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Risk-Based Unmanned Air System (UAS) Mission Path Planning Capability

Description:

TECHNOLOGY AREA(S): Air Platform 

OBJECTIVE: Develop an Unmanned Air System (UAS) pre-flight mission planning capability that utilizes path planning algorithms to minimize risk to personnel and property during UAS flight operations while reducing preparation times. 

DESCRIPTION: The Navy continues to increase its UAS fleet with new air vehicle systems of various sizes, capabilities, and maturity. UAS do not meet the airworthiness standards that allow manned aircraft to fly within the National Airspace with minimal restrictions placed on flight plans by real-time air traffic control. As a result, UAS operations are typically limited to very restrictive operational areas (e.g. maritime operations and in Active Restricted and Warning Areas) due to risk to personnel and property on the ground. When missions necessitate operation outside of these areas, it can be particularly challenging and time-prohibitive to develop mission plans that ensure proper levels of safety. These constraints significantly limit Navy UAS operations for research, test and evaluation, and fleet operations; therefore, advanced and robust technologies are needed to efficiently create mission flight path plans that enable safe UAS operations within the US National and Foreign Airspaces. Current mission path planning capabilities are primarily air vehicle (AV) centric and rely on human judgment to assess the appropriateness of a given mission plan within a broader context. The UAS air vehicle operator (AVO) is responsible for: 1) defining a complete mission plan prior to flight for autonomous execution, 2) investigating and assessing the risk to personnel and property on the ground using limited information, and 3) continuously monitoring the mission execution (and potentially intervening) for mission changes, vehicle failures, and/or airspace conflicts. The level of safety for a given mission, therefore, depends largely on an individual AVOs ability to synthesize a myriad of data elements and promptly determine and execute the best course of action. Mission planning tools to assist the AVO in the synthesis of such data will improve the overall level of safety of UAS operations, reduce pre-flight manpower requirements, and enable broader integration of UAS within US National and Foreign Airspaces. Mission path planning capabilities and algorithms are needed to improve and standardize the Navys UAS mission planning process, especially to minimize the risk to personnel and property that is independent of, and dependent on, the air vehicle. Potential technologies exist in the academic and industry communities for robotic control, machine learning, data fusion, and numerical optimization that can reduce the complexity of AVO path planning tasks. The risk-based algorithms, and associated technologies, need to be scalable from basic 2-D assessments (e.g. population data) to multidimensional optimization problems that handle mission/vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, and sensor requirements) and risk-based information uncertainties (e.g. inferring population densities from FAA Sectionals). Initial algorithm development may start with analysis of alternatives, and/or generic algorithm class representations to support the further development of the chosen technologies. The algorithm(s) will address flight path safety during normal flight and during contingency operations, including robustness to air vehicle failures and risk-based data uncertainties (e.g. population density data). 

PHASE I: Develop a risk-based UAS mission path planning capability using innovative algorithm(s) for pre-flight (non-real-time) planning tasks that addresses the latitude/longitude 2-D risk problem for personnel and property on the ground. Develop a visualization method to represent the optimization problem trade space and priorities. Identify available and potential information sources to build the required risk database for the proposed mission planning capability. Incorporate example data sources into an open architecture database format and interface to run preliminary, risk-minimized path planning example scenarios by the end of Phase I. 

PHASE II: Develop and demonstrate prototype technology to expand the Phase I capabilities to the multidimensional risk-based mission path planning problem. Include capabilities to minimize risk while incorporating air vehicle constraints (e.g. vehicle speeds, weight, size, maneuvering capabilities, atmospheric winds, sensor requirements) and potentially competing mission parameters (e.g. fuel consumption, time to destination, no-fly zones). Include multiple risk database sources with varying levels of detail from gross information (e.g. population data) to detailed local information (e.g. on-board sensor data). Assess the technologys performance for real-time path planning capabilities in the presence of flight path plan modification triggers like mission objectives, vehicle failures, and airspace conflicts. Identify capability limitations, restrictions, benefits, and growth opportunities for continued development and incorporation of third-party capabilities. Perform a series of integrated mission planning exercises with validation by creating comparative human operator mission plans under the same risk-based goals and assumptions. 

PHASE III: Transition the technology to a Navy UAS (e.g. MQ-25, Triton, Fire Scout, RQ-21 Blackjack, RQ-7 Shadow, or RQ-23 TigerShark), applicable Department of Defense or US Government UAS, or other commercial UAS application. UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities. Private Sector Commercial Potential: UAS are being developed for use across the United States, and the rest of the world, for a multitude of applications: police surveillance, package delivery, movie/TV industry, news, sporting events, recreational and business video recording, and weather monitoring. With an understanding that UAS have safety shortcomings in comparison with manned aircraft, the resulting risk to the population may be mitigated through path planning that minimizes exposure. Risk-based mission planning needs to be used to increase the safety UAS operations to the general population as the systems become more pervasive throughout our communities. 

REFERENCES: 

Gonzalez, Luis Felipe, Lee, Dong Seop, and Periaux, Jacques (December 2-4, 2009). Optimal Mission Path Planning (MPP) for an Air Sampling Unmanned Aerial System, Australasian Conference on Robotics and Automation (ACRA), Sydney, Australia. Retrieved from http://www.araa.asn.au/acra/acra2009/papers/pap107s1.pdf

Griner, Alina (2012). Human-RRT collaboration in Unmanned Aerial Vehicle mission path planning, MIT Dept. of Electrical Engineering and Computer Science, Cambridge, MA. Retrieved from http://dspace.mit.edu/handle/1721.1/76913?show=full; DoD Defense Science Board (July 2012).

Task Force Report: The Role of Autonomy in DoD Systems, Office of the Under Secretary of Defense for Acquisition, Technology and Logistics, Washington, D.C. Retrieved from http://fas.org/irp/agency/dod/dsb/autonomy.pdf;

Rudnick-Cohen, Herrmann, and Azarm (2015). Risk-based Path Planning Optimization Methods for UAVs Over Inhabited Areas, Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, MA. Retrieved from http://www.isr.umd.edu/~jwh2/papers/Rudnick-Cohen-DETC2015-47407.pdf;

Tompkins, Paul (May 2005). Mission-Directed Path Planning for Planetary Rover Exploration, Tech. Report CMU-RI-TR-05-20, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. Retrieved from http://www.ri.cmu.edu/pub_files/pub4/tompkins_paul_2005_1/tompkins_paul_2005_1.pdf

 

KEYWORDS: Mission Planning; Risk Reduction; UAS; Safety; Guidance And Control; Airworthiness 

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