기하학 기반의 백스테핑 제어를 이용한 쿼드로터 UAV의 경로추정

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2013. 2. 김현진.This thesis presents a variety of dynamics and trajectory control techniques for the quadrotor. The thesis includes many ways of expressing the dynamics of the quadrotor, such as nonlinear dynamics on Euler Lagrangian and simplied linearized dynamics using small angle assumption, and various control theory, i.e. a linear controller such as PID, LQR and robust control and a nonlinear controller such as feedback linearization, backstepping, geometric control and dynamic inversion. Also, a backstepping controller based on SE(3) to track the desired trajectory for a quadrotor unmanned aerial vehicle is proposed. The proposed controller consists of two parts: 1) a position control part and 2) an attitude control part. The position controller is used to track the desired Cartesian coordinates using position and velocity errors, while the attitude controller uses the rotation matrix error and body angular velocity error to stabilize attitude dynamics expressed on SO(3). Simulation results illustrate the more stable tracking performance of the proposed controller in noisy environments in comparison with other geometric-based controllers. Experimental results on a micro quadrotor show the satisfactory performance of the proposed controller.Abstract Table of Contents List of Figures 1 Introduction 2 Quadrotor Dynamics 3 Quadrotor Control Theory 4 Controller Design 5 Simulation Results 6 Experimental Results 7 ConclusionsMaste

Estimation and Control of Cooperative Aerial Manipulators for a Payload with an Arbitrary Center-of-Mass

This paper presents an integrated framework that integrates the kinematic and dynamic parameter estimation of an irregular object with non-uniform mass distribution for cooperative aerial manipulators. Unlike existing approaches, including impedance-based control which requires expensive force/torque sensors or the first-order-momentum-based estimator which is weak to noise, this paper suggests a method without such sensor and strong to noise by exploiting the decentralized dynamics and sliding-mode-momentum observer. First, the kinematic estimator estimates the relative distances of multiple aerial manipulators by using translational and angular velocities between aerial robots. By exploiting the distance estimation, the desired trajectories for each aerial manipulator are set. Second, the dynamic parameter estimation is performed for the mass of the common object and the vector between the end-effector frame and the center of mass of the object. Finally, the proposed framework is validated with simulations using aerial manipulators combined with two degrees-of-freedom robotic arms using a noisy measurement. Throughout the simulation, we can decrease the mass estimation error by 60% compared to the existing first-order momentum-based method. In addition, a comparison study shows that the proposed method satisfactorily estimates an arbitrary center-of-mass of an unknown payload in noisy environments

Scale-Aware Visual-Inertial Depth Estimation and Odometry Using Monocular Self-Supervised Learning

For real-world applications with a single monocular camera, scale ambiguity is an important issue. Because self-supervised data-driven approaches that do not require additional data containing scale information cannot avoid the scale ambiguity, state-of-the-art deep-learning-based methods address this issue by learning the scale information from additional sensor measurements. In that regard, inertial measurement unit (IMU) is a popular sensor for various mobile platforms due to its lightweight and inexpensiveness. However, unlike supervised learning that can learn the scale from the ground-truth information, learning the scale from IMU is challenging in a self-supervised setting. We propose a scale-aware monocular visual-inertial depth estimation and odometry method with end-to-end training. To learn the scale from the IMU measurements with end-to-end training in the monocular self-supervised setup, we propose a new loss function named as preintegration loss function, which trains scale-aware ego-motion by comparing the ego-motion integrated from IMU measurement and predicted ego-motion. Since the gravity and the bias should be compensated to obtain the ego-motion by integrating IMU measurements, we design a network to predict the gravity and the bias in addition to the ego-motion and the depth map. The overall performance of the proposed method is compared to state-of-the-art methods in the popular outdoor driving dataset, i.e., KITTI dataset, and the author-collected indoor driving dataset. In the KITTI dataset, the proposed method shows competitive performance compared with state-of-the-art monocular depth estimation and odometry methods, i.e., root-mean-square error of 5.435 m in the KITTI Eigen split and absolute trajectory error of 22.46 m and 0.2975 degrees in the KITTI odometry 09 sequence. Different from other up-to-scale monocular methods, the proposed method can estimate the metric-scaled depth and camera poses. Additional experiments on the author-collected indoor driving dataset qualitatively confirm the accurate performance of metric-depth and metric pose estimations

Benchmarking Deep Learning Models for Instance Segmentation

Instance segmentation has gained attention in various computer vision fields, such as autonomous driving, drone control, and sports analysis. Recently, many successful models have been developed, which can be classified into two categories: accuracy- and speed-focused. Accuracy and inference time are important for real-time applications of this task. However, these models just present inference time measured on different hardware, which makes their comparison difficult. This study is the first to evaluate and compare the performances of state-of-the-art instance segmentation models by focusing on their inference time in a fixed experimental environment. For precise comparison, the test hardware and environment should be identical; hence, we present the accuracy and speed of the models in a fixed hardware environment for quantitative and qualitative analyses. Although speed-focused models run in real-time on high-end GPUs, there is a trade-off between speed and accuracy when the computing power is insufficient. The experimental results show that a feature pyramid network structure may be considered when designing a real-time model, and a balance between the speed and accuracy must be achieved for real-time application

Benchmarking Deep Learning Models for Instance Segmentation

Instance segmentation has gained attention in various computer vision fields, such as autonomous driving, drone control, and sports analysis. Recently, many successful models have been developed, which can be classified into two categories: accuracy- and speed-focused. Accuracy and inference time are important for real-time applications of this task. However, these models just present inference time measured on different hardware, which makes their comparison difficult. This study is the first to evaluate and compare the performances of state-of-the-art instance segmentation models by focusing on their inference time in a fixed experimental environment. For precise comparison, the test hardware and environment should be identical; hence, we present the accuracy and speed of the models in a fixed hardware environment for quantitative and qualitative analyses. Although speed-focused models run in real-time on high-end GPUs, there is a trade-off between speed and accuracy when the computing power is insufficient. The experimental results show that a feature pyramid network structure may be considered when designing a real-time model, and a balance between the speed and accuracy must be achieved for real-time application

Real-time Optimal Planning and Model Predictive Control of a Multi-rotor with a Suspended Load

This paper presents planning and control algorithms for a multi-rotor with a suspended load. The suspended load cannot be controlled easily by the multi-rotor due to severe dynamic coupling between them. Difficulties are exacerbated by under-actuated, highly nonlinear nature of multi-rotor dynamics. Although many studies have been proposed to plan trajectories and control this system, there exist only a few reports on real-time trajectory generation. With this in mind, we propose a planning method which is capable of generating collision-free trajectories real-time and applicable to a high-dimensional nonlinear system. Using a differential flatness property, the system can be linearized entirely with elaborately chosen flat outputs. Convexification of non-convex constraints is carried out, and concave obstacle-avoidance constraints are converted to convex ones. After that, a convex optimization problem is solved to generate an optimal trajectory, but semi-feasible trajectory which considers only some parts of the initial state. We apply model predictive control with a sequential linear quadratic solver to compute a feasible collision-free trajectory and to control the system. Performance of the algorithm is validated by flight experiment.N

Molecular-level hybridization of single-walled carbon nanotubes and a copper complex with counterbalanced electrostatic interactions

Abstract Hybridization and wet processibility are highly desired development strategies for next-generation nanomaterials. In particular, the hybridization of carbon nanotubes (CNTs) and transition metals has been investigated for decades owing to the numerous advantages, such as high mechanical and electrical properties. However, manufacturing nano-hybridized CNTs/transition metals is complicated, and no studies have been reported on the dispersion and hybridization of transition metals with single-walled CNTs (SWCNTs) without any harsh or destructive methods due to the strong van der Waals forces. Herein, we demonstrate a one-step dispersion/hybridization of SWCNTs and a Cu-based complex and provide a mechanism derives from counterbalancing the electrostatic interactions via molecular-level charge transfer. The Cu-based complex-hybridized SWCNTs self-assemble and demonstrate suitable viscoelastic behaviors for various printing or coating processes. Finally, the nanostructured SWCNTs/Cu nanoparticle exhibits multifunctional electrothermal properties, electromagnetic interference shielding performances, and flexibilities. The proposed metal-complex-hybridized SWCNTs dispersions provide a wet process guideline for producing nanostructured electrodes

Robust Excitonic‐Insulating States in Cu‐Substituted Ta2NiSe5

Abstract Excitonic insulators exhibit intriguing quantum phases that further attract numerous interests in engineering the electrical and optical properties of Ta2NiSe5. However, tuning the electronic properties such as spin‐orbit coupling strength and orbital repulsion via pressure in Ta2NiSe5 are always accompanied with electron‐hole pair breaking, which is a bottleneck for further applications. Here, the robust excitonic‐insulating states invariant with electron‐doping concentrations in Ta2NiSe5 are demonstrated. The electron doping is conducted by substituting Cu into Ni site (Ta2Ni1‐xCuxSe5). The majority carrier of pristine sample is a hole‐type and is converted to electron‐type with a doping concentration over x = 0.01, whose carrier density can be controlled by varying the Cu concentration. The excitonic transition temperature (Tc) does not significantly alter with electron‐doping concentrations, which is stark contrast with the declining Tc as the hole‐type dopant of Fe or Co increases. The optical conductivity data also demonstrate the invariant excitonic‐insulating states in Cu‐doped Ta2NiSe5. The findings of invariant excitonic‐insulating states in n‐type Cu‐substituted Ta2NiSe5 can be utilized for further electronic device applications by using excitons