EKF-based parameter identification of multi-rotor unmanned aerial vehiclesmodels

This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically appliedPeer ReviewedPostprint (published version

Autonomous Flight in Unknown Indoor Environments

http://multi-science.metapress.com/content/80586kml376k2711/This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner. Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.National Science Foundation (U.S.) (NSF Division of Information and Intelligent Systems under grant # 0546467)United States. Army Research Office (ARO MAST CTA)Singapore. Armed Force

GPS based position control and waypoint navigation of a quad tilt-wing UAV

Unmanned aerial vehicles (UAV) are becoming increasingly capable nowadays and the civilian applications and the military tasks that can be carried out by these vehicles are far more critical than before. There have been remarkable advances in the design and development of UAVs. They are equipped with various sensors which make them capable of accomplishing missions in unconstrained environments which are dangerous or effortful for manned aircrafts. Recently, significant interest in unmanned aerial vehicles has directed researchers towards navigation problem of flying vehicles. This thesis work focuses on GPS based position control and waypoint navigation of a quad tilt-wing unmanned aerial vehicle (SUAVI: Sabanci University Unmanned Aerial Vehicle). The vehicle is capable of vertical take-off and landing (VTOL). It can also fly horizontally due to its tilt-wing structure. Mechanical and aerodynamic designs are first outlined. A nonlinear mathematical model expressed in a hybrid frame is then obtained using Newton-Euler formulation which also includes aerodynamics effects such as wind and gusts. Extended Kalman filtering (EKF) using raw IMU measurements is employed to obtain reliable orientation estimates which is crucial for attitude stabilization of the aerial vehicle. A high-level acceleration controller which utilizes GPS data produces roll and pitch references for the low-level attitude controllers for hovering and trajectory tracking of the aerial vehicle. The nonlinear dynamic equations of the vehicle are linearized around nominal operating points in hovering conditions and gravity compensated PID controllers are designed for position and attitude control. Simulations and several real flight experiments demonstrate success of the developed position control algorithms

MEMS 센서를 활용한 멀티로터형 무인항공기의 저비용 비행제어시스템의 설계에 관한 연구

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 여재익.흔히 드론(Drone)이라고 불리는 멀티로터형 무인항공기는 저렴하고 조종하기 쉬우며 간단한 구조와 수직 이착륙이 가능하여 군사적인 용도를 비롯하여 상업적인 용도로 널리 쓰이고 있다. 멀티로터형 무인항공기는 가속도 센서, 자이로스코프 센서를 포함하는 관성 측정 유닛(IMU)을 이용하여 지표면에 대한 자세를 측정하여 각 모터의 회전속도를 제어하는 방식으로 비행하며, 비행 방향을 추정할 수 있는 지자기 센서와 고도를 측정할 수 있는 기압계를 내장한다. 비행체에 탑재되는 비행제어유닛(Flight Control Unit, FCU)은 이러한 센서 데이터와 조종 명령을 이용하여 각 모터를 제어한다. 이러한 계산을 수행하기 위해서 기존의 비행 제어 시스템은 하나 이상의 32-bit 마이크로프로세서를 사용하며 이에 따라 비행제어를 위한 펌웨어를 개발하는데 있어 회로 및 패턴 설계 및 소프트웨어 개발 환경(SDK)을 구성하는데 있어 많은 시간과 인력, 비용을 필요로 하여 전체 시스템의 가격이 저렴하지 않다. 또한 작은 크기의 멀티로터에 사용되는 저렴한 비행제어유닛은 프로그래밍이 불가능하거나 확장성에 제약이 있어 하나의 제어 시스템으로 하나의 비행체 모델에만 적용하는 한계가 있다. 따라서 빠르고 간편하게 프로그래밍이 가능하며 저렴하고 구하기 쉬운 8-bit AVR 프로세서와 MEMS 센서, C/C++언어를 이용하여 비행제어시스템을 구성하였으며 그 결과 확장성을 갖추면서 가격이 저렴하면서 효율적인 비행 제어 시스템을 구성할 수 있었다. 부족한 8-bit 프로세서의 성능은 프로세서의 수량을 늘리는 병렬 컴퓨팅 방법으로 해결할 수 있었으며 상보필터의 간결한 구조로 인해 8-bit 프로세서의 낮은 컴퓨팅 성능으로도 초당 약 250Hz의 제어 주기를 가질 수 있었다. 자세 제어 알고리즘으로 Cascade controller를 선택하여 외란에 강하며 빠른 제어 속도를 얻을 수 있었다. 결과적으로 진동이 상대적으로 큰 팜 사이즈의 쿼드로터 UAV에서도 안정적인 비행 성능을 구현할 수 있었다.1. Introduction ············································································· 1 1. 1. About Research ···································································· 4 1. 2. Basic Theory ········································································ 6 1. 2. 1. Attitude Estimation ······························································· 8 1. 2. 2. Cascade PID Controller ······················································ 14 1. 3. Research Goal ······································································ 17 2. Hardware Design ·································································· 18 2. 1. PCB Design ············································································ 20 2. 1. 1. Design of Flight Controller ··············································· 20 2. 1. 2. Design of PMU for BLDC System ····································· 29 2. 2. Body Frame Design ·························································· 33 2. 2. 1. DC Motor Powered Quadcopter ······································ 34 2. 2. 2. BLDC Motor Powered Hexacopter ································· 36 3. Software Design ···································································· 38 3. 1. Flight Software Design ···················································· 38 3. 1. 1. Attitude Reference System ················································ 39 3. 1. 2. Cascade PID Controller ······················································ 44 3. 1. 3. Bluetooth-based Control System ···································· 46 3. 2. IMU & Attitude Reference System ··························· 51 3. 2. Attitude Control Performance ······································· 51 4. Conclusion ·············································································· 53 참고문헌 ························································································ 55 Abstract ························································································ 57Maste

Composite prototyping and vision based hierarchical control of a quad tilt-wing UAV

As the attention to unmanned systems is increasing, unmanned aerial vehicles (UAVs) are becoming more popular based on the rapid advances in technology and growth in operational experience. The main motivation in this vast research field is to diminish the human driven tasks by employing UAVs in critical civilian and military tasks such as traffic monitoring, disasters, surveillance, reconnaissance and border security. Researchers have been developing featured UAVs with intelligent navigation and control systems on more efficient designs aiming to increase the functionality, flight time and maneuverability. This thesis focuses on the composite prototyping and vision based hierarchical control of a quad tilt-wing aerial vehicle (SUAVI: Sabanci University Unmanned Aerial VehIcle). With the tilt-wing mechanism, SUAVI is one of the most challenging UAV concepts by combining advantages of vertical take-off and landing (VTOL) and horizontal flight. Various composite materials are tested for their mechanical properties and the most suitable one is used for prototyping of the aerial vehicle. A hierarchical control structure which consists of high-level and low-level controllers is developed. A vision based high-level controller generates attitude references for the low-level controllers. A Kalman filter fuses data from low-cost inertial sensors to obtain reliable orientation information. Low-level controllers are typically gravity compensated PID controllers. An image based visual servoing (IBVS) algorithm for VTOL, hovering and trajectory tracking is successfully implemented in simulations. Real flight tests demonstrate satisfactory performance of the developed control algorithms