be satisfied. Most UUV mission planning tools available today rely on models that quantify sensor coverage and energy consumption to define a ‘static’ mission plan prior to starting the mission. These plans often predefine the power budget for the UUV and its payloads, and guarantee an ample energy reserve for UUV emergency procedures. Missions are, however, dynamic in nature and the corresponding mission plans should be revaluated and optimized on-board the UUV during mission execution.

The Navy is looking for mission-effectiveness optimization algorithms that leverage classical control, optimization techniques, and modern artificial intelligence and machine learning methods to develop software tools able to dynamically recommend UUV routes and sensor-usage schedules. The proposed energy usage schedule must account for the UUV’s energy usage over the entire mission and dynamically adjust the schedule according to the mission requirements. The proposed algorithms must also define clear mission-objective satisfaction metrics for assessing mission effectiveness as a function of the mission priorities, the sensor-payload activation schedule, and the overall energy consumption of the UUV. The software implementation of these algorithms should provide the initial mission plan (i.e., route, and sensor operating modes and activation schedules); support on-board monitoring of the UUV’s energy usage across the navigation and sensor payloads; evaluate the path and schedule effectiveness with respect to mission objectives of the sensor payload activations along the planned UUV route in real-time on-board the UUV; and dynamically recommend changes to the current mission plan to maximize mission effectiveness. It is critical that any decision-making approach executed on-board the UUV in response to the dynamics of the mission and the environment to redefine the mission plan can be executed efficiently and within predefined computational and power-usage constraints demarcated by the UUV’s internal configuration.

To ensure interoperability with the PMS 406 portfolio, the software solution must comply with the Unmanned Maritime Autonomy Architecture (UMAA). UMAA establishes a standard for common interfaces and software reuse among the mission autonomy and the various vehicle controllers, payloads, and Command and Control (C2) services in the PMS 406 portfolio of Unmanned Systems (UxS) vehicles. The UMAA common standard for Interface Control Documents (ICDs) mitigates the risk of vendor lock from proprietary autonomy solutions; effects cross-domain interoperability of UxS vehicles; and allows for open architecture (OA) modularity of autonomy solutions, control systems, C2, and payloads. The Navy will provide the open standards for UMAA upon award of Phase I.

Work produced in Phase II may require access to classified information and become classified. Note that the prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been be implemented and approved by the Defense Counterintelligence Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DCSA and NAVSEA in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected contractor will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.

PHASE I: Develop a software-system concept with dynamically reconfigurable routing and sensor-usage schedule algorithms to maximize UUV mission effectiveness by making efficient use of the available energy. Quantify expected energy-usage efficiency improvements and their impact on UUV mission execution, e.g., duration and increased sensor duty cycles, and vehicle configuration, e.g., reduced battery size. Conduct simulations using realistic scenarios and surrogate UUV autonomous control systems to demonstrate and quantify mission effectiveness improvements to demonstrate feasibility of the concept. The software products completed during this phase should be sufficient to demonstrate the implementation feasibility of algorithms, with corresponding software modules interfaced with surrogate UUV autonomous control systems that optimize planning, execution, and energy usage on UUVs to achieve maximum mission effectiveness. The Phase I Option, if exercised, would include the initial layout and capabilities description to build the unit in Phase II. Notional computational and power-usage constraints for select classes of UUVs will be identified in this Phase by the performer in collaboration with PMS 406.

PHASE II: Prior to starting prototype development, performers must identify and discuss with PMS406 the following items: (i) target UUV, access requirements and availability; (ii) UUV autonomy framework and required documentation; (iii) computational and power-usage constraints applied to the targeted UUV (leveraging Phase I analysis); and, (iv) approach for accessing the UUV and all related information needed. Develop a full-scale system prototype that can generate initial mission plans that maximize mission effectiveness and dynamically quantify their effectiveness in realistic mine countermeasure (MCM) scenarios (both real world and simulated). Conduct test and evaluation of the system prototype to quantify UUV-mission-effectiveness improvements. Demonstrate the feasibility of integrating the prototype system with one or more UUV autonomy systems using either a real UUV or a high-fidelity software-in-the-loop (SITL) simulation. Conduct extensive test and evaluation to quantify the UUV mission effectiveness improvements from dynamic mission optimization in realistic MCM mission scenarios with successful demonstration showing that the software can be used on-board a UUV to maximize mission effectiveness without significantly overburdening the computational resources available within the UUV.

It is possible that portions of the work under this effort could be classified under Phase II or Phase III (see Description Section).

PHASE III DUAL USE APPLICATIONS: To ensure interoperability with the PMS 406 portfolio, refine the system solution to comply with the UMAA. Ensure that the system provides a UMAA-compliant software service that provides joint path-planning and energy-usage optimization services by dynamically defining UUV routes and payload activation schedules; and that the resulting service interfaces with UUV autonomous control systems and supports the development of mission plans that maximize mission effectiveness. The target transition platform for the software solution developed as part of this SBIR topic is the Razorback UUV. Inspection, maintenance and repair (IMR) missions for undersea infrastructure, and ocean-bottom mapping and exploration are dual-use applications where the UUV technology developed as part of this SBIR topic will have commercial impact.


  1. Thibbotuwawa, A., Nielsen, P., Zbigniew, B. and Bocewicz, Grzegorz. "Energy Consumption in Unmanned Aerial Vehicles: A Review of Energy Consumption Models and Their Relation to the UAV Routing." Proc. of 39th International Conference on Information Systems Architecture and Technology, Nysa, Poland, Sept. 16-18, 2018. https://link.springer.com/chapter/10.1007/978-3-319-99996-8_16
  2. "Unmanned Undersea Vehicles." RAND National Defense Research Institute. https://www.rand.org/pubs/monographs/MG808.html
  3. Ewachiw, Mark A., Jr. "Design of an Autonomous Underwater Vehicle (AUV) Charging System for Underway, Underwater Recharging." MS Thesis, 2014. Massachusetts Institute of Technology. Cambridge, MA. https://calhoun.nps.edu/bitstream/handle/10945/43069/Ewachiw%20%282014%29%20-%20CIVINS%20MIT%20Thesis.pdf?sequence=1&isAllowed=y
  4. Yang, N., Chang, D., Amini, M.R., Johnson-Roberson, M. and Sun, J. "Energy Management for Autonomous Underwater Vehicles using Economic Model Predictive Control." 2019 American Control Conference (ACC), Philadelphia, PA, USA, pp. 2639-2644. https://arxiv.org/pdf/1906.08719
  5. Woithe, H.C. and Kremer, U. "Feature based adaptive energy management of sensors on autonomous underwater vehicles." Ocean Engineering, vol. 97, 2015, pp. 21-29. https://www.sciencedirect.com/science/article/abs/pii/S0029801814004272

KEYWORDS: Algorithms and Software for UUVs; A&S; Mission Planning and Execution for UUVs; Dynamic Mission Planning; Energy Usage Optimization for UUVs; Unmanned Underwater Vehicle; UUV; MCM Operations


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