Dependable Low‐altitude Obstacle Avoidance for Robotic Helicopters Operating in Rural Areas

This paper presents a system enabling robotic helicopters to fly safely without user interaction at low altitude over unknown terrain with static obstacles. The system includes a novel reactive behavior‐based method that guides rotorcraft reliably to specified locations in sparsely occupied environments. System dependability is, among other things, achieved by utilizing proven system components in a component‐based design and incorporating safety margins and safety modes. Obstacle and terrain detection is based on a vertically mounted off‐the‐shelf two‐dimensional LIDAR system. We introduce two flight modes, pirouette descent and waggle cruise, which extend the field of view of the sensor by yawing the aircraft. The two flight modes ensure that all obstacles above a minimum size are detected in the direction of travel. The proposed system is designed for robotic helicopters with velocity and yaw control inputs and a navigation system that provides position, velocity, and attitude information. It is cost effective and can be easily implemented on a variety of helicopters of different sizes. We provide sufficient detail to facilitate the implementation on single‐rotor helicopters with a rotor diameter of approximately 1.8 m. The system was extensively flight‐tested in different real‐world scenarios in Queensland, Australia. The tests included flights beyond visual range without a backup pilot. Experimental results show that it is feasible to perform dependable autonomous flight using simple but effective methods. © 2013 Wiley Periodicals, Inc.     

Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) are becoming popular among researchers and vital platforms for several autonomous mission systems. In this paper, we present the design and development of a miniature autonomous rotorcraft weighing less than 700 g and capable of waypoint navigation, trajectory tracking, visual navigation, precise hovering, and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini‐ and microrotorcraft, an embedded and inexpensive autopilot was developed. To compensate for the weaknesses of the low‐cost equipment, we put our efforts into designing a reliable model‐based nonlinear controller that uses an inner‐loop outer‐loop control scheme. The developed flight controller considers the system's nonlinearities, guarantees the stability of the closed‐loop system, and results in a practical controller that is easy to implement and to tune. In addition to controller design and stability analysis, the paper provides information about the overall control architecture and the UAV system integration, including guidance laws, navigation algorithms, control system implementation, and autopilot hardware. The guidance, navigation, and control (GN&C) algorithms were implemented on a miniature quadrotor UAV that has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Experimental results that demonstrate the operation of the GN&C algorithms and the capabilities of our autonomous micro air vehicle are presented. © 2009 Wiley Periodicals, Inc.     

Modeling and Control of a Small Autonomous Aircraft Having Two Tilting Rotors

This paper presents recent work concerning a small tiltrotor aircraft with a reduced number of rotors. The design consists of two propellers which can tilt laterally and longitudinally. A model of the full birotor dynamics is provided, and a controller based on the backstepping procedure is synthesized for the purposes of stabilization and trajectory tracking. The proposed control strategy has been tested in simulation     

An adaptive vision-based autopilot for mini flying machines guidance,navigation and control

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This paper addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks such as take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.     

Optic flow-based vision system for autonomous 3D localization and control of small aerial vehicles

The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow.     

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

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.