121 research outputs found

    A brainstem-like modulation approach for gait transition in a quadruped robot

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    The ability to traverse a wide variety of terrains while walking is basically a requirement for performing useful tasks in our human centric world. In this article, we propose a bio-inspired robotic controller able to generate locomotion and to easily switch between different type of gaits. In order to improve the robot stability and response while locomoting, we adjust both the duty factor and the interlimb phase relationships, according to the velocities. We extend previous work, by applying nonlinear oscillators to generate the rhythmic locomotor movements for a quadruped robot, similarly to the biological counterparts. The generated trajectories are modulated by a drive signal, that modifies the oscillator frequency, amplitude and the coupling parameters among the oscillators, proportionally to the drive signal strength. By increasing the drive signal, locomotion can be elicited and velocity increased while switching to the appropriate gaits. This drive signal can be specified according to sensory information or set a priori. The implementation of the central pattern generator network and the activity modulation layer is shown in simulation and in an AIBO robot

    In silico case studies of compliant robots: AMARSI deliverable 3.3

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    In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

    Intelligent approaches in locomotion - a review

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    The evolution of modular artificial neural networks.

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    This thesis describes a novel approach to the evolution of Modular Artificial Neural Networks. Standard Evolutionary Algorithms, used in this application include: Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming and Genetic Programming; however, these often fail in the evolution of complex systems, particularly when such systems involve multi-domain sensory information which interacts in complex ways with system outputs. The aim in this work is to produce an evolutionary method that allows the structure of the network to evolve from simple to complex as it interacts with a dynamic environment. This new algorithm is therefore based on Incremental Evolution. A simulated model of a legged robot was used as a test-bed for the approach. The algorithm starts with a simple robotic body plan. This then grows incrementally in complexity along with its controlling neural network and the environment it reacts with. The network grows by adding modules to its structure - so the technique may also be termed a Growth Algorithm. Experiments are presented showing the successful evolution of multi-legged gaits and a simple vision system. These are then integrated together to form a complete robotic system. The possibility of the evolution of complex systems is one advantage of the algorithm and it is argued that it represents a possible path towards more advanced artificial intelligence. Applications in Electronics, Computer Science, Mechanical Engineering and Aerospace are also discussed

    Motion Planning and Control of Dynamic Humanoid Locomotion

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    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

    Developing Design and Analysis Framework for Hybrid Mechanical-Digital Control of Soft Robots: from Mechanics-Based Motion Sequencing to Physical Reservoir Computing

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    The recent advances in the field of soft robotics have made autonomous soft robots working in unstructured dynamic environments a close reality. These soft robots can potentially collaborate with humans without causing any harm, they can handle fragile objects safely, perform delicate surgeries inside body, etc. In our research we focus on origami based compliant mechanisms, that can be used as soft robotic skeleton. Origami mechanisms are inherently compliant, lightweight, compact, and possess unique mechanical properties such as– multi-stability, nonlinear dynamics, etc. Researchers have shown that multi-stable mechanisms have applications in motion-sequencing applications. Additionally, the nonlinear dynamic properties of origami and other soft, compliant mechanisms are shown to be useful for ‘morphological computation’ in which the body of the robot itself takes part in performing complex computations required for its control. In our research we demonstrate the motion-sequencing capability of multi-stable mechanisms through the example of bistable Kresling origami robot that is capable of peristaltic locomotion. Through careful theoretical analysis and thorough experiments, we show that we can harness multistability embedded in the origami robotic skeleton for generating actuation cycle of a peristaltic-like locomotion gait. The salient feature of this compliant robot is that we need only a single linear actuator to control the total length of the robot, and the snap-through actions generated during this motion autonomously change the individual segment lengths that lead to earthworm-like peristaltic locomotion gait. In effect, the motion-sequencing is hard-coded or embedded in the origami robot skeleton. This approach is expected to reduce the control requirement drastically as the robotic skeleton itself takes part in performing low-level control tasks. The soft robots that work in dynamic environments should be able to sense their surrounding and adapt their behavior autonomously to perform given tasks successfully. Thus, hard-coding a certain behavior as in motion-sequencing is not a viable option anymore. This led us to explore Physical Reservoir Computing (PRC), a computational framework that uses a physical body with nonlinear properties as a ‘dynamic reservoir’ for performing complex computations. The compliant robot ‘trained’ using this framework should be able to sense its surroundings and respond to them autonomously via an extensive network of sensor-actuator network embedded in robotic skeleton. We show for the first time through extensive numerical analysis that origami mechanisms can work as physical reservoirs. We also successfully demonstrate the emulation task using a Miura-ori based reservoir. The results of this work will pave the way for intelligently designed origami-based robots with embodied intelligence. These next generation of soft robots will be able to coordinate and modulate their activities autonomously such as switching locomotion gait and resisting external disturbances while navigating through unstructured environments

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

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    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

    Real time evolutionary algorithms in robotic neural control systems.

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    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

    Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

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    Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching
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