This paper considers the problem of obtaining good attitude estimates from measurements obtained from typical low cost inertial measurement units. The outputs of such systems are characterized by high noise levels and time varying additive biases. We formulate the filtering problem as deterministic observer kinematics posed directly on the special orthogonal group SO (3) driven by reconstructed attitude and angular velocity measurements. Lyapunov analysis results for the proposed observers are derived that ensure almost global stability of the observer error. The approach taken leads to an observer that we term the direct complementary filter. By exploiting the geometry of the special orthogonal group a related observer, termed the passive complementary filter, is derived that decouples the gyro measurements from the reconstructed attitude in the observer inputs. Both the direct and passive filters can be extended to estimate gyro bias online. The passive filter is further developed to provide a formulation in terms of the measurement error that avoids any algebraic reconstruction of the attitude. This leads to an observer on SO(3), termed the explicit complementary filter, that requires only accelerometer and gyro outputs; is suitable for implementation on embedded hardware; and provides good attitude estimates as well as estimating the gyro biases online. The performance of the observers are demonstrated with a set of experiments performed on a robotic test-bed and a radio controlled unmanned aerial vehicle.

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Nonlinear Complementary Filters on the Special Orthogonal Group

The days where swarms of unmanned aerial vehicles (UAVs) will occupy our skies are fast approaching due to the introduction of cost-efficient and reliable small aerial vehicles and the increasing demand for use of such vehicles in a plethora of civil applications. Governments and industry alike have been heavily investing in the development of UAVs. As such it is important to understand the characteristics of networks with UAVs to enable the incorporation of multiple, coordinated aerial vehicles into the air traffic in a reliable and safe manner. To this end, this survey reports the characteristics and requirements of UAV networks for envisioned civil applications over the period 2000–2015 from a communications and networking viewpoint. We survey and quantify quality-of-service requirements, network-relevant mission parameters, data requirements, and the minimum data to be transmitted over the network. Furthermore, we elaborate on general networking related requirements such as connectivity, adaptability, safety, privacy, security, and scalability. We also report experimental results from many projects and investigate the suitability of existing communication technologies for supporting reliable aerial networking.

Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopterâs capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics. Our experimental results include the first autonomous execution of a wide range of maneuvers, including but not limited to in-place flips, in-place rolls, loops and hurricanes, and even auto-rotation landings, chaos and tic-tocs, which only exceptional human pilots can perform. Our results also include complete airshows, which require autonomous transitions between many of these maneuvers. Our controllers perform as well as, and often even better than, our expert pilot.

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Autonomous Helicopter Aerobatics through Apprenticeship Learning

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. This paper presents the first successful autonomous completion on a real RC helicopter of the following four aerobatic maneuvers: forward flip and sideways roll at low speed, tail-in funnel, and nose-in funnel. Our experimental results significantly extend the state of the art in autonomous helicopter flight. We used the following approach: First we had a pilot fly the helicopter to help us find a helicopter dynamics model and a reward (cost) function. Then we used a reinforcement learning (optimal control) algorithm to find a controller that is optimized for the resulting model and reward function. More specifically, we used differential dynamic programming (DDP), an extension of the linear quadratic regulator (LQR).

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An Application of Reinforcement Learning to Aerobatic Helicopter Flight

Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Presenting proven methods, practical guidelines, and real-world flight-test results for a wide range of state-of-the-art flight vehicles, "Aircraft and Rotorcraft System Identification, Second Edition" addresses the entire process of aircraft and rotorcraft system identification from instrumentation and flight testing to model determination, validation, and application of the results. In this highly anticipated second edition, authors Tischler and Remple have added dedicated in-depth chapters presenting extended model structures and identification results for large flexible transport aircraft, and the detailed methodology to develop a continuous full flight envelope simulation model from individual system identification models and trim test data. Topics Discussed include: Frequency-response methods that are especially well suited for system identification of flight vehicle models from flight-test data; specific guidelines for flight testing, data analysis, and the proper selection of model structure complexity; and emphasis on the importance of physical insight in model development and applications. Special features: student version of CIFER[registered] with updated graphical user interface using MATLAB[registered]; numerous flight-test results for both manned and unmanned vehicles illustrating the wide-ranging roles of system identification, including the analysis of flight mechanics, feedback control, handling qualities, subsystem dynamics, structural analysis, higher-order models for aircraft and rotorcraft, and simulation; and, extensive problem sets at the end of each chapter, with many exercises based on flight-test data provided for the XV-15 in hover and cruise giving the reader hands-on real-world experience with system identification methods and interpretation of the results.

Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-Test Examples

System identification modeling of a small-scale unmanned rotorcraft for flight control design

Abstmcf: Flight testing of a fully-instrumented model-scale unmanned helicopter (Yamaha R-SO with loft. diameter rotor) was conducted for the purpose of dynamic model identification. This paper describes the application of CIFER' system identification techniques, which have been developed for full size helicopters, to this aircraft. An accurate, high-bandwidth, linear state-space model was derived for the hover condition. The model structure includes the explicit representation of regressive rotor-flap dynamics, rigid-body fuselage dynamics, and the yaw damper. The R-50 codiguration and identified dynamics are compared with those of a dynamically scaled UH-1H. The identified model shows excellent predictive capability and is well suited for flight control design and simulation applications.

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Mark B. Tischler

Papers

Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-Test Examples

System identification modeling of a small-scale unmanned rotorcraft for flight control design

System identification of small-size unmanned helicopter dynamics

Frequency-Response Method for Rotorcraft System Identification: Flight Applications to BO 105 Coupled Rotor/Fuselage Dynamics

Aircraft and Rotorcraft System Identification, Second Edition