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The use of tethered Unmanned Aircraft Systems (UAS) in aerial robotic applications is a relatively unexplored research field. This work addresses the attitude and position estimation of a small-size unmanned helicopter tethered to a moving platform using a multi-sensor data fusion algorithm based on a numerically efficient sigma-point Kalman filter implementation. For that purpose, the state prediction is performed using a kinematic process model driven by measurements of the inertial sensors (accelerometer and gyroscope) onboard the helicopter and the subsequent correction is done using information from additional sensors like magnetometer, barometric altimeter, LIDAR altimeter and magnetic encoders measuring the tether orientation relative to the helicopter. Assuming the tether is kept taut by an actuated device on the platform during the system operation, the helicopter position is estimated relative to the anchor point. Although this configuration avoids the need of a GPS, a standard operation mode for estimation of the absolute position (the position relative to the inertial reference frame) incorporating corrections with the GPS position and velocity measurements, is also implemented in order to highlight the benefits of the proposed tethered setup. The filter performance is evaluated in simulations.

Researchers at the U.S. Bureau of Mines, in conjunction with researchers at the University of Alabama and the U.S. Army, have developed a fuzzy system for controlling the flight of UH-1 helicopters through various maneuvers. Since flying a helicopter is an extremely difficult task, the fuzzy logic controller was necessarily quite complex. In fact, the control tasks were distributed over four individual control units, each of which had its own rules and associated membership functions. Because the fuzzy logic controller was large and because the rules implemented in the individual control units were not necessarily those a human pilot would use, an efficient technique for writing the rules was required. A genetic algorithm was used to discover rules that provided for effective control of the helicopter. Genetic algorithms are search algorithms based on the mechanics of natural genetics and have demonstrated the ability to locate rules for fuzzy logic controllers. This paper describes the architecture of the helicopter fuzzy logic controller, provides the details of the genetic algorithm application, and presents the results of an actual flight test using the computer software.