Introduction to the Special Issue on Aerial Manipulation

The papers in this special section focus on aerial manipulation which is intended as grasping, positioning, assembling and disassembling of mechanical parts, measurement instruments and any other kind of objects, performed by a flying robot equipped with arms and grippers. Aerial manipulators can be helpful in those industrial and service applications that are considered very dangerous for a human operator. For instance, think of tasks like the inspection of a bridge, the inspection and the fixing-up of high-voltage electric lines, the repairing of rotor blades and so on. These tasks are both very unsafe and expensive because they require the performance of professional climbers and/or specialists in the field. A drone with manipulation capabilities can instead assist the human operator in these jobs or, at least, in the most hazardous and critical situations. As a matter of fact, such devices can indeed operate in dangerous tasks like reaching the bottom of the deck of a bridge or the highest places of a plant or a building; they can avoid dangerous work at height; aerial platforms can increase the total number of inspections of a plant, monitoring the wear of the components. Without doubts, aerial manipulation will improve the quality of the job of many workers

모델 예측 제어와 네트워크 지연 보상 기법을 이용한 무인기의 네트워크 제어

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 김현진.본 연구는 시간에 따라 변화하는 네트워크 지연이 존재하는 네트워크 환경에서의 무인 항공기 (UAV)의 제어 기법에 대하여 소개한다. 네트워크 지연은 주로 상태 피드백과 제어 입력의 지연을 야기시키고, 이로 인해 UAV 제어 시스템의 안정성에 악영향을 미친다. 이와 같은 네트워크 지연에 대처하기 위하여 몇 가지 네트워크 제어 알고리즘이 제안되었지만 대부분의 기존 연구에서는 플랜트 동역학이 매우 단순하거나 정확히 알고 있다고 가정하였고, 일정한 네트워크 지연이 발생하는 상황에서만 수행되었다. 하지만 이러한 가정은 비선형 모델 및 시간에 민감한 제어 특성을 가지는 멀티로터 형태의 UAV에 적합하지 않다. 이러한 문제를 해결하기 위하여 멀티로터의 특성을 고려하여 설계된 모델 예측 제어 (MPC)를 이용한 네트워크 제어 시스템을 제안한다. 또한 경로 계획 및 상태 추정의 정확도를 높이고자 가우시안 프로세스 (GP) 기법을 적용하여, 멀티로터 동역학에 고려되지 않은 미지의 모델을 학습하도록 한다. 실내 비행 실험을 통하여 제안 된 알고리즘이 네트워크 지연을 효과적으로 보상하고 가우시안 프로세스 학습이 UAV의 경로 추적 성능을 향상 시킨다는 것을 보여준다.This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAVs path tracking performance.Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Thesis contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 GP-MPC for path planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Uplink delay compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Downlink delay compensation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Clock synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Model learning using Gaussian process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1 System dynamics for multirotor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2 Gaussian process to improve dynamic model . . . . . . . . . . . . . . . . . . . . . . 11 4 Model predictive control for networked UAV . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.1 MPC formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.2 MPC formulation for networked control systems . . . . . . . . . . . . . . . . . . . 15 5 Flight experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.1 Delay analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 5.3 Experiment 1: circular flight with network delays . . . . . . . . . . . . . . . . . . . 20 5.4 Experiment 2: two UAVs control with different network delays . . . . . . . . . . . 24 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Maste

Tracking and Grasping of Moving Objects Using Aerial Robotic Manipulators: A Brief Survey

Unmanned Aerial Vehicles (UAV) has evolved in recent years, their features have changed to be more useful to the society, although some years ago the drones had been thought to be teleoperated by humans and to take some pictures from above, which is useful; nevertheless, nowadays the drones are capable of developing autonomous tasks like tracking a dynamic target or even grasping different kind of objects. Some task like transporting heavy loads or manipulating complex shapes are more challenging for a single UAV, but for a fleet of them might be easier. This brief survey presents a compilation of relevant works related to tracking and grasping with aerial robotic manipulators, as well as cooperation among them. Moreover, challenges and limitations are presented in order to contribute with new areas of research. Finally, some trends in aerial manipulation are foreseeing for different sectors and relevant features for these kind of systems are standing out

Robust Control of Nonlinear Systems with applications to Aerial Manipulation and Self Driving Cars

This work considers the problem of planning and control of robots in an environment with obstacles and external disturbances. The safety of robots is harder to achieve when planning in such uncertain environments. We describe a robust control scheme that combines three key components: system identification, uncertainty propagation, and trajectory optimization. Using this control scheme we tackle three problems. First, we develop a Nonlinear Model Predictive Controller (NMPC) for articulated rigid bodies and apply it to an aerial manipulation system to grasp object mid-air. Next, we tackle the problem of obstacle avoidance under unknown external disturbances. We propose two approaches, the first approach using adaptive NMPC with open- loop uncertainty propagation and the second approach using Tube NMPC. After that, we introduce dynamic models which use Artificial Neural Networks (ANN) and combine them with NMPC to control a ground vehicle and an aerial manipulation system. Finally, we introduce a software framework for integrating the above algorithms to perform complex tasks. The software framework provides users with the ability to design systems that are robust to control and hardware failures where preventive action is taken before-hand. The framework also allows for safe testing of control and task logic in simulation before evaluating on the real robot. The software framework is applied to an aerial manipulation system to perform a package sorting task, and extensive experiments demonstrate the ability of the system to recover from failures. In addition to robust control, we present two related control problems. The first problem pertains to designing an obstacle avoidance controller for an underactuated system that is Lyapunov stable. We extend a standard gyroscopic obstacle avoidance controller to be applicable to an underactuated system. The second problem addresses the navigation of an Unmanned Ground Vehicle (UGV) on an unstructured terrain. We propose using NMPC combined with a high fidelity physics engine to generate a reference trajectory that is dynamically feasible and accounts for unsafe areas in the terrain