미지 환경에서의 안전 비행 운송을 위한 협업제어 및 경로생성 기법

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 김현진.Recently, aerial manipulators using unmanned aerial vehicles (UAVs) are receiving attention due to their superior mobility in three-dimensional space. It can be applied to a wide range of applications such as inspection of hard-to-reach structure or aerial transportation. This dissertation presents a viable approach to safe aerial transportation in unknown environments by using multiple aerial manipulators. Unlike existing approaches for cooperative manipulation based on force decomposition or impedance-based control that often requ- ire heavy or expensive force/torque sensors, this dissertation suggests a method without such sensors, by exploiting the decoupled dynamics to develop estimation and control alg- orithms. With the decoupled dynamics and the assumption of rigid grasp, an online estimator is designed initially to estimate the mass and inertial properties of an unknown payload using the states of the aerial manipulator only. Stable adaptive controller based on the online estimated parameter is then designed using Lyapunov methods. Through simulations, the performance of the proposed controller is compared with conventional passivity-based adaptive algorithms. This dissertation also proposes a motion generation algorithm for cooperative manipulators to transport a payload safely. If the payload is excessively heavy in comparison with the transportation ability of an aerial robot, an aerial robot may crash because of actuation limits on the motors. As a first step, the allowable flight envelope is analyzed with respect to the position of the end-effector. In order to keep the end-effector in the allowable fight region, kinematic coordination between a payload and cooperative aerial manipulators is first studied. A two-layer framework, in which the first layer computes the motion reference of the end-effectors and the second layer calculates the joint motion of the corresponding manipulator, is then developed in a task-prioritized fashion. When generating aerial manipulator trajectories, the desired trajectory is calculated to satisfy the unilateral constraints obtained by the allowable flight envelope. This work also considers the obstacle avoidance of cooperative aerial manipulators in unknown environments. Using the relative distance between an aerial robot and an obstacle as measured by an RGB-D camera and point cloud library (PCL), dynamic movement primitives (DMPs) modify the desired trajectory. By having the leader robot detect an obstacle and the follower robots maintain a given relative distance with the leader, improved efficiency of obstacle avoidance for cooperative robots can be achieved. Finally, the proposed synthesis of estimation, control, and planning algorithms are validated with experiments using custom-made aerial manipulators combined with a two-DOF (Degree Of Freedom) robotic arm. The proposed method is validated with trajectory tracking using two types of payloads. Cooperative aerial transportation in unknown environments is also performed with an unknown obstacle. Both experimental results suggest that the proposed approach can be utilized for safe cooperative aerial transportation.1 Introduction 1 1.1 Background and Motivations 1 1.2 Literature Survey 4 1.2.1 Cooperative Manipulation 4 1.2.2 Handling an Unknown Object 7 1.2.3 Obstacle Avoidance for Cooperative Robots 8 1.3 Research Objectives and Contributions 9 1.3.1 Estimation and Control Algorithm 10 1.3.2 Motion Planning within the Allowable Flight Envelope 11 1.3.3 Real-time Obstacle Avoidance using an RGB-D Camera 11 1.4 Thesis Organization 12 2 Background 14 2.1 Dynamics for Cooperative Aerial Manipulator 14 2.1.1 Rigid Body Statics 15 2.1.2 Dynamics for Single Aerial Manipulator 16 2.1.3 Decoupled Dynamics 19 2.2 Task Priority 22 2.3 DMPs 24 3 Estimator and Controller Design 26 3.1 Payload Mass and Inertia Parameter Estimation 28 3.1.1 System Parametrization 28 3.1.2 On-line Parameter Estimator 29 3.1.3 Robust Analysis for Measurement Noise 32 3.2 Controller Design 34 3.3 Simulation Results 40 4 Path Planning 45 4.1 Allowable Payload for Each Aerial Manipulator 45 4.2 Trajectory Generation with Unilateral Constraints 49 4.2.1 End-eector Trajectory Generation 49 4.2.2 Inverse Kinematics with Null Space Approach 49 4.2.3 Task Prioritization with Unilateral Constraints 56 5 Obstacle Avoidance in Unknown Environments 60 5.1 Obstacle Detection 60 5.2 Movement Primitives for Cooperative Aerial Manipulators 64 6 Experimental Validation and Results 71 6.1 Simulation Validation for Moving Obstacle 71 6.2 Experimental Setup 74 6.3 Experiment for Cooperative Aerial Transportation 77 6.3.1 Path Following with Two Types of Payloads 77 6.3.2 Aerial Transportations in Unknown Environments 78 7 Conclusions 93 Abstract (in Korean) 105Docto

Nonlinear adaptive control of an aerial manipulation system

This paper presents the nonlinear adaptive control of a quadrotor endowed with a 2 degrees of freedom (DOF) manipulator. By considering the quadrotor and the robot arm as a combined system, complete modeling of the aerial manipulation system (AMS) has been presented using the Euler-Lagrange method. A hierarchical nonlinear control scheme which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias and the masses have been tackled along with the reaction forces acting on the attitude part of the AMS. The proposed technique has been validated through simulations in two different scenarios

Active Inference for Integrated State-Estimation, Control, and Learning

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International Conference on Robotics and Automation (ICRA) 202

A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems

Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008–2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests

Aerial manipulation for indoor applications

This thesis presents the design and control of a small aerial manipulator operating in indoor environments. The critical challenges of functioning effectively in such environments are (i) maximizing workspace in constrained spaces like narrow corridors or tight corners, and (ii) achieving stable flight when carrying payloads of unknown mass in the presence of uncertainties. While aerial manipulation has been researched to some extent, few efforts have been made to address both of these challenges simultaneously. First, the dynamics of the quadrotor and manipulator are introduced. Then, two types of baseline flight controllers are described as well as a feedforward torque compensation controller and a robust adaptive augmenting controller. Next, the vehicle and manipulator design methodology is discussed. Lastly, results from the implementation of these algorithms on a real aerial manipulator are presented and conclusions of their efficacy are drawn

Modeling and nonlinear adaptive control of an aerial manipulation system

Autonomous aerial robots have become an essential part of many civilian and military applications. The workspace and agility of these vehicles motivated great research interest resulting in various studies addressing their control architectures and mechanical configurations. Increasing autonomy enabled them to perform tasks such as surveillance, inspection and remote sensing in hazardous and challenging environments. The ongoing research promises further contributions to the society, in both theory and practice. To furthermore extend their vast applications, aerial robots are equipped with the tools to enable physical interaction with the environment. These tasks represent a great challenge due to the technological limitations as well as the lack of sophisticated methods necessary for the control of the system to perform desired operations in an efficient and stable manner. Modeling and control problem of an aerial manipulation is still an open research topic with many studies addressing these issues from different perspectives. This thesis deals with the nonlinear adaptive control of an aerial manipulation system (AMS). The system consists of a quadrotor equipped with a 2 degrees of freedom (DOF) manipulator. The complete modeling of the system is done using the Euler-Lagrange method. A hierarchical nonlinear control structure which consists of outer and inner control loops has been utilized. Model Reference Adaptive Controller (MRAC) is designed for the outer loop where the required command signals are generated to force the quadrotor to move on a reference trajectory in the presence of mass uncertainties and reaction forces coming from the manipulator. For the inner loop, the attitude dynamics of the quadrotor and the joint dynamics of the 2-DOF robotic arm are considered as a fully actuated 5-DOF unified part of the AMS. Nonlinear adaptive control has been utilized for the low-level controller where the changes in inertias have been considered. The proposed controller is tested on a high fidelity AMS model in the presence of uncertainties, wind disturbances and measurement noise, and satisfactory trajectory tracking performance with improved robustness is achieved