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Rear Hemisphere Tail-Rotor Obstacle Avoidance for Unmanned & Manned Rotorcraft

Description:

OBJECTIVE: Develop a low-cost approach for unmanned (and manned) vertical take-off & landing (VTOL) cargo and utility aircraft to autonomously detect and avoid obstacles in the rear hemisphere while conducting low-level maneuvers during the pick-up and delivery of supplies. DESCRIPTION: Manned and unmanned VTOL rotorcraft maneuvering in low-level flight, take-offs and landing, and in confined landing zones (LZ) often lack situational awareness of obstacles in the rear hemisphere of the aircraft, especially at night and in degraded visual environments (DVE). Aircraft are typically equipped with forward and down-ward looking sensors, but lack full 360-degree coverage to detect and alert the crew and aircraft of obstacles as the aircraft is maneuvered in LZs and nap-of-the-earth (NOE) flight. The objective is to provide the aircrews and remote operators with sufficient warning to avoid obstacles in the rear hemisphere of the aircraft during turns and rearward maneuvers (within threshold 25-ft and objective 50-ft radius of main and tail rotor tips) and to provide an automated"stop"of the maneuver to the aircraft flight controls of immediate threat obstacles. For unmanned rotorcraft, the objective system should be capable of providing automated commands to the UAS'flight controls to stop and/or maneuver to avoid the obstacle. PHASE I: Determine the technical feasibility and operational suitability of using low-cost, off-the-shelf sensors/detectors (e.g. small RF used in automotive back-up and parking systems) to prevent obstacle tail strikes by unmanned (and manned) cargo and utility rotorcraft. The technical approach should provide at a minimum sufficient warning to the operator of obstacles 25-50 feet from the tail rotor, tailboom and rear half of the main rotor disk, such that the aircraft is not inadvertently maneuvered into the obstacle. For future unmanned systems the concept is to provide suitable cuing for the UAS to autonomously avoid obstacles in the rear hemisphere while maneuvering into or out of a pick-up or drop zone. The objective interface for this control is the Obstacle Field Navigation (OFN) system developed by the US Army AMRDEC's Aeroflightdynamics Directorate. The study should include: a problem analysis to define sensor requirements; sensor design concept(s)(frequency band, range, mechanically steered or solid state, performance in obscurants/dust, signature, etc); sensor data fusion and database considerations; potential algorithm for predicting probability of collision with stationary or moving obstacles; concept for cockpit alerting mechanism (display, stick shaker, audible annunciation, etc.); potential algorithm for avoiding obstacles autonomously (e.g. US Army Aeroflightdynamics Directorate's (AFDD) RiskMinOFN); concept for integration with existing platform avionics; and airworthiness qualification plan. The study should also include a plan for any further required development, and life-cycle cost estimates. Deliverable is the feasibility study as described above. PHASE II: Develop the best technical and operational approach from Phase I, and determine the quantity and location of sensors/detectors to provide 180-degree rear hemisphere coverage to detect and warn of obstacles 25-50 feet from the tailrotor and rear half of the main rotor disk. This phase should include: sensor design; sensor data fusion and database; algorithm for predicting probability of collision with stationary or moving obstacles; cockpit alerting mechanism (display, stick shaker, audible annunciation, etc.); algorithm for avoiding obstacles autonomously (e.g. AFDD's RiskMinOFN); and integration with existing platform avionics. Demonstrate the concept using off-the-shelf and/or prototype hardware installed on an existing unmanned VTOL aircraft. The system should be able to operate in a degraded visual environment (DVE) and provide audio and visual cues/warning of obstacles within 25-50 feet to the ground control station (GCS) operator and digital cues via OFN to the flight control system for a UAS to autonomously detect and avoid any obstacle in the rear hemisphere. A detailed project plan and test plan should be developed for integration and demonstration of the hardware and software. Hardware and software must be certified as airworthy for experimental purposes, and proper certification obtained from the FAA for UAS experimental testing. Objective is to demonstrate a TRL 6 suitable for transition to a PM for final development and acquisition. Deliverables include the prototype hardware and software, technical data, test results, and final project report. PHASE III: Develop a flight qualified, airworthy prototype design suitable for installation on any specified VTOL aircraft. Design, fabricate, acquire and integrate hardware to be installed and tested on the VTOL aircraft. Demonstrate operational suitability, reliability, and safety of the prototype to adequately demonstrate a TRL 8 for acquisition. The objective transition is to future cargo/utility UAS and optionally-piloted vehicles (OPV) to enable safe, autonomous approach, land and take-off from confined landing areas, by autonomously detecting, reacting to and avoiding obstacles as they maneuvers in the landing area. Commercial applications would include commercial UAS and commercial helicopters, especially those performing logistical services, transmission line and pipeline monitoring services, cargo delivery, emergency medical services and other rotorcraft that require the capability to operate and land in urban areas or confined sites with complex terrain and unknown obstacles. REFERENCES: Public Domain: 1. Waveform design principles for automotive radar systems: Rohling, H.; Meinecke, M.-M. Radar, 2001 CIE International Conference on, Proceedings, 2001. 2. Research activities in automotive radar: Rohling, H.; Meinecke, M.-M.; Mott, K.; Urs, L. Physics and Engineering of Millimeter and Sub-Millimeter Waves, 2001. The Fourth International Kharkov Symposium on, 2001. 3. Design considerations and technology assessment of phased-array antenna systems with RF MEMS for automotive radar applications: Schoebel, J.; Buck, T.; Reimann, M.; Ulm, M.; Schneider, M.; Jourdain, A.; Carchon, G.J.; Tilmans, H.A.C. Microwave Theory and Techniques, IEEE Transactions on, 2005. 4. New Automotive Applications for Smart Radar Systems, Ralph Mende and Hermann Rohling. 5. Moritz; Pre-Crash Sensing Ist functional evaluation based on a platform radar sensor; SAE Technical Paper Series 2000-01-2718. 6. Goerzen, C. and Whalley, M.,"Minimal risk motion planning: a new planner for autonomous UAVs in uncertain environments,"presented at The AHS International Specialists Meeting on Unmanned Rotorcraft, Tempe, AZ, Jan. 2011. 7. Goerzen, C. and Whalley M.,"Sensor Requirements for Autonomous Flight,"presented at the 2012 International Conference on Unmanned Aircraft Systems (ICUAS), Philadelphia, PA, June 2012. 8. libOFN User's Guide, Ver 1.0, July 21, 2010. 9. libSLAD User's Guide, Ver 1.0, July 21, 2010. 10. Mettler, B., Kong, Z., Goerzen, C., and Whalley, M.,"Benchmarking of Obstacle Field Navigation Algorithms for Autonomous Helicopters,"presented at the American Helicopter Society 66th Annual Forum, Phoenix, AZ, May 11-13, 2010. 11. Tsenkov, P., Howlett, J., Whalley, M., Schulein, G., Takahashi, M., Rhinehart, M., Mettler, B.,"A system for 3D autonomous rotorcraft navigation in urban environments,"presented at the 2008 AIAA Guidance, Navigation and Control Conference, Honolulu, Hawaii, August 2008. 12. Precision Autonomous Landing Adaptive Control Experiment (PALACE), Colin Theodore, Mark Tischler, US Army Aeroflightdynamics Directorate (AMRDEC), Ames Research Center, CA, DTIC Accession Number: ADA481066, http://handle.dtic.mil/100.2/ADA481066, 01 NOV 2006.
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