Artificial Intelligence and Machine Learning-Based Autonomous Mission Planning for Intelligence, Surveillance, and Reconnaissance (ISR) Missions

Navy STTR 21.B - Topic N21B-T021
NAVAIR - Naval Air Systems Command
Opens: May 19, 2021 - Closes: June 17, 2021 (12:00pm edt)

N21B-T021 TITLE: Artificial Intelligence and Machine Learning-Based Autonomous Mission Planning for Intelligence, Surveillance, and Reconnaissance (ISR) Missions

RT&L FOCUS AREA(S): Artificial Intelligence (AI)/Machine Learning (ML);Autonomy

TECHNOLOGY AREA(S): Air Platforms;Battlespace Environments;Information Systems

OBJECTIVE: Develop a capability to autonomously generate mission plans for onboard Unmanned Aerial Systems (UAS) in support of Intelligence, Surveillance, and Reconnaissance (ISR) missions by applying artificial intelligence (AI) and machine learning (ML) techniques.

DESCRIPTION: With today's advances in software and hardware, autonomous operation is a capability, even if still somewhat disruptive, that is fully realizable as highlighted in references 1–6. In fact, autonomous operation is becoming a critical capability in order to stay ahead of our adversaries. But there are other reasons for autonomous systems [Ref 2], such as "when the world can’t be sufficiently specified a priori" and "when adaptation must occur at machine speed". It also makes a good case for AI, which enables significant autonomy and includes learning, reasoning, introspection, decision making, and much more. Exploiting unmanned systems autonomous mission planning is the next stage in enhancing the capabilities of these systems in the operational environments.

This project’s success relies on utilizing sophisticated software solutions including machine intelligence/learning and modern computer hardware or graphics processing units (CPUs/GPUs – a scaled version of a workload-optimized massively parallelized computer). It should be evident that the size of unmanned aerial vehicles (UAVs) (Groups 1-5) and the types of missions will impact the overall mission planning requirements and complexity.

The goal is to be entirely autonomous; however, in particular with Group 4-5 systems, embedding trust/risk capabilities and detailed contingency plans in autonomous operation—if unacceptable behavior is detected—is as critical as meeting mission success. Even within autonomous operations, there will still be means to alert the Common Control System operator via the envisioned tool that monitors trust embedded on the platform. With these risk mitigations capabilities, the goal of this project will focus on ISR collection – a more simplistic mission when compared to a strike execution mission, which would in the future add considerable levels of mission complexities.

All UAVs will have the necessary sensors and flight control systems to embed the software to generate autonomous missions from takeoff (flight plan and mission plan) to landing, while completing missions including collection and dissemination of ISR data, i.e., when connectivity is available. It is anticipated that activity-based intelligence and/or other relevant information will start the components-based planning process to determine a suitable platform; route planning, types of sensors in support of ISR collection and sensor collection requirements to generate an entire flight plan with associated requirements; and when to disseminate data. Note that many route planning and resource management algorithms exist, thus any solution should include the ability to adaptively change a particular part of the overall planning process. It should also include consideration for automated contingency plans and dynamic replanning capabilities due to various unexpected factors, such as weather, change in mission requirements, etc. These fully autonomous, mission planning service capabilities must be able to be integrated into the Next-Gen Navy Mission Planning System (NGNMPS) and be shared with the Common Control Systems operator with any available communication system with the ability to be modified if necessary, and more importantly, to actually realize the autonomous behavior be embedded on board the platform. Due to the autonomous plan to be initially shared NGNMPS and CCS operator, it will be necessary to define how the plan is presented to the operators.

Finally, in order to meet mission requirements, the solution needs to specify CPU/GPU requirements to achieve as close to real-time performance as possible; and to paraphrase the Heilmeier Catechism exams for success [Ref 11], it will be essential to understand "how to eventually test, verify and evaluate the overall accuracy and performance of the autonomous mission planning process" that need to be addressed as part of this development effort.

PHASE I: Generate a concept of autonomous mission planning from launch to execution of mission specific requirements (ISR as specified in a tasking order and other data such as activity based intelligence data) to data dissemination, and finally, to return to base. This mission plan may also be an airborne modification (dynamic replanning) to the current mission, applying artificial intelligence techniques. Mission plans will take into consideration threat and friendly disposition, weather, terrain, and any onboard sensor (collection) requirements and limitations. In addition the concept needs to outline required hardware to achieve real-time or near real-time processing capabilities. The Phase I effort will include prototype plans to be developed under Phase II. The overall solution should outline data sources and information that will be required to successfully generate mission plans. It is also required to take into account STANAG processes and procedures to minimize proprietary solutions.

PHASE II: Develop a prototype software solution that can be tested in a simulated mission environment.

In Phase II, the program office will provide additional details about the platforms and sensors characteristics and other vital data critical in support of a realistic prototype development.

PHASE III DUAL USE APPLICATIONS: Finalize the prototype version. Perform final testing and verification in a simulated environment and potentially in a real environment using a surrogate vehicle. Transition to naval platform.

Companies such as Amazon, and similar delivery companies that have already started drone-based package delivery, would benefit from this development. FEDEX and UPS would benefit in terms of using large UAVs for package deliveries from large collection centers to smaller distribution centers.

REFERENCES:

  1. "The role of autonomy in DoD systems." Defense Science Board, Department of Defense, 2012, July. https://fas.org/irp/agency/dod/dsb/autonomy.pdf
  2. Stack, J. "Autonomy & autonomous unmanned systems: Overview, investment approach, and opportunities." Office of Naval Research Science & Technology, 2019 September 26. https://www.nationalacademies.org/event/09-25-2019/docs/D6731F8D0ABF361CB04E477B57856ED99859C049B008
  3. Dyndal, G.L., Berntsen T.A. and Redse-Johansen, S. "Autonomous military drones: No longer science fiction." NATO Review, 2017 July 28. https://www.nato.int/docu/review/articles/2017/07/28/autonomous-military-drones-nolonger- science-fiction/index.html
  4. Cebul, D. "The future of autonomous weapons systems: A domain-specific analysis." Center for Strategic and International Studies, New Perspectives in Foreign Policy, 14, 2017 December 20. https://www.csis.org/npfp/future-autonomous-weapons-systems-domain-specific-analysis/
  5. Wilson, J.R. "Artificial intelligence (AI) in unmanned vehicles." Military & Aerospace Electronics, 2019 April 1. https://www.militaryaerospace.com/home/article/16709577/artificial-intelligence-ai-in-unmanned-vehicles
  6. Kazior, T. and Lee, D. "Future autonomous systems overview." Autonomy Working Group, 2016 August 31. https://cra.org/ccc/wp-content/uploads/sites/2/2016/08/Autonomous-Systems-WG-Overview-final.pdf
  7. Atyabi, A., MahmoudZadeh, S. and Nefti-Meziani, S. "Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective." Annual Reviews in Control, 46, 2018, pp.196-215. https://doi.org/10.1016/j.arcontrol.2018.07.002
  8. Stenger, A., Fernando, B. and Heni, M. "Autonomous mission planning for UAVs - A cognitive approach." Paper presentation, Deutscher Luft – und Raumfahrtkongress 2012, Berlin, Germany, 2012 September 10-12. https://www.dglr.de/publikationen/2013/281398.pdf
  9. Llinas, J. and Scrofani, J. "Foundational technologies for activity-based intelligence—A review of the literature." Naval Postgraduate School, 2014 February. https://calhoun.nps.edu/bitstream/handle/10945/40913/NPS-EC-14-001.pdf?sequence=1&isAllowed=y
  10. "Robotics and autonomous systems strategy." U.S. Department of the Army, 2017 March. https://www.tradoc.army.mil/Portals/14/Documents/RAS_Strategy.pdf
  11. "The Heilmeier Cathecism." Defense Advanced Research Project Agency. https://www.darpa.mil/work-with-us/heilmeier-catechism

KEYWORDS: Mission Planning; Unmanned Aerial Vehicle; UAV; Intelligence, Surveillance and Reconnaissance; ISR; Artificial Intelligence; Autonomous; Machine Learning

TPOC-1: Amber Spiegel

Phone: (703) 200-7851

 

TPOC-2: Istvan Der 

Phone: (703) 200-7851

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