Parameter and Payload Identification of a 2-DOF Robotic Manipulator: An Algebraic Identification Approach

Este artículo aborda el problema de identificar los parámetros y las cargas útiles de un manipulador robótico 2-DOF. La metodología propuesta para nuestra investigación fue el método de identificación algebraica realizada en dos etapas: primero, identificando los parámetros del manipulador, y segundo, identificando la carga útil de la punta. Se utilizaron dos casos de simulación numérica diferentes para validar la metodología de identificación propuesta. En ambos casos se logró una rápida convergencia con un bajo porcentaje de error.This paper addresses the problem of identifying the parameters and the payloads of a 2-DOF robotic manipulator. The methodology proposed for our research was the algebraic identification method conducted in two stages: First, identifying the parameters of the manipulator and second, identifying the tip payload. Two different numerical simulation cases were used to validate the proposed identification methodology. In both cases, fast convergence was achieved with a low error percentage

구조로봇을 위한 강건한 계층적 동작 계획 및 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 박종우.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions. We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels. In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration. We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization. Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner. Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.최근에 등장한 새로운 세대의 로봇은 기존에는 인간만이 할 수 있었던 복잡한 일을 로봇 또한 수행할 수 있음을 보여주었다. 특히 DARPA Robotics Challenge를 통해 이러한 사실을 잘 확인할 수 있으며, 이 로봇들은 공장과 같은 정형화된 환경에서 자동화된 일을 반복적으로 수행하던 임무에서 더 나아가 극한의 환경에서 인간을 대신하여 위험한 임무를 수행할 수 있는 방향으로 발전하고 있다. 그래서 사람들은 재난환경에서 안전하고 시의 적절하게 대응할 수 있는 여러 가지 대안 중에서 실현 가능성이 높은 대처 방안으로 로봇을 생각하게 되었다. 하지만 이러한 로봇은 동적으로 변화하는 비정형 환경에서 임무를 수행할 수 있어야 하기 때문에 불확실성에 대해 강건해야하고, 다양한 환경 조건에서 능동적으로 반응을 할 수 있어야 한다. 본 학위논문에서는 로봇이 비정형 환경에서 강건하면서도 적응적으로 동작할 수 있는 실시간 최적화 기반의 동작 계획 및 제어 방법과 구조 로봇의 설계 개념을 제안하고자 한다. 인간은 많은 자유도를 가지고 있으며, 하나의 전신 동작을 생성할 때 다양한 기구학 혹은 동역학적 특성을 가지는 세부 동작 혹은 작업을 정의하고, 이를 효과적으로 종합할 수 있다. 그리고 학습을 통해 각 동작 요소들을 최적화할 뿐만 아니라 상황 에 따라 각 동작 요소에 우선순위를 부여하여 이를 효과적으로 결합하거나 분리하여 실시간으로 최적의 동작을 생성하고 제어한다. 즉, 상황에 따라 중요한 동작요소를 우선적으로 수행하고 우선순위가 낮은 동작요소는 부분 혹은 전체적으로 포기하기도 하면서 매우 유연하게 전체 동작을 생성하고 최적화 한다. 인간과 같이 다자유도를 보유한 로봇 또한 기구학과 동역학적 특성을 가지는 다양한 세부 동작 혹은 작업을 작업공간(task space) 혹은 관절공간(configuration space)에서 정의할 수 있으며, 우선순위에 따라 이를 효과적으로 결합하여 전체 동작을 생 성하고 제어할 수 있다. 서로 양립하기 어려운 로봇의 동작 문제를 해결하기 위해 동작들 사이에 우선순위를 부여하여 계층을 생성하고, 이에 따라 로봇의 전신 동작을 구현하는 방법은 오랫동안 연구가 진행되어 왔다. 이러한 계층적 최적화를 이용하면 우선순위가 높은 동작부터 순차적으로 실행하지만, 우선순위가 낮은 동작요소들도 가능한 만족시키는 최적의 해를 찾을 수 있다. 하지만 관절의 구동 범위와 같은 부등식의 조건이 포함된 계층적 최적화 문제에서 특이점에 대한 강건성까지 확보할 수 있는 방법에 대해서는 아직까지 많은 부분이 밝 혀진 바가 없다. 따라서 본 학위논문에서는 등식과 부등식으로 표현되는 구속조건 혹은 동작요소를 계층적 최적화에 동시에 포함시키고, 특이점이 존재하더라도 강건성과 수렴성을 보장하는 관절공간에서의 최적해를 확보하는데 집중한다. 왜나하면 비정형 임무를 수행하는 로봇은 사전에 계획된 동작을 수행하는 것이 아닌 변화하는 환경조건에 따라 실시간으로 동작을 계획하고 제어해야 하기 때문에 특이점이 없는 자세로 로봇을 항상 제어하기가 어렵다. 그리고 이렇게 특이점을 회피하는 방향으로 로봇을 제어하는 것은 로봇의 운용성을 심각하게 저해시킬 수 있다. 특이점 근방에서의 해의 강건성이 보장되지 않으면 로봇 관절에 과도한 속도 혹은 토크가 발생하여 로봇의 임무 수행이 불가능하거나 환경과 로봇의 손상을 초래할 수 있으며, 나아가 로봇과 함께 임무를 수행하는 사람에게 상해를 가할 수도 있다. 특이점에 대한 강건성을 확보하기 위해 우선순위 기반의 계층적 최적화와 정규화 (regularization)를 통합하여 정규화된 계층적 최적화 (RHQP: Regularized Hierarchical Quadratic Program) 문제를 다룬다. 부등식이 포함된 계층적 최적화에 정규화를 동시에 고려함으로써 야기되는 많은 문제점들을 해결하고 해의 최적성과 강건성을 확보할 수 있는 방법을 제안한다. 특히 외부의 최적화 프로그램을 사용하지 않고 수치적 최적화 (numerical optimization) 이론과 우선순위에 기반을 두는 여유자유도 로봇의 해석 기법을 이용하여 계산의 효율성을 극대화할 수 있는 이차 프로그램(quadratic programming)을 제안한다. 또한 이와 동시에 정규화된 계층적 최적화 문제의 이론적 구조를 철저하게 분석한다. 특히 특이값 분해 (singular value decomposition)를 통해 최적해와 부등식 조건을 처리하는데 필요한 라그랑지 승수를 재귀적인 방법으로 해석적 형태로 구함으로써 계산의 효율성을 증대시키고 동시에 부등식의 조건을 오류 없이 정확하게 처리할 수 있도록 하였다. 그리고 정규화된 계층적 최적화를 힘제어까지 확장하여 환경과 로봇의 안전한 상호작용을 보장하여 로봇이 적절한 힘으로 환경과 접촉할 수 있도록 하였다. 불확실성이 존재하는 비정형 환경에서 비정형 임무를 수행할 수 있는 구조로봇의 핵심 설계 개념을 제시한다. 비정형 환경에서의 조작 성능과 이동 성능을 동시에 확보할 수 있는 형상으로 로봇을 설계하여 구조 로봇으로 하여금 최종 목적으로 설정된 인간을 대신하여 부상자를 구조하고 위험물을 처리하는 임무를 효과적으로 수행할 수 있도록 한다. 구조 로봇에 필요한 매니퓰레이터는 부상자 구조 임무와 위험물 처리 임무에 따라 교체 가능한 모듈형으로 설계하여 각각의 임무에 따라 최적화된 매니퓰 레이터를 장착하여 임무를 수행할 수 있다. 하체는 트랙과 관절이 결합된 하이브리드 형태를 취하고 있으며, 주행 임무와 조작임무에 따라 형상을 변경할 수 있다. 형상 변경과 모듈화된 매니퓰레이터를 통해서조작 성능과 험한 지형에서 이동할 수 있는 주행 성능을 동시에 확보하였다. 최종적으로 구조로봇의 설계와 실시간 계층적 제어를 이용하여 비정형 실내외 환경에서 구조로봇이 주행임무, 위험물 조작임무, 부상자 구조 임무를 성공적으로 수 행할 수 있음을 해석과 실험을 통하여 입증함으로써 본 학위논문에서 제안한 설계와 정규화된 계층적 최적화 기반의 제어 전략의 유용성을 검증하였다.1 Introduction 1 1.1 Motivations 1 1.2 Related Works and Research Problems for Hierarchical Control 3 1.2.1 Classical Approaches 3 1.2.2 State-of-the-Art Strategies 4 1.2.3 Research Problems 7 1.3 Robust Rescue Robots 9 1.4 Research Goals 12 1.5 Contributions of ThisThesis 13 1.5.1 Robust Hierarchical Task-Priority Control 13 1.5.2 Design Concepts of Robust Rescue Robot 16 1.5.3 Hierarchical Motion and ForceControl 17 1.6 Dissertation Preview 18 2 Preliminaries for Task-Priority Control Framework 21 2.1 Introduction 21 2.2 Task-Priority Inverse Kinematics 23 2.3 Recursive Formulation of Null Space Projector 28 2.4 Conclusion 31 3 Robust Hierarchical Task-Priority Control 33 3.1 Introduction 33 3.1.1 Motivations 35 3.1.2 Objectives 36 3.2 Task Function Approach 37 3.3 Regularized Hierarchical Optimization with Equality Tasks 41 3.3.1 Regularized Hierarchical Optimization 41 3.3.2 Optimal Solution 45 3.3.3 Task Error and Hierarchical Matrix Decomposition 49 3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56 3.4 Regularized Hierarchical Optimization with Inequality Constraints 60 3.4.1 Lagrange Multipliers 61 3.4.2 Modified Active Set Method 66 3.4.3 Illustrative Examples of Modified Active Set Method 70 3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72 3.5 DLS-HQP Algorithm 79 3.6 Concluding Remarks 80 4 Rescue Robot Design and Experimental Results 83 4.1 Introduction 83 4.2 Rescue Robot Design 85 4.2.1 System Design 86 4.2.2 Variable Configuration Mobile Platform 92 4.2.3 Dual Arm Manipulators 95 4.2.4 Software Architecture 97 4.3 Performance Verification for Hierarchical Motion Control 99 4.3.1 Real-Time Motion Generation 99 4.3.2 Task Specifications 103 4.3.3 Singularity Robust Task Priority 106 4.3.4 Inequality Constraint Handling and Computation Time 111 4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117 4.5 Field Tests 122 4.6 Concluding Remarks 126 5 Hierarchical Motion and Force Control 129 5.1 Introduction 129 5.2 Operational Space Control 132 5.3 Acceleration-Based Hierarchical Motion Control 134 5.4 Force Control 137 5.4.1 Force Control with Inner Position Loop 141 5.4.2 Force Control with Inner Velocity Loop 144 5.5 Motion and Force Control 145 5.6 Numerical Results for Acceleration-Based Motion and Force Control 148 5.6.1 Task Specifications 150 5.6.2 Force Control Performance 151 5.6.3 Singularity Robustness and Inequality Constraint Handling 155 5.7 Velocity Resolved Motion and Force Control 160 5.7.1 Velocity-Based Motion and Force Control 161 5.7.2 Experimental Results 163 5.8 Concluding Remarks 167 6 Conclusion 169 6.1 Summary 169 6.2 Concluding Remarks 173 A Appendix 175 A.1 Introduction to PID Control 175 A.2 Inverse Optimal Control 176 A.3 Experimental Results and Conclusion 181 Bibliography 183 Abstract 207박

Line-of-sight-stabilization and tracking control for inertial platforms

Nowadays, line of sight stabilization and tracking using inertially stabilized platforms (ISPs) are still challenging engineering problems. With a growing demand for high-precision applications, more involved control techniques are necessary to achieve better performance. In this work, kinematic and dynamic models for a three degrees-of-freedom ISP are presented. These models are based in the vehicle-manipulator system (VMS) framework for modeling of robot manipulators operating in a mobile base (vehicles). The dynamic model follows the Euler-Lagrange formulation and is implemented by numeric simulations using the iterative Newton-Euler method. Two distinct control strategies for both stabilization and tracking are proposed: (i) computed torque control and (ii) sliding mode control using the recent SuperTwisting Algorithm (STA) combined with a High-Order Sliding Mode Observer (HOSMO). Simulations using data from a simulated vessel allow us to compare the performance of the computed torque controllers with respect to the commonly used P-PI controller. Besides, the results obtained for the sliding mode controllers indicate that the Super-Twisting algorithm offers ideal robustness to the vehicle motion disturbances and also to parametric uncertainties, resulting in a stabilization precision of approximately 0,8 mrad.Hoje em dia, a estabilização e o rastreamento da linha de visada utilizando plataformas inerciais continuam a constituir desafiadores problemas de engenharia. Com a crescente demanda por aplicações de alta precisão, técnicas de controle complexas são necessárias para atingir melhor desempenho. Neste trabalho, modelos cinemáticos e dinâmicos para uma plataforma mecânica de estabilização inercial são apresentados. Tais modelos se baseiam no formalismo para sistemas veículo-manipulator para a modelagem de manipuladores robóticos operando em uma base móvel (veículo). O modelo dinâmico apresentado segue a formulação analítica de Euler-Lagrange e é implementado em simulações numéricas através do método iterativo de Newton-Euler. Duas estratégias de controle distintas para estabilização e rastreamento são propostas: (i) controle por torque-computado e (ii) controle por modos deslizantes utilizando o recente algoritmo Super-Twisting combinado com um observador baseado em modos deslizantes de alta ordem. Simulações utilizando dados de movimentação de um navio simulado permitem comparar o desempenho dos controladores por torque computado em relação a um tipo comum de controlador linear utilizado na literatura: o P-PI. Além disso, os resultados obtidos para o controle por modos deslizantes permitem concluir que o algoritmo Super-Twisting apresenta rejeição ideal a perturbações provenientes do movimento do veículo e também a incertezas paramétricas, resultando em precisão de estabilização de aproximadamente 0,8 mrad

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users

Hybrid walking therapy with fatigue management for spinal cord injured individuals

In paraplegic individuals with upper motor neuron lesions the descending path for signals from central nervous system to the muscles are lost or diminished. Motor neuroprosthesis based on electrical stimulation can be applied to induce restoration of motor function in paraplegic patients. Furthermore, electrical stimulation of such motor neuroprosthesis can be more efficiently managed and delivered if combined with powered exoskeletons that compensate the limited force in the stimulated muscles and bring additional support to the human body. Such hybrid overground gait therapy is likely to be more efficient to retrain the spinal cord in incomplete injuries than conventional, robotic or neuroprosthetic approaches. However, the control of bilateral joints is difficult due to the complexity, non-linearity and time-variance of the system involved. Also, the effects of muscle fatigue and spasticity in the stimulated muscles complicate the control task. Furthermore, a compliant joint actuation is required to allow for a cooperative control approach that is compatible with the assist-as-needed rehabilitation paradigm. These were direct motivations for this research. The overall aim was to generate the necessary knowledge to design a novel hybrid walking therapy with fatigue management for incomplete spinal cord injured subjects. Research activities were conducted towards the establishment of the required methods and (hardware and software) systems that required to proof the concept with a pilot clinical evaluation. Speciffically, a compressive analysis of the state of the art on hybrid exoskeletons revealed several challenges which were tackled by this dissertation. Firstly, assist-as-needed was implemented over the basis of a compliant control of the robotic exoskeleton and a closed-loop control of the neuroprosthesis. Both controllers are integrated within a hybrid-cooperative strategy that is able to balance the assistance of the robotic exoskeleton regarding muscle performance. This approach is supported on the monitoring of the leg-exoskeleton physical interaction. Thus the fatigue caused by neuromuscular stimulation was also subject of speciffic research. Experimental studies were conducted with paraplegic patients towards the establishment of an objective criteria for muscle fatigue estimation and management. The results of these studies were integrated in the hybrid-cooperative controller in order to detect and manage muscle fatigue while providing walking therapy. Secondly closed-loop control of the neuroprosthesis was addressed in this dissertation. The proposed control approach allowed to tailor the stimulation pattern regarding the speciffic residual motor function of the lower limb of the patient. In order to uncouple the closed-loop control from muscle performance monitoring, the hybrid-cooperative control approach implemented a sequential switch between closed-loop and open-loop control of the neuroprosthesis. Lastly, a comprehensive clinical evaluation protocol allowed to assess the impact of the hybrid walking therapy on the gait function of a sample of paraplegic patients. Results demonstrate that: 1) the hybrid controller adapts to patient residual function during walking, 2) the therapy is tolerated by patients, and 3) the walking function of patients was improved after participating in the study. In conclusion, the hybrid walking therapy holds potential for rehabilitate walking in motor incomplete paraplegic patients, guaranteeing further research on this topic. This dissertation is framed within two research projects: REHABOT (Ministerio de Ciencia e Innovación, grant DPI2008-06772-C03-02) and HYPER (Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, grant CSD2009-00067 CONSOLIDER INGENIO 2010). Within these research projects, cutting-edge research is conducted in the eld of hybrid actuation and control for rehabilitation of motor disorders. This dissertation constitutes proof-of concept of the hybrid walking therapy for paraplegic individuals for these projects. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------En individuos parapléjicos con lesiones de la motoneurona superior, la conexión descendente para la transmisión de las señales del sistema nervioso central a los músculos se ve perdida o disminuida. Las neuroprótesis motoras basadas en la estimulación eléctrica pueden ser aplicadas para inducir la restauración de la función motora en pacientes con paraplejia. Además, la estimulación eléctrica de tales neuroprótesis motoras se puede gestionar y aplicar de manera más eficiente mediante la combinación con exoesqueletos robóticos que compensen la generación limitada de fuerza de los músculos estimulados, y proporcionen soporte adicional para el cuerpo. Dicha terapia de marcha ambulatoria puede ser probablemente más eficaz para la recuperación de las funciones de la médula espinal en lesiones incompletas que las terapias convencionales, robóticas o neuroprotesicas. Sin embargo, el control bilateral de las articulaciones es difícil debido a la complejidad, no-linealidad y la variación con el tiempo de las características del sistema en cuestión. Además, la fatiga muscular y la espasticidad de los músculos estimulados complican la tarea de control. Por otra parte, se requiere una actuación robótica modulable para permitir un enfoque de control cooperativo compatible con el paradigma de rehabilitación de asistencia bajo demanda. Todo lo anterior constituyó las motivaciones directas para esta investigación. El objetivo general fue generar el conocimiento necesario para diseñar un nuevo tratamiento híbrido de rehabilitación marcha con gestión de la fatiga para lesionados medulares incompletos. Se llevaron a cabo actividades de investigación para el establecimiento de los métodos necesarios y los sistemas (hardware y software) requeridos para probar el concepto mediante una evaluación clínica piloto. Específicamente, un análisis del estado de la técnica sobre exoesqueletos híbridos reveló varios retos que fueron abordados en esta tesis. En primer lugar, el paradigma de asistencia bajo demanda se implementó sobre la base de un control adaptable del exoesqueleto robótico y un control en lazo cerrado de la neuroprótesis. Ambos controladores están integrados dentro de una estrategia híbrida cooperativa que es capaz de equilibrar la asistencia del exoesqueleto robótico en relación con el rendimiento muscular. Este enfoque se soporta sobre la monitorización de la interacción física entre la pierna y el exoesqueleto. Por tanto, la fatiga causada por la estimulación neuromuscular también fue objeto de una investigación específica. Se realizaron estudios experimentales con pacientes parapléjicos para el establecimiento de un criterio objetivo para la detección y la gestión de la fatiga muscular. Los resultados de estos estudios fueron integrados en el controlador híbrido-cooperativo con el fin de detectar y gestionar la fatiga muscular mientras se realiza la terapia híbrida de rehabilitación de la marcha. En segundo lugar, el control en lazo cerrado de la neuroprótesis fue abordado en esta tesis. El método de control propuesto permite adaptar el patrón de estimulación en relación con la funcionalidad residual específica de la extremidad inferior del paciente. Sin embargo, con el n de desacoplar el control en lazo cerrado de la monitorización del rendimiento muscular, el enfoque de control híbrido-cooperativo incorpora una conmutación secuencial entre el control en lazo cerrado y en lazo abierto de la neuropr otesis. Por último, un protocolo de evaluación clínica global permitido evaluar el impacto de la terapia híbrida de la marcha en la función de la marcha de una muestra de pacientes parapléjicos. Los resultados demuestran que: 1) el controlador híbrido se adapta a la función residual del paciente durante la marcha, 2) la terapia es tolerada por los pacientes, y 3) la funci on de marcha del paciente mejora despu es de participar en el estudio. En conclusión, la terapia de híbrida de la marcha alberga un potencial para la rehabilitación de la marcha en pacientes parapléjicos incompletos motor, garantizando realizar investigación más profunda sobre este tema. Esta tesis se enmarca dentro de los dos proyectos de investigación: REHABOT (Ministerio de Ciencia e Innovación, referencia DPI2008-06772-C03-02) y HYPER (Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, referencia CSD2009-00067 CONSOLIDER INGENIO 2010). Dentro de estos proyectos se lleva a cabo investigación de vanguardia en el campo de la actuación y el control híbrido de la combinación robot-neuroprótesis para la rehabilitación de trastornos motores. Esta tesis constituye la prueba de concepto de la terapia de híbrida de la marcha para individuos parapléjicos en estos proyectos.This dissertation is framed within two research projects: REHABOT (Ministerio de Ciencia e Innovación, grant DPI2008-06772-C03-02) and HYPER (Hybrid Neuroprosthetic and Neurorobotic Devices for Functional Compensation and Rehabilitation of Motor Disorders, grant CSD2009-00067 CONSOLIDER INGENIO 2010

Motion Planning and Control of Dynamic Humanoid Locomotion

Inspired by human, humanoid robots has the potential to become a general-purpose platform that lives along with human. Due to the technological advances in many field, such as actuation, sensing, control and intelligence, it finally enables humanoid robots to possess human comparable capabilities. However, humanoid locomotion is still a challenging research field. The large number of degree of freedom structure makes the system difficult to coordinate online. The presence of various contact constraints and the hybrid nature of locomotion tasks make the planning a harder problem to solve. Template model anchoring approach has been adopted to bridge the gap between simple model behavior and the whole-body motion of humanoid robot. Control policies are first developed for simple template models like Linear Inverted Pendulum Model (LIPM) or Spring Loaded Inverted Pendulum(SLIP), the result controlled behaviors are then been mapped to the whole-body motion of humanoid robot through optimization-based task-space control strategies. Whole-body humanoid control framework has been verified on various contact situations such as unknown uneven terrain, multi-contact scenarios and moving platform and shows its generality and versatility. For walking motion, existing Model Predictive Control approach based on LIPM has been extended to enable the robot to walk without any reference foot placement anchoring. It is kind of discrete version of \u201cwalking without thinking\u201d. As a result, the robot could achieve versatile locomotion modes such as automatic foot placement with single reference velocity command, reactive stepping under large external disturbances, guided walking with small constant external pushing forces, robust walking on unknown uneven terrain, reactive stepping in place when blocked by external barrier. As an extension of this proposed framework, also to increase the push recovery capability of the humanoid robot, two new configurations have been proposed to enable the robot to perform cross-step motions. For more dynamic hopping and running motion, SLIP model has been chosen as the template model. Different from traditional model-based analytical approach, a data-driven approach has been proposed to encode the dynamics of the this model. A deep neural network is trained offline with a large amount of simulation data based on the SLIP model to learn its dynamics. The trained network is applied online to generate reference foot placements for the humanoid robot. Simulations have been performed to evaluate the effectiveness of the proposed approach in generating bio-inspired and robust running motions. The method proposed based on 2D SLIP model can be generalized to 3D SLIP model and the extension has been briefly mentioned at the end

Application of reinforcement learning in robotic disassembly operations

Disassembly is a key step in remanufacturing. To increase the level of automation in disassembly, it is necessary to use robots that can learn to perform new tasks by themselves rather than having to be manually reprogrammed every time there is a different job. Reinforcement Learning (RL) is a machine learning technique that enables the robots to learn by trial and error rather than being explicitly programmed. In this thesis, the application of RL to robotic disassembly operations has been studied. Firstly, a literature review on robotic disassembly and the application of RL in contact-rich tasks has been conducted in Chapter 2. To physically implement RL in robotic disassembly, the task of removing a bolt from a door chain lock has been selected as a case study, and a robotic training platform has been built for this implementation in Chapter 3. This task is chosen because it can demonstrate the capabilities of RL to pathfinding and dealing with reaction forces without explicitly specifying the target coordinates or building a force feedback controller. The robustness of the learned policies against the imprecision of the robot is studied by a proposed method to actively lower the precision of the robots. It has been found that the robot can learn successfully even when the precision is lowered to as low as ±0.5mm. This work also investigates whether learned policies can be transferred among robots with different precisions. Experiments have been performed by training a robot with a certain precision on a task and replaying the learned skills on a robot with different precision. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision, as it is suspected that the policies trained on high-precision robots have been overfitted to the precise robots. In Chapter 4, the approach of using a digital-twin-assisted simulation-to-reality transfer to accelerate the learning performance of the RL has been investigated. To address the issue of identifying the system parameters, such as the stiffness and damping of the contact models, that are difficult to measure directly but are critical for building the digital twins of the environments, system identification method is used to minimise the discrepancy between the response generated from the physical and digital environments by using the Bees Algorithm. It is found that the proposed method effectively increases RL's learning performance. It is also found that it is possible to have worse performance with the sim-to-real transfer if the reality gap is not effectively addressed. However, increasing the size of the dataset and optimisation cycles have been demonstrated to reduce the reality gap and lead to successful sim-to-real transfers. Based on the training task described in Chapters 4 and 5, a full factorial study has been conducted to identify patterns when selecting the appropriate hyper-parameters when applying the Deep Deterministic Policy Gradient (DDPG) algorithm to the robotic disassembly task. Four hyper-parameters that directly influence the decision-making Artificial Neural Network (ANN) update have been chosen for the study, with three levels assigned to each hyper-parameter. After running 241 simulations, it is found that for this particular task, the learning rates of the actor and critic networks are the most influential hyper-parameters, while the batch size and soft update rate have relatively limited influence. Finally, the thesis is concluded in Chapter 6 with a summary of findings and suggested future research directions

Switching control systems and their design automation via genetic algorithms

The objective of this work is to provide a simple and effective nonlinear controller. Our strategy involves switching the underlying strategies in order to maintain a robust control. If a disturbance moves the system outside the region of stability or the domain of attraction, it will be guided back onto the desired course by the application of a different control strategy. In the context of switching control, the common types of controller present in the literature are based either on fuzzy logic or sliding mode. Both of them are easy to implement and provide efficient control for non-linear systems, their actions being based on the observed input/output behaviour of the system. In the field of fuzzy logic control (FLC) using error feedback variables there are two main problems. The first is the poor transient response (jerking) encountered by the conventional 2-dimensional rule-base fuzzy PI controller. Secondly, conventional 3-D rule-base fuzzy PID control design is both computationally intensive and suffers from prolonged design times caused by a large dimensional rule-base. The size of the rule base will increase exponentially with the increase of the number of fuzzy sets used for each input decision variable. Hence, a reduced rule-base is needed for the 3-term fuzzy controller. In this thesis a direct implementation method is developed that allows the size of the rule-base to be reduced exponentially without losing the features of the PID structure. This direct implementation method, when applied to the reduced rule-base fuzzy PI controller, gives a good transient response with no jerking