Long-term unmanned vehicle operation requires autonomy capable of replanning activities responsive to changing vehicle and environment conditions. For unmanned aircraft systems, human handlers perform refueling/recharge and maintenance activities between flights, so the period of autonomy is typically limited to one flight. This paper investigates flight planning for a solar-energy-harvesting seaplane designed for persistent ocean surveillance without the need for human handling over a potentially long-term mission. A multiflight planner is introduced to generate energy-aware plans for persistent ocean surveillance. A novel heuristic is proposed to solve an asymmetric, nonmetric, negative-cost traveling salesman problem. Heuristic admissibility is demonstrated under specific conditions, and the characteristics of optimal multiflight plans are analyzed over a series of surveillance missions.

The Flying Fish autonomous solar-energy-harvesting seaplane was designed for persistent deployment on the open ocean as a combination aerial-observation and drifting-buoy platform. Two generations of eld-tested vehicles have demonstrated continuous sequences of self-initiated autonomous ight operations in marine and freshwater environments. The addition of solar energy collection in the second-generation vehicle brings extended-toperpetual system deployment within reach. This paper presents the implementation and preliminary results of the modeling and planning utilities meant to achieve energy-aware mission management for safe, long-term unattended, vehicle deployment. Results are presented from ight-test-derived simulations and models.

Air-data systems (ADS) measure wind speed and direction, the loss of which requires aerodynamic forces to be estimated from inertial measurements and aircraft dynamics and performance models. The nature of ADS measurements require air-data probes be subject to the spectrum of environmental conditions. Even with designs meant to withstand harsh conditions, instances of ADS probe failure have been recorded for diverse platform types and situations. Further, since all ADS probes on a common platform are subject to the same conditions, instances of multiple simultaneous failures are not uncommon. Robust air data measurement therefore becomes a multi-sensor data-fusion problem wherein the system may be subject to failures that effect groups of like sensors, such as pitot-static probes, simultaneously. This paper presents an algorithm for fault detection and data fusion of ADS failures in the framework of an unmanned autonomous seaplane with a heritage of air-data probe failures. The fault detection scheme is based on sensor signal characterization and monitoring and on the comparison and fusion of redundant sensor measurements. A GPS/INS-driven backup will also be proposed that can be used both as an ADS diagnostic tool and to allow safe flight to an emergency landing or until air-data sensor functionality can otherwise be restored. Flight test data from two generations of unmanned seaplanes demonstrates the efficacy of the algorithm for a range of real-world failure cases with varied sensors.

*† ‡ § ** †† , The Flying Fish platform is an ocean, environmental monitoring buoy that repositions as an Unmanned Aerial System (UAS), maintaining a pre-set watch circle. To operate in the open ocean, the platform must be robust to moderate sea state conditions and must function unattended thus fully-autonomously. Our concept was conceived as an alternate solution to surface boat designs, avoiding the hydrodynamic drag of ocean waves and currents while in flight. Over the first project year, we developed and repeatedly demonstrated our prototype vehicle’s ability to autonomously “hop” across a GPS-defined “watch circle”, providing initial validation of the unified UAS-buoy (air/sea vehicle) persistent ocean monitoring concept. This paper will describe the vehicle design and performance characterization through simulation and flight-testing and provide insight to the Phase II vehicle which will operate for long periods with a balanced energy budget.

Unmanned air systems are becoming increasingly pervasive in academia as well as industry. Aerospace research and education have to-date focused on the fundamental aerodynamics, structures, and control technologies required for these systems, typically relying on commercial avionics packages to provide the hardware and baseline software needed for unmanned vehicle flight testing. This paper describes an ongoing effort at the University of Michigan to design, implement, and adapt to multiple platforms an open-source, reconfigurable flight management system. Emphasis has been placed on modularity and extensibility, resulting in a system that equally addresses Aerospace education and research challenges. This paper overviews the avionics and software design, implementation, and testing processes completed to-date. Use of the University of Michigan Flight Management System (UM-FMS) to support education in software engineering, embedded instrumentation, and multi-layer control is discussed, along with adaptation to specific flight vehicles.

*† ‡ § The Solus research UAV integrates low -cost Commercial Off The Shelf (COTS) pr oducts into an extensible computing and sensor package. Reconfigurable autopilot software running on the QNX real -time operating system gathers sensor data at sufficient frequency and fidelity to enable system identification via post -flight data analysis. This paper describes the Solus UAV hardware and its reconfigurable autopilot software. Results demonstrate the importance of sensor accuracy and filter tuning, requiring a tradeoff between high frequency noise rejection and accurate maneuver characterizati on. In addition, the paper presents lessons learned during the development and integration of UAV sensor and autopilot systems.