Integrative Biomimetics of Autonomous Hexapedal Locomotion

Dürr V, Arena PP, Cruse H, et al. Integrative Biomimetics of Autonomous Hexapedal Locomotion. Frontiers in Neurorobotics. 2019;13: 88.Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size

A Bio-inspired Distributed Control Architecture: Coupled Artificial Signalling Networks

This thesis studies the applicability of computational models inspired by the structure and dynamics of signalling networks to the control of complex control problems. In particular, this thesis presents two different abstractions that aim to capture the signal processing abilities of biological cells: a stand-alone signalling network and a coupled signalling network. While the former mimics the interacting relationships amongst the components in a signalling pathway, the latter replicates the connectionism amongst signalling pathways. After initially investigating the feasibility of these models for controlling two complex numerical dynamical systems, Chirikov's standard map and the Lorenz system, this thesis explores their applicability to a difficult real world control problem, the generation of adaptive rhythmic locomotion patterns within a legged robotic system. The results highlight that the locomotive movements of a six-legged robot could be controlled in order to adapt the robot's trajectory in a range of challenging environments. In this sense, signalling networks are responsible for the robot adaptability and inter limb coordination as they self-adjust their dynamics according to the terrain's irregularities. More generally, the results of this thesis highlight the capacity of coupled signalling networks to decompose non-linear problems into smaller sub-tasks, which can then be independently solved

Bio-inspired Dynamic Control Systems with Time Delays

The world around us exhibits a rich and ever changing environment of startling, bewildering and fascinating complexity. Almost everything is never as simple as it seems, but through the chaos we may catch fleeting glimpses of the mechanisms within. Throughout the history of human endeavour we have mimicked nature to harness it for our own ends. Our attempts to develop truly autonomous and intelligent machines have however struggled with the limitations of our human ability. This has encouraged some to shirk this responsibility and instead model biological processes and systems to do it for us. This Thesis explores the introduction of continuous time delays into biologically inspired dynamic control systems. We seek to exploit rich temporal dynamics found in physical and biological systems for modelling complex or adaptive behaviour through the artificial evolution of networks to control robots. Throughout, arguments have been presented for the modelling of delays not only to better represent key facets of physical and biological systems, but to increase the computational potential of such systems for the synthesis of control. The thorough investigation of the dynamics of small delayed networks with a wide range of time delays has been undertaken, with a detailed mathematical description of the fixed points of the system and possible oscillatory modes developed to fully describe the behaviour of a single node. Exploration of the behaviour for even small delayed networks illustrates the range of complex behaviour possible and guides the development of interesting solutions. To further exploit the potential of the rich dynamics in such systems, a novel approach to the 3D simulation of locomotory robots has been developed focussing on minimising the computational cost. To verify this simulation tool a simple quadruped robot was developed and the motion of the robot when undergoing a manually designed gait evaluated. The results displayed a high degree of agreement between the simulation and laser tracker data, verifying the accuracy of the model developed. A new model of a dynamic system which includes continuous time delays has been introduced, and its utility demonstrated in the evolution of networks for the solution of simple learning behaviours. A range of methods has been developed for determining the time delays, including the novel concept of representing the time delays as related to the distance between nodes in a spatial representation of the network. The application of these tools to a range of examples has been explored, from Gene Regulatory Networks (GRNs) to robot control and neural networks. The performance of these systems has been compared and contrasted with the efficacy of evolutionary runs for the same task over the whole range of network and delay types. It has been shown that delayed dynamic neural systems are at least as capable as traditional Continuous Time Recurrent Neural Networks (CTRNNs) and show significant performance improvements in the control of robot gaits. Experiments in adaptive behaviour, where there is not such a direct link between the enhanced system dynamics and performance, showed no such discernible improvement. Whilst we hypothesise that the ability of such delayed networks to generate switched pattern generating nodes may be useful in Evolutionary Robotics (ER) this was not borne out here. The spatial representation of delays was shown to be more efficient for larger networks, however these techniques restricted the search to lower complexity solutions or led to a significant falloff as the network structure becomes more complex. This would suggest that for anything other than a simple genotype, the direct method for encoding delays is likely most appropriate. With proven benefits for robot locomotion and the open potential for adaptive behaviour delayed dynamic systems for evolved control remain an interesting and promising field in complex systems research

ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems

Schilling M, Cruse H. ReaCog, a Minimal Cognitive Controller Based on Recruitment of Reactive Systems. Frontiers in Neurorobotics. 2017;11: 3.It has often been stated that for a neuronal system to become a cognitive one, it has to be large enough. In contrast, we argue that a basic property of a cognitive system, namely the ability to plan ahead, can already be fulfilled by small neuronal systems. As a proof of concept, we propose an artificial neural network, termed reaCog, that, first, is able to deal with a specific domain of behavior (six-legged-walking). Second, we show how a minor expansion of this system enables the system to plan ahead and deploy existing behavioral elements in novel contexts in order to solve current problems. To this end, the system invents new solutions that are not possible for the reactive network. Rather these solutions result from new combinations of given memory elements. This faculty does not rely on a dedicated system being more or less independent of the reactive basis, but results from exploitation of the reactive basis by recruiting the lower-level control structures in a way that motor planning becomes possible as an internal simulation relying on internal representation being grounded in embodied experiences

Posture control of a low-cost commercially available hexapod robot for uneven terrain locomotion

Legged robots hold the advantage on uneven and irregular terrain, where they exhibit superior mobility over other terrestrial, mobile robots. One of the fundamental ingredients in achieving this exceptional mobility on uneven terrain is posture control, also referred to as attitude control. Many approaches to posture control for multi-legged robots have been taken in the literature; however, the majority of this research has been conducted on custom designed platforms, with sophisticated hardware and, often, fully custom software. Commercially available robots hardly feature in research on uneven terrain locomotion of legged robots, despite the significant advantages they pose over custom designed robots, including drastically lower costs, reusability of parts, and reduced development time, giving them the serious potential to be employed as low-cost research and development platforms. Hence, the aim of this study was to design and implement a posture control system on a low-cost, commercially available hexapod robot – the PhantomX MK-II – overcoming the limitations presented by the lower cost hardware and open source software, while still achieving performance comparable to that exhibited by custom designed robots. For the initial controller development, only the case of the robot standing on all six legs was considered, without accounting for walking motion. This Standing Posture Controller made use of the Virtual Model Control (VMC) strategy, along with a simple foot force distribution rule and a direct force control method for each of the legs, the joints of which can only be position controlled (i.e. they do not have torque control capabilities). The Standing Posture Controller was experimentally tested on level and uneven terrain, as well as on a dynamic balance board. Ground truth measurements of the posture during testing exhibited satisfactory performance, which compared favourably to results of similar tests performed on custom designed platforms. Thereafter, the control system was modified for the more general case of walking. The Walking Posture Controller still made use of VMC for the high-level posture control, but the foot force distribution was expanded to also account for a tripod of ground contact legs during walking. Additionally, the foot force control structure was modified to achieve compliance control of the legs during the swing phase, while still providing direct force control during the stance phase, using the same overall control structure, with a simple switching strategy, all without the need for torque control or modification of the motion control system of the legs, resulting in a novel foot force control system for low-cost, legged robots. Experimental testing of the Walking Posture Controller, with ground truth measurements, revealed that it improved the robot’s posture response by a small amount when walking on flat terrain, while on an uneven terrain setup the maximum roll and pitch angle deviations were reduced by up to 28.6% and 28.1%, respectively, as compared to the uncompensated case. In addition to reducing the maximum deviations on uneven terrain, the overall posture response was significantly improved, resulting in a response much closer to that observed on flat terrain, throughout much of the uneven terrain locomotion. Comparing these results to those obtained in similar tests performed with more sophisticated, custom designed robots, it is evident that the Walking Posture Controller exhibits favourable performance, thus fulfilling the aim of this study.Dissertation (MEng)--University of Pretoria, 2018.Mechanical and Aeronautical EngineeringMEngUnrestricte

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

Investigating Sensorimotor Control in Locomotion using Robots and Mathematical Models

Locomotion is a very diverse phenomenon that results from the interactions of a body and its environment and enables a body to move from one position to another. Underlying control principles rely among others on the generation of intrinsic body movements, adaptation and synchronization of those movements with the environment, and the generation of respective reaction forces that induce locomotion. We use mathematical and physical models, namely robots, to investigate how movement patterns emerge in a specific environment, and to what extent central and peripheral mechanisms contribute to movement generation. We explore insect walking, undulatory swimming and bimodal terrestrial and aquatic locomotion. We present relevant findings that explain the prevalence of tripod gaits for fast climbing based on the outcome of an optimization procedure. We also developed new control paradigms based on local sensory pressure feedback for anguilliform swimming, which include oscillator-free and decoupled control schemes, and a new design methodology to create physical models for locomotion investigation based on a salamander-like robot. The presented work includes additional relevant contributions to robotics, specifically a new fast dynamically stable walking gait for hexapedal robots and a decentralized scheme for highly modular control of lamprey-like undulatory swimming robots

Locomotion and pose estimation in compliant, torque-controlled hexapedal robots

Several scenarios, such as disaster response or terrestrial and extra-terrestrial exploration, comprise environments that are dangerous or even inaccessible for humans. In those cases, autonomous robots pose a promising alternative to render such endeavours possible. While most of today’s robotic explorers are wheeled or tracked vehicles, legged systems gained increased attention in recent years. With their unique combination of omnidirectional mobility and intrinsic manipulation capabilities, they are envisioned to serve as the rough terrain specialists in scouting or sample and return missions. Especially, small to mid-size hexapods are of great interest for those scenarios. Providing static stability across a wide range of walking speeds, they offer an attractive trade-off between versatility and complexity. Another important advantage is their redundancy, allowing them to tolerate the loss of single legs. However, due to their small size, the computational on-board resources are limited. Thus, the use of smart and efficient algorithms is of utmost importance in order to enable autonomous operation within a priori unknown rough environments. Working towards such autonomous robotic scouts, this thesis contributes with the development, implementation, and test of a self-contained walking layer as well as a 6 degrees of freedom (DOF) leg odometry for compliant, torque-controlled, hexapedal robots. Herein, the important property of all presented algorithms is the sole use of proprioceptive measurements provided by the legs, i. e. joint angles and joint torques. Especially the joint torque sensors improve the walking process by enabling the use of sensitive compliance controllers and distributed collision detection. Comprising a set of algorithms, the walking layer organises and structures the walking process in order to generate robust, adaptive, and leg loss tolerant locomotion in uneven terrain. Furthermore, it encapsulates the walking process, and thus hides its complexity from higher-level algorithms such as navigation. Its three main functional components are a flexible, biologically-inspired gait coordination algorithm, single leg reflexes, and active joint compliance control. Thereof, the gait coordination algorithm realises temporal adaptation of the step sequence while reflexes adjust the leg trajectories to the local terrain. The joint compliance control reduces internal forces and allows for situation dependent stiffness adjustments. An algorithmic extensions to the basic gait coordination enables the immediate adaptation to leg loss. In combination with stiffness and pose adjustments, this allows the hexapod to retain stable locomotion on five legs. In order to account for the emerging gait, the leg odometry algorithm employs an optimisation approach to obtain a kinematics-based pose estimate from joint angle measurements. Fusing the resulting pitch and roll angle estimates with joint-torque-measurement-based attitude data, reduces the associated drift, and thus stabilises the overall pose estimate. Various simulations and experiments with the six-legged, torque-controlled DLR Crawler demonstrate the effectiveness of the proposed walking layer as well as the 6-DOF leg odometry.Für die planetare Exploration sowie den Einsatz in Katastrophengebieten sind autonome Laufroboter zunehmend von Interesse. In diesen Szenarien sollen sie den Menschen an gefährlichen oder schwer zugänglichen Orten ersetzen und dort Erkundungseinsätze sowie Probenahmen in schwierigem Gelände durchführen. Unter der Vielzahl an möglichen Systemen bieten im Besonderen kleinere Sechsbeiner einen sehr guten Kompromiss zwischen Stabilität, hoher Beweglichkeit, Vielseitigkeit und einer vertretbaren Komplexität der Regelung. Ein weiterer Vorteil ist ihre Redundanz, die es ihnen erlaubt, den Ausfall einzelner Beine mit geringem Aufwand zu kompensieren. Dementgegen ist die beschränkte Rechenkapazität ein Nachteil der reduzierten Größe. Um diesen auszugleichen und das autonome Agieren in einer unbekannten Umgebung zu ermöglichen, werden daher einfache und effiziente Algorithmen benötigt, die im Zusammenspiel jedoch ein komplexes Verhalten erzeugen. Auf dem Weg zum autonom explorierenden Laufroboter entwickelt diese Arbeit einen robusten, adaptiven und fehlertoleranten Laufalgorithmus sowie eine 6D Eigenbewegungsschätzung für nachgiebige, drehmomentgeregelte Sechsbeiner. Besonders herauszustellen ist, dass alle in der Arbeit vorgestellten Algorithmen ausschließlich die propriozeptive Sensorik der Beine verwenden. Durch diesen Ansatz kann der Laufprozess von anderen Prozessen, wie der Navigation, getrennt und somit der Datenaustausch effizient gestaltet werden. Für die Fortbewegung in unebenem Gelände kombiniert der vorgestellte Laufalgorithmus eine flexible, biologisch inspirierte Gangkoordination mit verschiedenen Einzelbeinreflexen und einer nachgiebigen Gelenkregelung. Hierbei übernimmt die Gangkoordination die zeitliche Steuerung der Schrittfolge, während die Einzelbeinreflexe für eine räumliche Variation der Fußtrajektorien zuständig sind. Die nachgiebige Gelenkregelung reduziert interne Kräfte und erlaubt eine Anpassung der Gelenksteifigkeiten an die lokalen Umgebungsbedingungen sowie den aktuellen Zustand des Roboters. Eine wichtige Eigenschaft des Laufalgorithmus ist seine Fähigkeit, den Ausfall einzelner Beine zu kompensieren. In diesem Fall erfolgt eine Adaption der Gangkoordination über die Erneuerung der Nachbarschaftsbeziehungen der Beine. Zusätzlich verbessern eine Veränderung der Pose und eine Erhöhung der Gelenksteifigkeiten die Stabilität des durch den Beinverlust beeinträchtigten Roboters. Gleich dem Laufalgorithmus verwendet die 6D Eigenbewegungsschätzung nur die Messungen der propriozeptiven Sensoren der Beine. Hierbei arbeitet der Algorithmus in einem dreistufigen Verfahren. Zuerst berechnet er mit Hilfe der Beinkinematik und einer Optimierung die Pose des Roboters. Nachfolgend bestimmt er aus den Gelenkmomentmessungen den Gravitationsvektor und berechnet daraus die Neigungswinkel des Systems. Eine Fusion dieser Werte mit den Nick- und Rollwinkeln der ersten Stufe stabilisiert daraufhin die gesamte Odometrie und reduziert deren Drift. Alle in dieser Arbeit entwickelten Algorithmen wurden mit Hilfe von Simulationen sowie Experimenten mit dem drehmomentgeregelten DLR Krabbler erfolgreich validiert