657 research outputs found

    Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

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    Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

    Motion Planning and Control for the Locomotion of Humanoid Robot

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    This thesis aims to contribute on the motion planning and control problem of the locomotion of humanoid robots. For the motion planning, various methods were proposed in different levels of model dependence. First, a model free approach was proposed which utilizes linear regression to estimate the relationship between foot placement and moving velocity. The data-based feature makes it quite robust to handle modeling error and external disturbance. As a generic control philosophy, it can be applied to various robots with different gaits. To reduce the risk of collecting experimental data of model-free method, based on the simplified linear inverted pendulum model, the classic planning method of model predictive control was explored to optimize CoM trajectory with predefined foot placements or optimize them two together with respect to the ZMP constraint. Along with elaborately designed re-planning algorithm and sparse discretization of trajectories, it is fast enough to run in real time and robust enough to resist external disturbance. Thereafter, nonlinear models are utilized for motion planning by performing forward simulation iteratively following the multiple shooting method. A walking pattern is predefined to fix most of the degrees of the robot, and only one decision variable, foot placement, is left in one motion plane and therefore able to be solved in milliseconds which is sufficient to run in real time. In order to track the planned trajectories and prevent the robot from falling over, diverse control strategies were proposed according to the types of joint actuators. CoM stabilizer was designed for the robots with position-controlled joints while quasi-static Cartesian impedance control and optimization-based full body torque control were implemented for the robots with torque-controlled joints. Various scenarios were set up to demonstrate the feasibility and robustness of the proposed approaches, like walking on uneven terrain, walking with narrow feet or straight leg, push recovery and so on

    Locomoção de humanoides robusta e versátil baseada em controlo analítico e física residual

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    Humanoid robots are made to resemble humans but their locomotion abilities are far from ours in terms of agility and versatility. When humans walk on complex terrains or face external disturbances, they combine a set of strategies, unconsciously and efficiently, to regain stability. This thesis tackles the problem of developing a robust omnidirectional walking framework, which is able to generate versatile and agile locomotion on complex terrains. We designed and developed model-based and model-free walk engines and formulated the controllers using different approaches including classical and optimal control schemes and validated their performance through simulations and experiments. These frameworks have hierarchical structures that are composed of several layers. These layers are composed of several modules that are connected together to fade the complexity and increase the flexibility of the proposed frameworks. Additionally, they can be easily and quickly deployed on different platforms. Besides, we believe that using machine learning on top of analytical approaches is a key to open doors for humanoid robots to step out of laboratories. We proposed a tight coupling between analytical control and deep reinforcement learning. We augmented our analytical controller with reinforcement learning modules to learn how to regulate the walk engine parameters (planners and controllers) adaptively and generate residuals to adjust the robot’s target joint positions (residual physics). The effectiveness of the proposed frameworks was demonstrated and evaluated across a set of challenging simulation scenarios. The robot was able to generalize what it learned in one scenario, by displaying human-like locomotion skills in unforeseen circumstances, even in the presence of noise and external pushes.Os robôs humanoides são feitos para se parecerem com humanos, mas suas habilidades de locomoção estão longe das nossas em termos de agilidade e versatilidade. Quando os humanos caminham em terrenos complexos ou enfrentam distúrbios externos combinam diferentes estratégias, de forma inconsciente e eficiente, para recuperar a estabilidade. Esta tese aborda o problema de desenvolver um sistema robusto para andar de forma omnidirecional, capaz de gerar uma locomoção para robôs humanoides versátil e ágil em terrenos complexos. Projetámos e desenvolvemos motores de locomoção sem modelos e baseados em modelos. Formulámos os controladores usando diferentes abordagens, incluindo esquemas de controlo clássicos e ideais, e validámos o seu desempenho por meio de simulações e experiências reais. Estes frameworks têm estruturas hierárquicas compostas por várias camadas. Essas camadas são compostas por vários módulos que são conectados entre si para diminuir a complexidade e aumentar a flexibilidade dos frameworks propostos. Adicionalmente, o sistema pode ser implementado em diferentes plataformas de forma fácil. Acreditamos que o uso de aprendizagem automática sobre abordagens analíticas é a chave para abrir as portas para robôs humanoides saírem dos laboratórios. Propusemos um forte acoplamento entre controlo analítico e aprendizagem profunda por reforço. Expandimos o nosso controlador analítico com módulos de aprendizagem por reforço para aprender como regular os parâmetros do motor de caminhada (planeadores e controladores) de forma adaptativa e gerar resíduos para ajustar as posições das juntas alvo do robô (física residual). A eficácia das estruturas propostas foi demonstrada e avaliada em um conjunto de cenários de simulação desafiadores. O robô foi capaz de generalizar o que aprendeu em um cenário, exibindo habilidades de locomoção humanas em circunstâncias imprevistas, mesmo na presença de ruído e impulsos externos.Programa Doutoral em Informátic

    Bipedal Walking Analysis, Control, and Applications Towards Human-Like Behavior

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    Realizing the essentials of bipedal walking balance is one of the core studies in both robotics and biomechanics. Although the recent developments of walking control on bipedal robots have brought the humanoid automation to a different level, the walking performance is still limited compared to human walking, which also restricts the related applications in biomechanics and rehabilitation. To mitigate the discrepancy between robotic walking and human walking, this dissertation is broken into three parts to develop the control methods to improve three important perspectives: predictive walking behavior, gait optimization, and stepping strategy. To improve the predictive walking behavior captured by the model predictive control (MPC) which is transitionally applied with the nonlinear tracking control in sequence, a quadratic program (QP)-based controller is proposed to unify center of mass (COM) planning using MPC and a nonlinear torque control with control Lyapunov function (CLF). For the gait optimization, we focus on the algorithms of trajectory optimization with direct collocation framework. We propose a robust trajectory optimization using step-time sampling for a simple walker under terrain uncertainties. Towards generating human-like walking gait with multi-domain (phases), we improve the optimization through contact with more accurate transcription method for level walking, and generalize the hybrid zero dynamics (HZD) gait optimization with modified contact conditions for walking on various terrains. The results are compared with human walking gaits, where the similar trends and the sources of discrepancies are identified. In the third part for stepping strategy, we perform step estimation based on capture point (CP) for different human movements, including single-step (balance) recovery, walking and walking with slip. The analysis provides the insights of the efficacy and limitation of CP-based step estimation for human gait

    Scaled Autonomy for Networked Humanoids

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    Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

    Low obstacles avoidance for lower limb exoskeletons

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    Gli esoscheletri motorizzati per gli arti inferiori (LLEs) sono robot indossabili che permettono a soggetti con disabilità degli arti inferiori di camminare indipendentemente, e persino migliorare le prestazioni degli arti inferiori nel caso di soggetti sani. Nonostante i recenti sviluppi, l'uso di questa promettente tecnologia è relegato agli ambiti clinici e di ricerca; il suo utilizzo come strumento per camminare in modo indipendente in ambienti non controllati è ancora mancante. Il motivo principale di questa limitazione è dovuto alla mancanza di adattabilità degli LLE ai diversi ambienti che possono essere incontrati durante il cammino: la maggioranza degli LLE sfrutta traiettorie predefinite degli arti inferiori senza valutare l'ambiente circostante. Questo implica che ogni tipo di controllo addizionale è a carico dell'utente, e risulta in un sovraccarico fisico e cognitivo da parte di quest'ultimo. Questa tesi si pone l'obbiettivo di superare le limitazioni sopracitate, proponendo un approccio innovativo per aumentare l'autonomia degli LLE. In particolare, il metodo proposto ha lo scopo di stimare la traiettoria degli arti inferiori ottimale, così da poter superare in modo autonomo gli ostacoli bassi che potrebbero essere incontrati lungo il cammino. Tramite l'uso di una stereo camera unita ad un algoritmo di Computer Vision, l'ambiente viene percepito in modo da identificare il pavimento e gli ostacoli che potrebbero influenzare il cammino con l'obbiettivo di selezionare il punto d'appoggio ottimale per il piede. Successivamente, un algoritmo iterativo per la generazione della traiettoria del piede senza collisioni (CFFTG) permette di ottenere i dati necessari a calcolare la cinematica inversa dell'esoscheletro, ed infine gli angoli ai giunti ottenuti da quest'ultima vengono forniti ai controllori dei motori per effettuare il movimento desiderato. Test sperimentali in simulazione (basati su dati reali) sono stati eseguiti per valutare il comportamento dell'algoritmo di Computer Vision e del CFFTG, mostrando ottimi risultati in diversi scenari. Inoltre, le assunzioni su cui si basa la soluzione proposta permettono la sua compatibilità con la maggioranza degli esoscheletri commerciali e di ricerca attualmente disponibili. Credo che pensare agli esoscheletri come degli agenti semi autonomi, piuttosto che come dei dispositivi controllati unicamente dall'utente, rappresenti non solo un percorso che porterà alla simbiosi tra uomo ed esoscheletro, ma anche all'uso di questa tecnologia nella vita di tutti i giorni.Powered lower limb exoskeletons (LLEs) are innovative wearable robots that allow independent walking in people with severe gait impairments, or even to augment lower limb capabilities of able-bodied users. Despite the recent advancements, the use of this promising technology is still restricted to controlled research/clinical settings; uptake in real-life conditions as a device to promote user independence is still lacking. The main reason behind this limitation can be traced back to the lack adaptability of LLEs to the different walking conditions that may be encountered in real world settings: the majority of LLEs relies on predefined gait trajectories and is generally unaware of the environment in which gait occurs. This means that the control burden is entirely on the user, resulting in an increased physical and cognitive workload. This thesis aims at overcoming the aforementioned limitations by proposing a novel approach to enhance the autonomy of the LLEs. In particular, the proposed method has the purpose of estimating the optimal gait trajectory of the exoskeleton in order to autonomously avoid low obstacles on the ground. By using a depth camera coupled with Computer Vision software module, the environment is sensed to detect the ground plane and obstacles that might interfere with the forward motion, in order to predict the following foothold. Then, an iterative-based collision-free foot trajectory generator (CFFTG) algorithm is proposed to calculate the optimal foot motion and the joints’ angles to be sent to the exoskeleton low-level controllers. Experimental tests have been carried out in simulation to evaluate both the CV module and the CFFTG based on real data, showing successful performance in different scenarios. In addition, the assumptions that have been considered in this work make the proposed approach compatible with the majority of exoskeletons in research and on the market. I believe that re-thinking exoskeletons as semi-autonomous agents will represent not only the cornerstone to promote a more symbiotic human-exoskeleton interaction but may also pave the way for the use of this technology in the everyday life

    Humanoid Robots

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    For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

    Bio-Inspired Robotics

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    Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field
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