UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking

A Miniature Integrated Navigation System for Rotary-Wing Unmanned Aerial Vehicles

This paper presents the development of a low cost miniature navigation system for autonomous flying rotary-wing unmanned aerial vehicles (UAVs). The system incorporates measurements from a low cost single point GPS and a triaxial solid state inertial/magnetic sensor unit. The navigation algorithm is composed of three modules running on a microcontroller: the sensor calibration module, the attitude estimator, and the velocity and position estimator. The sensor calibration module relies on a recursive least square based ellipsoid hypothesis calibration algorithm to estimate biases and scale factors of accelerometers and magnetometers without any additional calibration equipment. The attitude estimator is a low computational linear attitude fusion algorithm that effectively incorporates high frequency components of gyros and low frequency components of accelerometers and magnetometers to guarantee both accuracy and bandwidth of attitude estimation. The velocity and position estimator uses two cascaded complementary filters which fuse translational acceleration, GPS velocity, and position to improve the bandwidth of velocity and position. The designed navigation system is feasible for miniature UAVs due to its low cost, simplicity, miniaturization, and guaranteed estimation errors. Both ground tests and autonomous flight tests of miniature unmanned helicopter and quadrotor have shown the effectiveness of the proposed system, demonstrating its promise in UAV systems

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

Error analysis of algorithms for camera rotation calculation in GPS/IMU/camera fusion for UAV sense and avoid systems

In this paper four camera pose estimation algorithms are investigated in simulations. The aim of the investigation is to show the strengths and weaknesses of these algorithms in the aircraft attitude estimation task. The work is part of a research project where a low cost UAV is developed which can be integrated into the national airspace. Two main issues are addressed with these measurements, one is the sense-and-avoid capability of the aircraft and the other is sensor redundancy. Both parts can benefit from a good attitude estimate. Thus, it is important to use the appropriate algorithm for the camera rotation estimation. Results show that many times even the simplest algorithm can perform at an acceptable level of precision for the sensor fusion. © 2014 IEEE