Konrad Rudin

Implementation of a Nonlinear Attitude Estimator for Aerial Robotic Vehicles

Attitude estimation is a key component of the avionics suite of any aerial robotic vehicle. This paper details theoretical and practical solutions in order to obtain a robust nonlinear attitude estimator for flying vehicles equipped with low-cost sensors. The attitude estimator is based on a nonlinear explicit complementary filter that has been significantly enhanced with an effective gyro-bias compensation via the design of an anti-windup nonlinear integrator. A measurement decoupling strategy is proposed in order to make roll and pitch estimation robust to magnetic disturbances that are known to cause errors in yaw estimation. In addition, this paper discusses the fixed-point numerical implementation of the algorithm. Finally, simulation and experimental results confirm the performance of the proposed method.

A Robust Attitude Controller and its Application to Quadrotor Helicopters

Abstract In this paper, a novel nonlinear hierarchical controller for attitude control is proposed. This controller is obtained using Lyapunov methodology. Model uncertainties in the system are estimated on-line based on a time-delay control approach. The robustness of the flight controller is enhanced using an anti-windup integrator technique and semi-global asymptotical stability is proven. The control law obtained is simple enough for an implementation on a small microcontroller. Simulation results for a model of a quadrotor helicopter illustrate the performance of the proposed control algorithm.

Long-Endurance Sensing and Mapping Using a Hand-Launchable Solar-Powered UAV

This paper investigates and demonstrates the potential for very long endurance autonomous aerial sensing and mapping applications with AtlantikSolar, a small-sized, hand-launchable, solar-powered fixed-wing unmanned aerial vehicle. The platform design as well as the on-board state estimation, control and path-planning algorithms are overviewed. A versatile sensor payload integrating a multi-camera sensing system, extended on-board processing and high-bandwidth communication with the ground is developed. Extensive field experiments are provided including publicly demonstrated field-trials for search-and-rescue applications and long-term mapping applications. An endurance analysis shows that AtlantikSolar can provide full-daylight operation and a minimum flight endurance of 8 h throughout the whole year with its full multi-camera mapping payload. An open dataset with both raw and processed data is released and accompanies this paper contribution.

Nonlinear attitude estimation with measurement decoupling and anti-windup gyro-bias compensation

Abstract Any low-cost IMU is subject to biased measurement in its gyroscopes. This paper presents a nonlinear attitude observer which incorporates anti-windup gyro-bias compensation. It also provides an extension of the decoupling strategy of IMU measurement recently proposed in the literature by providing almost global stability proof along with a comprehensive study of the decoupling strategy. Simulation and experimental results are given to illustrate the concept.

Robotic technologies for solar-powered UAVs: Fully autonomous updraft-aware aerial sensing for multiday search-and-rescue missions

Large-scale aerial sensing missions can greatly benefit from the perpetual endurance capability provided by high-performance low-altitude solar-powered UAVs. However, today these UAVs suffer from small payload capacity, low energetic margins and high operational complexity. To tackle these problems, this paper presents four individual technical contributions and integrates them into an existing solar-powered UAV system: First, a lightweight and power-efficient day/night-capable sensing system is discussed. Second, means to optimize the UAV platform to the specific payload and to thereby achieve sufficient energetic margins for day/night-flight with payload are presented. Third, existing autonomous launch and landing functionality is extended for solar-powered UAVs. Fourth, as a main contribution an extended Kalman filter-based autonomous thermal updraft tracking framework is developed. Its novelty is that it allows the end-to-end integration of the thermal-induced roll moment into the estimation process. It is assessed against unscented Kalman filter and particle filter methods in simulation and implemented on the aircraft’s low-power autopilot. The complete system is verified during a 26-hour search-and-rescue aerial sensing mockup mission that represents the first-ever fully-autonomous perpetual endurance flight of a small solar-powered UAV with a day/night-capable sensing payload. It also represents the first time that solar-electric propulsion and autonomous thermal updraft tracking are combined in flight. In contrast to previous work that has focused on the energetic feasibility of perpetual flight, the individual technical contributions of this paper are considered core functionality to guarantee ease-of-use, effectivity and reliability in future multi-day aerial sensing operations with small solar-powered UAVs.

Strategies for sensor-fault compensation on UAVs: Review, discussions & additions

This paper provides a review of different possible strategies to compute meaningful states of an unmanned aerial vehicle (UAV) in the presence of sensor faults. The paper reviews some strategies that enable to compute useful outputs and to reconstruct sensor data when some sensor(s) become(s) deficient. In addition to a classical sensor suite for UAVs (IMU, GPS, barometer and airspeed sensor), the paper introduces the benefits of using visual aids for sensor-fault tolerance. Finally, the paper provides the reader with an overview schematic - discussed throughout the paper- in order to summarize some possible sensor fault-tolerant systems in UAVs.

A sensor fault detection for aircraft using a single Kalman filter and hidden Markov models

This paper presents a new scheme for sensor fault detection and isolation. It uses a single Kalman filter and a Gaussian hidden Markov model for each of the monitored sensors. This combination is able to simultaneously detect single and multiple sensor faults, still guaranteeing optimal system state estimation. This algorithm also can run on systems with limited computational power. The efficiency of the approach is evaluated through simulation of an aircraft to detect airspeed and GPS sensor faults. The results show fast fault detection and low false-alarm rate.