TECHNOLOGY AREAS: Air Platform, Ground/Sea Vehicles

ACQUISITION PROGRAM: PMA-263, PEO(U&W)

OBJECTIVE: To explore and develop autonomous control algorithms for safe, robust, and effective maneuvering of small (roughly 0.5-4 meter wingspan) unmanned air systems in challenging environmental/weather conditions. This should enable vehicles to survive and maintain some mission capability in a broader range of weather conditions as well as taking advantage of environmental conditions to increase endurance. This should be done to the greatest extent possible using existing sensors only or at most, relatively cheap, low energy, and lightweight additions.

DESCRIPTION: Current small unmanned air systems have limited ability to fly in challenging environmental/weather conditions. Environmental disturbances may lead to loss or damage to the vehicle, an unacceptably high rate of fuel/energy usage, and/or an inability to follow trajectories with sufficient accuracy to carry out mission tasking such as sensing. However, there are examples of systems that are able to operate effectively in such environments. This includes skilled piloting of manned gliders and ultra-lite aircraft, birds, and skilled control of small RC aircraft. These animal and man/machine systems are able to not only operate successfully in challenging conditions, but also to take advantage of atmospheric conditions to improve endurance. There has been some experimentation with autonomous control approaches to take advantage of atmospheric phenomena, such as increasing endurance by soaring on thermals. However, there are a much wider range of phenomena that could potentially be utilized in developing an autonomous maneuvering system including extracting energy from gusts and taking advantage of velocity gradients. Further, another important issue is avoiding loss of vehicle and limiting structural loads that might cause damage in difficult conditions.

One goal of this effort will be to minimize the sensing requirements to enable such a system including cost, weight, volume, and power. For example, control approaches could use existing parameters like energy rate and acceleration and either relate that to explicit models of atmospheric phenomena or utilize that in control approaches implicitly based on such models. Alternatively, existing sensors may be used in non-traditional ways. For example, an air vehicle with GPS data on altitude could potentially use an air data sensor to learn some knowledge about local environmental conditions. Approaches that rely on large, expensive, and power-consuming LIDAR or Doppler Radar sensors are not appropriate. Further, the goal of this effort is to develop the control algorithms and not platform or sensor hardware. Another important goal of the effort will be to ensure any approach is not a point design suitable only for a single type of configuration, but can be applied to a broad range of small unmanned air vehicles including future designs. Finally, analysis and certification techniques to ensure the approach is safe and reliable will be important, and must be considered in the design of the approach.

The Navy will only fund proposals that are innovative address R&D and involve technical risk.

PHASE I: Phase I will provide initial development of the algorithms and experimentation using a limited-fidelity simulation. The simulation should include models of the platform, the sensing approaches, and the relevant environmental phenomena at a reasonable level of complexity and uncertainty (although not necessarily a high degree of fidelity). Phase I should also develop a set of sensing requirements for the particular approach and estimate the cost, weight, volume, and power requirements of the sensing approach and of the processing power required to run all on-board algorithms. Relevant metrics for the simulation proof of concept may include the probability of upsets that could lead to loss of vehicle relative to different environmental conditions, endurance or a related metric such as average thrust required, and the accuracy of maintaining mission sensor field of regard over a desired target area and/or of following particular trajectories.

PHASE II: Phase II shall allow for further development of the algorithms and testing using a high fidelity nonlinear 6- Degree-of-Freedom aircraft model with sufficient complexity for a proof of concept. This model should exhibit both static and dynamic instabilities, relevant disturbances, sensor noise, and uncertainties in its plant dynamics. If feasible, flight test on a small unmanned vehicle may be used in conjunction with simulation. Phase II will also allow for refinement of sensing requirements.

PHASE III: Phase III will develop a software package for use by government and industry to apply the proposed algorithms to a wide range of control systems. Phase III may also allow for experimentation on a target small UAV.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This technology would be relevant to a wide range of civilian uses of unmanned air systems including first responders, environmental monitoring, law enforcement, park service, and agriculture.

REFERENCES:
1. Allen, M. J., "Autonomous Soaring for Improved Endurance of a Small Uninhabited Air Vehicle," AIAA 2005-1025, 43rd AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 10-13 January, 2005.

2. Allen, M. J., "Updraft Model for Development of Autonomous Soaring Uninhabited Air Vehicles," AIAA 2006-1510, 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 9-12 January, 2006.

3. Allen, M. J. and Lin, V., "Guidance and Control of an Autonomous Soaring Vehicle with Flight Test Results," AIAA Aerospace Sciences Meeting and Exhibit, AIAA Paper 2007-867, American Institute of Aeronautics and Astronautics, Reno, Nevada, January 2007.

4. Wharington, J., "Autonomous Control of Soaring Aircraft by Reinforcement Learning," PhD Thesis, Royal Melbourne Institute of Technology, Melbourne, Australia, November 1998.

5. Langelaan, J. W., "Gust Energy Extraction for Mini- and Micro- Uninhabited Aerial Vehicles," AIAA.-2008-0223, 2008.

6. Langelaan, J. W., "Long Distance/Duration Trajectory Optimization for Small UAVs," AIAA Guidance, Navigation and Control Conference, August 16-19 2007.

7. Kerlinger, Paul, Flight Strategies of Migrating Hawks, The University of Chicago Press, Chicago and London, 1989.

8. Boslough, Mark B. E., "Autonomous Dynamic Soaring Platform for Distributed Mobile Sensor Arrays," SAND2002-1896, Sandia National Laboratories, 2002.

9. Patel, Chinmay K., and Ilan Kroo, "Control Law Design for Improving UAV Performance Using Wind Turbulence," AIAA-2006-0231, 2006.

KEYWORDS: autonomous control; unmanned air system; maneuvering; weather