Farid Kendoul

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.

Autonomous Flying Robots

This chapter contains a non-technical and general discussion about unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs). This chapter presents some fundamental definitions related to UAVs and MAVs for clarification, and discusses the contents of this monograph. The goal of this chapter is to help the reader to become familiar with the contents of the monograph and understand what to expect from each chapter.

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.

Beyond visual range obstacle avoidance and infrastructure inspection by an autonomous helicopter

This paper demonstrates the feasibility of accomplishing real-world inspection tasks beyond visual range with an autonomous helicopter using simple but effective methods. We propose a LIDAR-based perception and guidance system that enables a helicopter to perform obstacle detection and avoidance, terrain following, and close-range inspection. The system has been implemented on board the CSIRO unmanned helicopter and flight tested in a number of different mission scenarios in unknown environments. We have successfully completed 37 missions and recorded more than 14 hours of autonomous flight time. Missions beyond visual range were executed without a backup pilot. The system has achieved a high success rate and has proven to be dependable.

Embedded autopilot for accurate waypoint navigation and trajectory tracking: Application to miniature rotorcraft UAVs

In this paper, we describe a miniature flight platform weighing less than 700 grams and capable of waypoint navigation, trajectory tracking, precise hovering and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini and micro rotorcraft, a lightweight/portable and inexpensive Guidance, Navigation, and Control system (GN&C) was developed. To compensate for the weaknesses of the low-cost equipment, we put our efforts in obtaining a reliable model-based nonlinear controller. The GN&sC system was implemented on a small four rotor helicopter which has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Flight test results that demonstrate the operation of the GN&C algorithms on a real MAV are presented.

Four-dimensional guidance and control of movement using time-to-contact: Application to automated docking and landing of unmanned rotorcraft systems

This paper presents the development and experimental validation of a bio-inspired autopilot, called TauPilot, based on the ecological tau theory proposed by the psychologist David Lee. Tau theory postulates that animals and humans use a combination of simple guidance strategies and the tau variable (τ) (representing time-to-contact) to prospectively guide and control most of their purposeful movements. This research investigates the feasibility and effectiveness of applying tau theory principles to guidance and control of movement in four dimensions (three spatial dimensions plus time), with application to various crucial maneuvres of unmanned aircraft systems (UAS) such as braking, automated aerial docking and automatic landing. TauPilot includes a tau-guidance system, a tau-navigation system and a tau-controller, resulting in a four-dimensional (4D) guidance, navigation and control system that has the capability to accurately fit maneuvres or actions into 4D slots using only the universal temporal variable, tau. TauPilot has been integrated into two rotorcraft UAS and demonstrated in more than 1000 successful tau-controlled flights. TauPilot provided the UAS with the capability to perform the following maneuvres with high spatial and temporal accuracy: tau-braking, 4D straight- and curved-path tau-docking to a virtual target (a three-dimensional point in space), vertical and 4D coordinated tau-landing, and 4D tau-interception of a moving target point.

A visual navigation system for autonomous flight of micro air vehicles

Many applications of unmanned aerial vehicles (UAVs) require the capability to navigate to some goal and to perform precise and safe landing. In this paper, we present a visual navigation system as an alternative pose estimation method for environments and situations in which GPS is unavailable. The developed visual odometer is an incremental procedure that estimates the vehicles ego-motion by extracting and tracking visual features, using an onboard camera. For more robustness and accuracy, the visual estimates are fused with measurements from an Inertial Measurement Unit (IMU) and a Pressure Sensor Altimeter (PSA) in order to provide accurate estimates of the vehicles height, velocity and position relative to a given location. These estimates are then exploited by a nonlinear hierarchical controller for achieving various navigation tasks such as take-off, landing, hovering, target tracking, etc. In addition to the odometer description, the paper presents validation results from autonomous flights using a small quadrotor UAV.

Flight control of a small helicopter in unknown wind conditions

This paper presents a novel application of a two-time scale controller, using a disturbance observer, for the hover flight control of a Rotary wing Unmanned Aerial Vehicle (RUAV). Flapping and servo dynamics, important from a practical point of view, are included in the RUAV model. The two-time scale controller takes advantage of the ‘decoupling’ of the translational and rotation dynamics of the rigid body, resulting in a two-level hierarchical control scheme. The inner loop controller (attitude control) tracks the attitude commands generated by the outer loop controller and sets the main rotor thrust vector, while the outer loop controller (position control) tracks the reference position. The proposed controller uses the disturbance observer to approximate the external disturbances such as wind gusts. Hover flight simulation results are presented in this paper using the proposed backstepping-based controller.

Real-time wind speed estimation and compensation for improved flight

This paper presents the development and experimental validation of a prototype system for online estimation and compensation of wind disturbances onboard small Rotorcraft unmanned aerial systems (RUAS). The proposed approach consists of integrating a small pitot-static system onboard the vehicle and using simple but effective algorithms for estimating the wind speed in real time. The baseline flight controller has been augmented with a feed-forward term to compensate for these wind disturbances, thereby improving the flight performance of small RUAS in windy conditions. The paper also investigates the use of online airspeed measurements in a closed-loop for controlling the RUAS forward motion without the aid of a global positioning system (GPS). The results of more than 80 flights with a RUAS confirm the validity of our approach.

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.