Pattern generators with sensory feedback for the control of quadruped locomotion

Central Pattern Generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environmen

Adaptive quadruped locomotion: learning to detect and avoid an obstacle

Dissertação de mestrado em Engenharia de InformáticaAutonomy and adaptability are key features in the design and construction of a robotic system capable of carrying out tasks in an unstructured and not predefined environment. Such features are generally observed in animals, biological systems that usually serve as an inspiration models to the design of robotic systems. The autonomy and adaptability of these biological systems partially arises from their ability to learn. Animals learn to move and control their own body when young, they learn to survive, to hunt and avoid undesirable situations, from their progenitors. There has been an increasing interest in defining a way to endow these abilities into the design and creation of robotic systems. This dissertation proposes a mechanism that seeks to create a learning module to a quadruped robot controller that enables it to both, detect and avoid an obstacle in its path. The detection is based on a Forward Internal Model (FIM) trained online to create expectations about the robot’s perceptive information. This information is acquired by a set of range sensors that scan the ground in front of the robot in order to detect the obstacle. In order to avoid stepping on the obstacle, the obstacle detections are used to create a map of responses that will change the locomotion according to what is necessary. The map is built and tuned every time the robot fails to step over the obstacle and defines how the robot should act to avoid these situations in the future. Both learning tasks are carried out online and kept active after the robot has learned, enabling the robot to adapt to possible new situations. The proposed architecture was inspired on [14, 17], but applied here to a quadruped robot with different sensors and specific sensor configuration. Also, the mechanism is coupled with the robot’s locomotion generator based in Central Pattern Generators (CPG)s presented in [22]. In order to achieve its goal, the controller send commands to the CPG so that the necessary changes in the locomotion are applied. Results showed the success in both learning tasks. The robot was able to detect the obstacle, and change its locomotion with the acquired information at collision time.Autonomia e capacidade de adaptação são características chave na criação de sistemas robóticos capazes de levar a cabo diversas tarefas em ambientes não especificamente preparados nem configurados para tal. Estas características são comuns nos animais, sistemas biológicos que muitas vezes servem de modelo e inspiração para desenhar e construir sistemas robóticos. A autonomia e adaptabilidade destes sistemas advém parcialmente da sua capacidade de aprender. Quando ainda jovens, os animais aprendem a controlar o seu corpo e a movimentar-se, muitos mamíferos aprendem a caçar e a sobreviver com os seus progenitores. Ultimamente tem havido um crescente interesse em como aplicar estas características no desenho e criação de sistemas robóticos. Nesta dissertação é proposto um mecanismo que permita que um robô quadrúpede seja capaz de detectar e evitar um obstáculo no seu caminho. A detecção é baseada num Forward Internal Model (FIM) que aprende a prever os valores dos sensores de percepção do robô, os quais procuram detectar obstáculos no seu caminho. Por forma a evitar os obstáculos, os sinais de detecção dos obstáculos são usados na criação de um mapa que permitirá ao robô alterar a sua locomoção mediante o que é necessário. Este mapa é construído à medida que o robô falha e tropeça no obstáculo. Nesse momento, o mapa é definido para que tal situação seja evitada no futuro. Ambos os processos de aprendizagem são levados a cabo em tempo de execução e mantêm esse processo activo por forma a possibilitar a readaptação a eventuais novas situações. Este mecanismo foi inspirado nos trabalhos [14, 17], mas aplicados aqui a um quadrúpede com uma configuração de sensores diferente e específica. O mecanismo será interligado ao gerador da locomoção, baseado em Control Pattern Generator (CPG) proposto em [22]. Por forma a atingir o objectivo final, o controlador irá enviar comandos para o CPG a fim da locomoção ser alterada como necessário. Os resultados obtidos mostram o sucesso em ambos os processos de aprendizagem. O robô é capaz de detectar o obstáculo e alterar a sua locomção de acordo com a informação adquirida nos momentos de colisão com o mesmo, conseguindo efectivamente passar por cima do obstáculo sem nenhum tipo de colisão

A Bio-inspired architecture for adaptive quadruped locomotion over irregular terrain

Tese de doutoramento Programa Doutoral em Engenharia Electrónica e de ComputadoresThis thesis presents a tentative advancement on walking control of small quadruped and humanoid position controlled robots, addressing the problem of walk generation by combining dynamical systems approach to motor control, insights from neuroethology research on vertebrate motor control and computational neuroscience. Legged locomotion is a complex dynamical process, despite the seemingly easy and natural behavior of the constantly present proficiency of legged animals. Research on locomotion and motor control in vertebrate animals from the last decades has brought to the attention of roboticists, the potential of the nature’s solutions to robot applications. Recent knowledge on the organization of complex motor generation and on mechanics and dynamics of locomotion has been successfully exploited to pursue agile robot locomotion. The work presented on this manuscript is part of an effort on the pursuit in devising a general, model free solution, for the generation of robust and adaptable walking behaviors. It strives to devise a practical solution applicable to real robots, such as the Sony’s quadruped AIBO and Robotis’ DARwIn- OP humanoid. The discussed solutions are inspired on the functional description of the vertebrate neural systems, especially on the concept of Central Pattern Generators (CPGs), their structure and organization, components and sensorimotor interactions. They use a dynamical systems approach for the implementation of the controller, especially on the use of nonlinear oscillators and exploitation of their properties. The main topics of this thesis are divided into three parts. The first part concerns quadruped locomotion, extending a previous CPG solution using nonlinear oscillators, and discussing an organization on three hierarchical levels of abstraction, sharing the purpose and knowledge of other works. It proposes a CPG solution which generates the walking motion for the whole-leg, which is then organized in a network for the production of quadrupedal gaits. The devised solution is able to produce goal-oriented locomotion and navigation as directed through highlevel commands from local planning methods. In this part, active balance on a standing quadruped is also addressed, proposing a method based on dynamical systems approach, exploring the integration of parallel postural mechanisms from several sensory modalities. The solutions are all successfully tested on the quadruped AIBO robot. In the second part, is addressed bipedal walking for humanoid robots. A CPG solution for biped walking based on the concept of motion primitives is proposed, loosely based on the idea of synergistic organization of vertebrate motor control. A set of motion primitives is shown to produce the basis of simple biped walking, and generalizable to goal-oriented walking. Using the proposed CPG, the inclusion of feedback mechanisms is investigated, for modulation and adaptation of walking, through phase transition control according to foot load information. The proposed solution is validated on the humanoid DARwIn-OP, and its application is evaluated within a whole-body control framework. The third part sidesteps a little from the other two topics. It discusses the CPG as having an alternative role to direct motor generation in locomotion, serving instead as a processor of sensory information for a feedback based motor generation. In this work a reflex based walking controller is devised for the compliant quadruped Oncilla robot, to serve as purely feedback based walking generation. The capabilities of the reflex network are shown in simulations, followed by a brief discussion on its limitations, and how they could be improved by the inclusion of a CPG.Esta tese apresenta uma tentativa de avanço no controlo de locomoção para pequenos robôs quadrúpedes e bipedes controlados por posição, endereçando o problema de geração motora através da combinação da abordagem de sistemas dinâmicos para o controlo motor, e perspectivas de investigação neuroetologia no controlo motor vertebrado e neurociência computacional. Andar é um processo dinâmico e complexo, apesar de parecer um comportamento fácil e natural devido à presença constante de animais proficientes em locomoção terrestre. Investigação na área da locomoção e controlo motor em animais vertebrados nas últimas decadas, trouxe à atenção dos roboticistas o potencial das soluções encontradas pela natureza aplicadas a aplicações robóticas. Conhecimento recente relativo à geração de comportamentos motores complexos e da mecânica da locomoção tem sido explorada com sucesso na procura de locomoção ágil na robótica. O trabalho apresentado neste documento é parte de um esforço no desenho de uma solução geral, e independente de modelos, para a geração robusta e adaptável de comportamentos locomotores. O foco é desenhar uma solução prática, aplicável a robôs reais, tal como o quadrúpede Sony AIBO e o humanóide DARwIn-OP. As soluções discutidas são inspiradas na descrição funcional do sistema nervoso vertebrado, especialmente no conceito de Central Pattern Generators (CPGs), a sua estrutura e organização, componentes e interacção sensorimotora. Estas soluções são implementadas usando uma abordagem em sistemas dinâmicos, focandos o uso de osciladores não lineares e a explorando as suas propriedades. Os tópicos principais desta tese estão divididos em três partes. A primeira parte explora o tema de locomoção quadrúpede, expandindo soluções prévias de CPGs usando osciladores não lineares, e discutindo uma organização em três níveis de abstracção, partilhando as ideias de outros trabalhos. Propõe uma solução de CPG que gera os movimentos locomotores para uma perna, que é depois organizado numa rede, para a produção de marcha quadrúpede. A solução concebida é capaz de produzir locomoção e navegação, comandada através de comandos de alto nível, produzidos por métodos de planeamento local. Nesta parte também endereçado o problema da manutenção do equilíbrio num robô quadrúpede parado, propondo um método baseado na abordagem em sistemas dinâmicos, explorando a integração de mecanismos posturais em paralelo, provenientes de várias modalidades sensoriais. As soluções são todas testadas com sucesso no robô quadrupede AIBO. Na segunda parte é endereçado o problema de locomoção bípede. É proposto um CPG baseado no conceito de motion primitives, baseadas na ideia de uma organização sinergética do controlo motor vertebrado. Um conjunto de motion primitives é usado para produzir a base de uma locomoção bípede simples e generalizável para navegação. Esta proposta de CPG é usada para de seguida se investigar a inclusão de mecanismos de feedback para modulação e adaptação da marcha, através do controlo de transições entre fases, de acordo com a informação de carga dos pés. A solução proposta é validada no robô humanóide DARwIn-OP, e a sua aplicação no contexto do framework de whole-body control é também avaliada. A terceira parte desvia um pouco dos outros dois tópicos. Discute o CPG como tendo um papel alternativo ao controlo motor directo, servindo em vez como um processador de informação sensorial para um mecanismo de locomoção puramente em feedback. Neste trabalho é desenhado um controlador baseado em reflexos para a geração da marcha de um quadrúpede compliant. As suas capacidades são demonstradas em simulação, seguidas por uma breve discussão nas suas limitações, e como estas podem ser ultrapassadas pela inclusão de um CPG.The presented work was possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant SFRH/BD/62047/2009

Control of legged locomotion using dynamical systems:design methods and adaptive frequency oscillators

Legged robots have gained an increased attention these past decades since they offer a promising technology for many applications in unstructured environments where the use of wheeled robots is clearly limited. Such applications include exploration and rescue tasks where human intervention is difficult (e.g. after a natural disaster) or impossible (e.g. on radioactive sites) and the emerging domain of assistive robotics where robots should be able to meaningfully and efficiently interact with humans in their environment (e.g. climbing stairs). Moreover the technology developed for walking machines can help designing new rehabilitation devices for disabled persons such as active prostheses. However the control of agile legged locomotion is a challenging problem that is not yet solved in a satisfactory manner. By taking inspiration from the neural control of locomotion in animals, we develop in this thesis controllers for legged locomotion. These controllers are based on the concept of Central Pattern Generators (CPGs), which are neural networks located in the spine of vertebrates that generate the rhythmic patterns that control locomotion. The use of a strong mathematical framework, namely dynamical systems theory, allows one to build general design methodologies for such controllers. The original contributions of this thesis are organized along three main axes. The first one is a work on biological locomotion and more specifically on crawling human infants. Comparisons of the detailed kinematics and gait pattern of crawling infants with those of other quadruped mammals show many similarities. This is quite surprising since infant morphology is not well suited for quadruped locomotion. In a second part, we use some of these findings as an inspiration for the design of our locomotion controllers. We try to provide a systematic design methodology for CPGs. Specifically we design an oscillator to independently control the swing and stance durations during locomotion, then using insights from dynamical systems theory we construct generic networks supporting different gaits and finally we integrate sensory feedback in the system. Experiments on three different simulated quadruped robots show the effectiveness of the approach. The third axis of research focus on dynamical systems theory and more specifically on the development of an adaptive mechanism for oscillators such that they can learn the frequency of any periodic signal. Interestingly this mechanism is generic enough to work with a large class of oscillators. Extensive mathematical analysis are provided in order to understand the fundamental properties of this mechanism. Then an extension to pools of adaptive frequency oscillators with a negative feedback loop is used to build programmable CPGs (i.e. CPGs that can encode any periodic pattern as a structurally stable limit cycle). We use the system to control the locomotion of a humanoid robot. We also show applications of this system to signal processing

Real time evolutionary algorithms in robotic neural control systems.

This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial Neural Network (ANN) on-line (in this context on-line means while it is in use). Traditionally, Evolutionary Algorithms (Genetic Algorithms, Evolutionary Strategies and Evolutionary Programming) have been used to train networks before use - that is off-line, as have other learning systems like Back-Propagation and Simulated Annealing. However, this means that the network cannot react to new situations (which were not in its original training set). The system outlined here uses a Simulated Legged Robot as a test-bed and allows it to adapt to a changing Fitness function. An example of this in reality would be a robot walking from a solid surface onto an unknown surface (which might be, for example, rock or sand) while optimising its controlling network in real-time, to adjust its locomotive gait, accordingly. The project initially developed a Central Pattern Generator (CPG) for a Bipedal Robot and used this to explore the basic characteristics of RTEA. The system was then developed to operate on a Quadruped Robot and a test regime set up which provided thousands of real-environment like situations to test the RTEAs ability to control the robot. The programming for the system was done using Borland C++ Builder and no commercial simulation software was used. Through this means, the Evolutionary Operators of the RTEA were examined and their real-time performance evaluated. The results demonstrate that a RTEA can be used successfully to optimise an ANN in real-time. They also show the importance of Neural Functionality and Network Topology in such systems and new models of both neurons and networks were developed as part of the project. Finally, recommendations for a working system are given and other applications reviewed

Locomoção bípede adaptativa a partir de uma única demonstração usando primitivas de movimento

Doutoramento em Engenharia EletrotécnicaEste trabalho aborda o problema de capacidade de imitação da locomoção humana através da utilização de trajetórias de baixo nível codificadas com primitivas de movimento e utilizá-las para depois generalizar para novas situações, partindo apenas de uma demonstração única. Assim, nesta linha de pensamento, os principais objetivos deste trabalho são dois: o primeiro é analisar, extrair e codificar demonstrações efetuadas por um humano, obtidas por um sistema de captura de movimento de forma a modelar tarefas de locomoção bípede. Contudo, esta transferência não está limitada à simples reprodução desses movimentos, requerendo uma evolução das capacidades para adaptação a novas situações, assim como lidar com perturbações inesperadas. Assim, o segundo objetivo é o desenvolvimento e avaliação de uma estrutura de controlo com capacidade de modelação das ações, de tal forma que a demonstração única apreendida possa ser modificada para o robô se adaptar a diversas situações, tendo em conta a sua dinâmica e o ambiente onde está inserido. A ideia por detrás desta abordagem é resolver o problema da generalização a partir de uma demonstração única, combinando para isso duas estruturas básicas. A primeira consiste num sistema gerador de padrões baseado em primitivas de movimento utilizando sistemas dinâmicos (DS). Esta abordagem de codificação de movimentos possui propriedades desejáveis que a torna ideal para geração de trajetórias, tais como a possibilidade de modificar determinados parâmetros em tempo real, tais como a amplitude ou a frequência do ciclo do movimento e robustez a pequenas perturbações. A segunda estrutura, que está embebida na anterior, é composta por um conjunto de osciladores acoplados em fase que organizam as ações de unidades funcionais de forma coordenada. Mudanças em determinadas condições, como o instante de contacto ou impactos com o solo, levam a modelos com múltiplas fases. Assim, em vez de forçar o movimento do robô a situações pré-determinadas de forma temporal, o gerador de padrões de movimento proposto explora a transição entre diferentes fases que surgem da interação do robô com o ambiente, despoletadas por eventos sensoriais. A abordagem proposta é testada numa estrutura de simulação dinâmica, sendo que várias experiências são efetuadas para avaliar os métodos e o desempenho dos mesmos.This work addresses the problem of learning to imitate human locomotion actions through low-level trajectories encoded with motion primitives and generalizing them to new situations from a single demonstration. In this line of thought, the main objectives of this work are twofold: The first is to analyze, extract and encode human demonstrations taken from motion capture data in order to model biped locomotion tasks. However, transferring motion skills from humans to robots is not limited to the simple reproduction, but requires the evaluation of their ability to adapt to new situations, as well as to deal with unexpected disturbances. Therefore, the second objective is to develop and evaluate a control framework for action shaping such that the single-demonstration can be modulated to varying situations, taking into account the dynamics of the robot and its environment. The idea behind the approach is to address the problem of generalization from a single-demonstration by combining two basic structures. The first structure is a pattern generator system consisting of movement primitives learned and modelled by dynamical systems (DS). This encoding approach possesses desirable properties that make them well-suited for trajectory generation, namely the possibility to change parameters online such as the amplitude and the frequency of the limit cycle and the intrinsic robustness against small perturbations. The second structure, which is embedded in the previous one, consists of coupled phase oscillators that organize actions into functional coordinated units. The changing contact conditions plus the associated impacts with the ground lead to models with multiple phases. Instead of forcing the robot’s motion into a predefined fixed timing, the proposed pattern generator explores transition between phases that emerge from the interaction of the robot system with the environment, triggered by sensor-driven events. The proposed approach is tested in a dynamics simulation framework and several experiments are conducted to validate the methods and to assess the performance of a humanoid robot

Climbing and Walking Robots

Nowadays robotics is one of the most dynamic fields of scientific researches. The shift of robotics researches from manufacturing to services applications is clear. During the last decades interest in studying climbing and walking robots has been increased. This increasing interest has been in many areas that most important ones of them are: mechanics, electronics, medical engineering, cybernetics, controls, and computers. Today’s climbing and walking robots are a combination of manipulative, perceptive, communicative, and cognitive abilities and they are capable of performing many tasks in industrial and non- industrial environments. Surveillance, planetary exploration, emergence rescue operations, reconnaissance, petrochemical applications, construction, entertainment, personal services, intervention in severe environments, transportation, medical and etc are some applications from a very diverse application fields of climbing and walking robots. By great progress in this area of robotics it is anticipated that next generation climbing and walking robots will enhance lives and will change the way the human works, thinks and makes decisions. This book presents the state of the art achievments, recent developments, applications and future challenges of climbing and walking robots. These are presented in 24 chapters by authors throughtot the world The book serves as a reference especially for the researchers who are interested in mobile robots. It also is useful for industrial engineers and graduate students in advanced study

Towards the Improvement of robot motion learning techniques

Dissertação de Mestrado em Engenharia InformáticaThis manuscript presents solutions and methods to address some of the many problems that arise when dealing with the complex task of motor skill learning in robots. In the last years, several research lines have focused on learning motion primitives either through imitation learning or reinforcement learning. However, for many applications, learning a motion primitive of a single form is not enough and it is required that after being assimilated, the primitive is generalizable such that it can be executed in different contexts and for distinct instances of the same task. Therefore, the motion primitive must adapt a set of parameters according to the environment variables instead of always executing the exact same motor commands when it is put into action. Another aspect to have into consideration is how the learning process of motion primitives is guided. Some primitives are too complex to be learned all at once, i.e, learning all their intricacies without a properly structured approach may be intractable. In this thesis, these aspects are mindfully taken into account, allowing to develop reinforcement learning techniques that are then used to teach a controller of a biped robot that is only able to generate stable locomotion on a flat surface, making it tolerant to a range of slope angles, perpendicular and/or parallel to the direction of walking. Legged locomotion is a relevant example of a complex and dynamic motor skill that has been the focus of intensive research for many years in robotics and it is expected for the techniques that are successful in the learning of such a hard task to be useful in other contexts. In order to achieve this goal, three main steps, divided into chapters of this thesis, are taken. First, an existing algorithm - Cost-regularized Kernel Regression (CrKR) - originally introduced to allow learning to generalize parameterized policies is modified and extended into a new algorithm named CrKR++. Some of the performed changes allow to use the algorithm for training sessions with a high number of samples, which is needed when it is intended to learn complex policies. This feat would be impracticable with the original version of the algorithm due to its high computational complexity. The remaining changes are issued with the purpose of improving the general effectiveness of the algorithm. Second, a framework that enables storing, combining and mutual learning of parameterized policies is presented. This framework, where the CrKR++ algorithm plays a core role, provides the means, for instance, to create a movement primitives library or to perform gradual learning of a motor skill, being named Flexible Framework for Learning (F3L). Finally, the developed framework is used to teach the controller of the biped robot to adapt its locomotion parameters according to the slope angles of the underlying surface. The achieved solution and intermediate steps are tested in simulation software with Dynamic Anthropomorphic Robot with Intelligence–Open Platform (DARwIn-OP) in carefully delineated experiments.Esta tese apresenta soluções e métodos que abordam alguns dos muitos problemas que surgem quando lidando com o complexo problema da aprendizagem de tarefas motoras em robôs. Nos últimos anos, várias linhas de investigação focaram-se na aprendizagem de primitivas de movimento, quer pela aprendizagem via imitação quer pela aprendizagem via reforço. Contudo, em muitas aplicações, não basta assimilar uma primitiva numa única forma e pode ser necessário que depois de assimilada, uma primitiva seja generalizável de maneira a ser possível executá-la em diferentes contextos e para diferentes instâncias de uma mesma tarefa. Uma primitiva de movimento deve portanto nestes casos adaptar um conjunto de parâmetros de acordo com as condições do meio envolvente em vez de executar sempre os mesmos comandos motores quando colocada em ação. Outro aspeto a ter em consideração é ainda a forma como o processo de aprendizagem das primitivas de movimento é guiado. Algumas primitivas são demasiado complexas para serem apreendidas de uma vez só, isto é, aprender todas as suas nuances sem uma abordagem estruturada pode revelar-se extremamente difícil. Nesta tese, estes dois aspetos são tidos em conta, o que permite desenvolver novas técnicas de aprendizagem via reforço que são depois usadas para ensinar um programa controlador de um robô bípede que é apenas capaz de lidar com superfícies planas, tornando-o tolerante a uma gama de inclinações em direções perpendiculares ou paralelas à direção do movimento. A locomoção com pernas é o exemplo definitivo de uma tarefa motora complexa e dinâmica que tem sido alvo de investigação intensiva durante anos na robótica. É de esperar que as técnicas que sejam bem sucedidas na aprendizagem de uma tarefa com este grau de dificuldade sejam também úteis em outros contextos. Para atingir este objetivo, três passos principais, que se dividem em capítulos desta tese são dados. Em primeiro lugar, um algoritmo já existente - CrKR - ,originalmente criado para permitir a aprendizagem de políticas parametrizadas, é modificado e transformado num novo algoritmo denominado CrKR++. Algumas das modificações feitas permitem usar o algoritmo em sessões de treino com um maior número de amostras, o que é necessário quando se pretende aprender políticas com um elevado grau de complexidade. Tal seria impossível com a versão original do algoritmo devido à sua elevada complexidade computacional. As restantes modificações são introduzidas com o propósito de melhorar a eficácia geral do algoritmo. Em segundo lugar, uma framework que permite o armazenamento, a combinação e a aprendizagem mútua de políticas parametrizadas é apresentada. Esta framework, onde o algoritmo CrKR++ desempenha uma função nuclear, providencia os meios para, por exemplo, criar uma biblioteca de primitivas de movimento ou realizar aprendizagem gradual de uma tarefa motora sendo denominada de F3L. Por fim, a framework desenvolvida é utilizada para ensinar o controlador do robô bípede a adaptar determinados parâmetros da locomoção em função da inclinação da superfície subjacente. A solução alcançada bem como os passos intermédios são testados em software de simulação com o robô DARwIn-OP em experiências cuidadosamente delineadas

Generating walking behaviours in legged robots

Many legged robots have boon built with a variety of different abilities, from running to liopping to climbing stairs. Despite this however, there has been no consistency of approach to the problem of getting them to walk. Approaches have included breaking down the walking step into discrete parts and then controlling them separately, using springs and linkages to achieve a passive walking cycle, and even working out the necessary movements in simulation and then imposing them on the real robot. All of these have limitations, although most were successful at the task for which they were designed. However, all of them fall into one of two categories: either they alter the dynamics of the robots physically so that the robot, whilst very good at walking, is not as general purpose as it once was (as with the passive robots), or they control the physical mechanism of the robot directly to achieve their goals, and this is a difficult task.In this thesis a design methodology is described for building controllers for 3D dynam¬ ically stable walking, inspired by the best walkers and runners around — ourselves — so the controllers produced are based 011 the vertebrate Central Nervous System. This means that there is a low-level controller which adapts itself to the robot so that, when switched on, it can be considered to simulate the springs and linkages of the passive robots to produce a walking robot, and this now active mechanism is then controlled by a relatively simple higher level controller. This is the best of both worlds — we have a robot which is inherently capable of walking, and thus is easy to control like the passive walkers, but also retains the general purpose abilities which makes it so potentially useful.This design methodology uses an evolutionary algorithm to generate low-level control¬ lers for a selection of simulated legged robots. The thesis also looks in detail at previous walking robots and their controllers and shows that some approaches, including staged evolution and hand-coding designs, may be unnecessary, and indeed inappropriate, at least for a general purpose controller. The specific algorithm used is evolutionary, using a simple genetic algorithm to allow adaptation to different robot configurations, and the controllers evolved are continuous time neural networks. These are chosen because of their ability to entrain to the movement of the robot, allowing the whole robot and network to be considered as a single dynamical system, which can then be controlled by a higher level system.An extensive program of experiments investigates the types of neural models and net¬ work structures which are best suited to this task, and it is shown that stateless and simple dynamic neural models are significantly outperformed as controllers by more complex, biologically plausible ones but that other ideas taken from biological systems, including network connectivities, are not generally as useful and reasons for this are examined.The thesis then shows that this system, although only developed 011 a single robot, is capable of automatically generating controllers for a wide selection of different test designs. Finally it shows that high level controllers, at least to control steering and speed, can be easily built 011 top of this now active walking mechanism

Robust walking of a quadraped robot

Master'sMASTER OF ENGINEERIN