일군의 동적 논홀로노믹 기계시스템의 수동성기반 적응 및 강건 안정화 제어기법 연구

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 8. 이동준.We present novel passivity-based stabilization control frameworks for a class of nonholonomic mechanical systems with uncertain inertial parameters. Passive configuration decomposition is first applied to configuration-level decompose the system's Lagrange-DAlembert dynamics into two separate systems. Each of these decomposed systems evolves on its respective configuration space and individually inherits Lagrangian structure and passivity from the original dynamics. Utilizing the nonlinearity and passivity of the decomposed dynamics, we then derive adaptive passivity-based time-varying control (APBVC) and robust passivity-based switching control (RPBSC) schemes, which adopt the concepts of adaptive control and sliding-mode control respectively to achieve stabilization for this certain class of nonholonomic mechanical systems. Both simulation and experimental results are provided to verify our proposed control frameworks.Chapter 1 Introduction 1 1.1 Motivation and Objectives 1 1.2 State of the Art 3 1.3 Contribution of this Work 4 Chapter 2 System Description 6 2.1 Nonholonomic Mechanical Systems with Symmetry Structure 6 2.2 Passive Configuration Decomposition 8 2.3 Control Objective 13 Chapter 3 Passivity-Based Time-Varying Control 16 3.1 Nominal Passivity-Based Time-Varying Control 16 3.2 Adaptive Passivity-Based Time-Varying Control 19 Chapter 4 Passivity-Based Switching Control 25 4.1 Nominal Passivity-Based Switching Control 25 4.2 Robust Passivity-Based Switching Control 29 Chapter 5 Simulation and Experiment 38 5.1 Simulation 38 5.2 Experiment 62 Chapter 6 Conclusion and Future Work 82 6.1 Conclusion 82 6.2 Future Work 83 Bibliography 85 요약 92Maste

분산 제약하에서 원격 제어되는 다수의 논홀로노믹 이동형 로봇 대형 재구성 제어

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 이동준.본 논문에서는 변화하는 주행 환경에서 분산 제약 하에 다수의 원격으로 제어되는 논홀로노믹 이동형 로봇 대형 재구성 제어에 대한 새로운 프레임워크를 제시하였다. 센싱과 컴퓨팅 능력이 갖추어진 온보드 시스템 로봇들을 활용하여 최근 개발된 예측 디스플레이 기법을 적용, 효율적인 군집 로봇의 원격 제어가 가능하도록 하였다. 잘 알려진 논홀로노믹 패시브 디컴포지션 기법을 기반으로 대형 변경이 가능하도록 새로운 파라미터를 추가, 대형 변경간 발생할 수 있는 문제들에 대해 파악하고 이를 해결하기 위해 포텐셜 필드를 활용하였다. n대의 로봇으로 다양한 대형 변경이 가능토록 시뮬레이션 환경을 조성, 39대의 탱크를 이용하여여 5가지의 각기 다른 대형으로의 변환을 새로이 제시한 알고리즘을 적용하여 구현하였다. 또한 실제 로봇 3대를 활용하여 알고리즘의 효용성에 대한 실험을 필두로 좁은 길목, 개활지 등 연속적으로 변화하는 환경 속에서의 구동을 통해 최종적으로 제시한 프레임워크의 타당성에 대해 검증하였다.We propose a novel framework for formation reconguration of multiple nonholonomic wheeled mobile robots (WMRs) in the changing driving environment. We utilize an onboard system of WMRs with the capability of sensing and computing. Each WMR has the same computing power for visualizing the driving environment, handling the sensing information and calculating the control action. One of the WMRs is the leader with the FPV camera and SLAM, while others with monocular cameras with limited FoV, as the followers, keep a certain desired formation during driving in a distributed manner. We set two control objectives, one is group driving and the other is holding the shape of the formation. We have to capture the control objectives separately and simultaneously, we make the best use of nonholonomic passive decomposition to split the WMRs' kinematics into those of the formation maintaining and group driving. The repulsive potential function to prevent the collision among WMRs and attractive potential function to restrict the boundary of follower WMRs' moving space due to limited FoV range of the monocular cameras while switching their formation are also used. Simulation with 39 tanks and experiments with three WMRs are also performed to verify the proposed framework.Acknowledgements iii List of Figures vii Abbreviations ix 1 Introduction 1 2 Formation Reconguration Control Design 5 2.1 Nonholonomic Passive Decomposition . . . . . . . . . . . . . . . 5 2.2 Attractive and Repulsive Potential Function . . . . . . . . . . . . 10 2.3 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3 Estimation and Predictive Display 20 3.1 Distributed Pose Estimation . . . . . . . . . . . . . . . . . . . . . 20 3.1.1 EKF Pose Estimation of Leader WMR . . . . . . . . . . . 20 3.1.2 EKF Pose Estimation of Follower WMRs . . . . . . . . . 22 3.2 Predictive Display for Distributed WMRs Teleoperation . . . . . 23 4 Experiment 27 4.1 Test Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Demonstrate the Proposed Algorithm . . . . . . . . . . . . . . . 30 4.3 Teleoperation Experiment with the Algorithm . . . . . . . . . . . 33 5 Conclusion 40Maste

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments

Cooperative Grasping Control of Multiple Nonholonomic Mobile Manipulators with Obstacle Avoidance

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2014. 2. 이동준.We present a novel cooperative grasping control framework for multiple kine- matic nonholonomic mobile manipulators. Our control framework enables mul- tiple mobile manipulators to drive the grasped object with velocity commands, while rigidly maintaining the grasping shape with no dedicated grasp-enforcing xtures. And also, obstacle avoidance framework either via their whole formation maneuver or internal formation reconguration is proposed. For this, nonholo- nomic passive decomposition [1, 2] is utilized to split the robots' motion into the three aspects (i.e., grasping shapegrasped object maneuverinternal motions) so that we can control these aspects simultaneously and separately. Peculiar dy- namics of the internal motions is exploited to achieve obstacle avoidance via the formation reconguration. Simulations are performed to support the theory.List of Figures iv Abbreviations vi Symbols vii 1 Introduction 1 1.1 Motivation and Objectives 1 1.2 Relevant Works 3 2 System Description 6 2.1 System Model 6 2.2 Grasping Map h(q) 9 3 Nonholonomic Passive Decomposition 12 3.1 Nonholonomic passive decomposition 12 3.1.1 Shape Distribution 14 3.1.2 Quotient Distribution 15 3.1.3 Locked Distribution 15 3.1.4 Properties of Modes 16 3.2 Grasping Control 23 4 Obstacle Avoidance 25 4.1 Obstacle Avoidance Control Design 25 4.1.1 Obstacle Avoidance via Whole Formation Control 26 4.1.2 Obstacle Avoidance via Internal Motion 29 4.1.3 Avoidance Feasibility 33 5 Conclusion and Future Work 38 5.1 Conclusion 38 5.2 Future Work 39 Bibliography 39 Acknowledgements 45Maste

Passivity-Based Trajectory Tracking and Formation Control of Nonholonomic Wheeled Robots Without Velocity Measurements

This note proposes a passivity-based control method for trajectory tracking and formation control of nonholonomic wheeled robots without velocity measurements. Coordinate transformations are used to incorporate the nonholonomic constraints, which are then avoided by controlling the front end of the robot rather than the center of the wheel axle into the differential equations. Starting from the passivity-based coordination design, the control goals are achieved via an internal controller for velocity tracking and heading control and an external controller for formation in the port-Hamiltonian framework. This approach endows the resulting controller with a physical interpretation. To avoid unavailable velocity measurements or unreliable velocity estimations, we derive the distributed control law with only position measurements by introducing a dynamic extension. In addition, we prove that our approach is suitable not only for acyclic graphs but also for a class of non-acyclic graphs, namely, ring graphs. Simulations are provided to illustrate the effectiveness of the approach

Control of Multiple Arm Systems With Rolling Constraints

When multiple arms are used to manipulate a large object, it is necessary to maintain and control contacts between the object and effector(s) on one or more arms. The contacts are characterized by holonomic as well as nonholonomic constraints. This paper addresses the control of mechanical systems subject to nonholonomic constraints, rolling constraints in particular. It has been shown that such a system is always controllable, but cannot be stabilized to a single equilibrium by smooth feedback [l, 2]. In this paper, we show that the system is not input-state linearizable though input-output linearization is possible with appropriate output equations. Further, if the system is position-controlled (i.e., the output equation is a functions of position variables only), it has a zero dynamics which is Lagrange stable but not asymptotically stable. We discuss the analysis and controller design for planar as well as spatial multi-arm systems and present results from computer simulations to demonstrate the theoretical results

The Mechanics and Control of Undulatory Robotic Locomotion

In this dissertation, we examine a formulation of problems of undulatory robotic locomotion within the context of mechanical systems with nonholonomic constraints and symmetries. Using tools from geometric mechanics, we study the underlying structure found in general problems of locomotion. In doing so, we decompose locomotion into two basic components: internal shape changes and net changes in position and orientation. This decomposition has a natural mathematical interpretation in which the relationship between shape changes and locomotion can be described using a connection on a trivial principal fiber bundle. We begin by reviewing the processes of Lagrangian reduction and reconstruction for unconstrained mechanical systems with Lie group symmetries, and present new formulations of this process which are easily adapted to accommodate external constraints. Additionally, important physical quantities such as the mechanical connection and reduced mass-inertia matrix can be trivially determined using this formulation. The presence of symmetries then allows us to reduce the necessary calculations to simple matrix manipulations. The addition of constraints significantly complicates the reduction process; however, we show that for invariant constraints, a meaningful connection can be synthesized by defining a generalized momentum representing the momentum of the system in directions allowed by the constraints. We then prove that the generalized momentum and its governing equation possess certain invariances which allows for a reduction process similar to that found in the unconstrained case. The form of the reduced equations highlights the synthesized connection and the matrix quantities used to calculate these equations. The use of connections naturally leads to methods for testing controllability and aids in developing intuition regarding the generation of various locomotive gaits. We present accessibility and controllability tests based on taking derivatives of the connection, and relate these tests to taking Lie brackets of the input vector fields. The theory is illustrated using several examples, in particular the examples of the snakeboard and Hirose snake robot. We interpret each of these examples in light of the theory developed in this thesis, and examine the generation of locomotive gaits using sinusoidal inputs and their relationship to the controllability tests based on Lie brackets