Design and development of a hominid robot with local control in its adaptable feet to enhance locomotion capabilities

With increasing mechanization of our daily lives, the expectations and demands in robotic systems increase in the general public and in scientists alike. In recent events such as the Deepwater Horizon''-accident or the nuclear disaster at Fukushima, mobile robotic systems were used, e.g., to support local task forces by gaining visual material to allow an analysis of the situation. Especially the Fukushima example shows that the robotic systems not only have to face a variety of different tasks during operation but also have to deal with different demands regarding the robot's mobility characteristics. To be able to cope with future requirements, it seems necessary to develop kinematically complex systems that feature several different operating modes. That is where this thesis comes in: A robotic system is developed, whose morphology is oriented on chimpanzees and which has the possibility due to its electro-mechanical structure and the degrees of freedom in its arms and legs to walk with different gaits in different postures. For the proposed robot, the chimpanzee was chosen as a model, since these animals show a multitude of different gaits in nature. A quadrupedal gait like crawl allows the robot to traverse safely and stable over rough terrain. A change into the humanoid, bipedal posture enables the robot to move in man-made environments. The structures, which are necessary to ensure an effective and stable locomotion in these two poses, e.g., the feet, are presented in more detail within the thesis. This includes the biological model and an abstraction to allow a technical implementation. In addition, biological spines are analyzed and the development of an active, artificial spine for the robotic system is described. These additional degrees of freedom can increase the robot's locomotion and manipulation capabilities and even allow to show movements, which are not possible without a spine. Unfortunately, the benefits of using an artificial spine in robotic systems are nowadays still neglected, due to the increased complexity of system design and control. To be able to control such a kinematically complex system, a multitude of sensors is installed within the robot's structures. By placing evaluation electronics close by, a local and decentralized preprocessing is realized. Due to this preprocessing is it possible to realize behaviors on the lowest level of robot control: in this thesis it is exemplarily demonstrated by a local controller in the robot's lower leg. In addition to the development and evaluation of robot's structures, the functionality of the overall system is analyzed in different environments. This includes the presentation of detailed data to show the advantages and disadvantages of the local controller. The robot can change its posture independently from a quadrupedal into a bipedal stance and the other way around without external assistance. Once the robot stands upright, it is to investigate to what extent the quadrupedal walking pattern and control structures (like the local controller) have to be modified to contribute to the bipedal walking as well

Learning dynamic motor skills for terrestrial locomotion

The use of Deep Reinforcement Learning (DRL) has received significantly increased attention from researchers within the robotics field following the success of AlphaGo, which demonstrated the superhuman capabilities of deep reinforcement algorithms in terms of solving complex tasks by beating professional GO players. Since then, an increasing number of researchers have investigated the potential of using DRL to solve complex high-dimensional robotic tasks, such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve using conventional optimization approaches. Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions, disaster responses and science expeditions, strongly demand mobility and versatility in legged locomotion to enable task completion. In order to create useful physical robots, it is necessary to design controllers to synthesize the complex locomotion behaviours observed in humans and other animals. In the past, legged locomotion was mainly achieved via analytical engineering approaches. However, conventional analytical approaches have their limitations, as they require relatively large amounts of human effort and knowledge. Machine learning approaches, such as DRL, require less human effort compared to analytical approaches. The project conducted for this thesis explores the feasibility of using DRL to acquire control policies comparable to, or better than, those acquired through analytical approaches while requiring less human effort. In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic locomotion behaviours for legged robots. We first proposed a novel DRL framework for the locomotion of humanoid robots. The proposed learning framework is capable of acquiring robust and dynamic motor skills for humanoids, including balancing, walking, standing-up fall recovery. We subsequently improved upon the learning framework and design a novel multi-expert learning architecture that is capable of fusing multiple motor skills together in a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The successful deployment of learned control policies on a real quadrupedal robot demonstrates the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control

Bio-Inspired Robotics

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

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

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

Injury and Skeletal Biomechanics

This book covers many aspects of Injury and Skeletal Biomechanics. As the title represents, the aspects of force, motion, kinetics, kinematics, deformation, stress and strain are examined in a range of topics such as human muscles and skeleton, gait, injury and risk assessment under given situations. Topics range from image processing to articular cartilage biomechanical behavior, gait behavior under different scenarios, and training, to musculoskeletal and injury biomechanics modeling and risk assessment to motion preservation. This book, together with "Human Musculoskeletal Biomechanics", is available for free download to students and instructors who may find it suitable to develop new graduate level courses and undergraduate teaching in biomechanics

Integration of aerial and terrestrial locomotion modes in a bioinspired robotic system

In robotics, locomotion is a fundamental task for the development of high-level activities such as navigation. For a robotic system, the challenge of evading environmental obstacles depends both on its physical capabilities and on the strategies followed to achieve it. Thus, a robot with the ability to develop several modes of locomotion (walking, flying or swimming) has a greater probability of success in achieving its goal than a robot that develops only one. In nature, Hymenoptera insects use terrestrial and aerial modes of locomotion to carry out their activities. Mimicry the physical capabilities of these insects opens the possibility of improvements in the area of robotic locomotion. Therefore, this work seeks to generate a bio-inspired robotic system that integrates the terrestrial and aerial modes of locomotion. The methodology used in this research project has considered the anatomical study and characterization of Hymenoptera insects locomotion, the proposal of conceptual models that integrate terrestrial and aerial modes locomotion, the construction of a physical platform and experimental testing of the system. In addition, a gait generation approach based on an artificial nervous system of coupled nonlinear oscillators has been proposed. This approach has resulted in the generation of a coherent and functional gait pattern that, in combination with the flight capabilities of the system, has constituted an aero-terrestrial robot. The results obtained in this work include the construction of a bioinspired physical platform, the generation of the gait process using an artificial nervous system and the experimental tests on the integration of aero-terrestrial locomotion.Conacyt - Becario Naciona

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life

Climbing and Walking Robots

With the advancement of technology, new exciting approaches enable us to render mobile robotic systems more versatile, robust and cost-efficient. Some researchers combine climbing and walking techniques with a modular approach, a reconfigurable approach, or a swarm approach to realize novel prototypes as flexible mobile robotic platforms featuring all necessary locomotion capabilities. The purpose of this book is to provide an overview of the latest wide-range achievements in climbing and walking robotic technology to researchers, scientists, and engineers throughout the world. Different aspects including control simulation, locomotion realization, methodology, and system integration are presented from the scientific and from the technical point of view. This book consists of two main parts, one dealing with walking robots, the second with climbing robots. The content is also grouped by theoretical research and applicative realization. Every chapter offers a considerable amount of interesting and useful information

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

Control of Bio-Inspired Sprawling Posture Quadruped Robots with an Actuated Spine

Sprawling posture robots are characterized by upper limb segments protruding horizontally from the body, resulting in lower body height and wider support on the ground. Combined with an actuated segmented spine and tail, such morphology resembles that of salamanders or crocodiles. Although bio-inspired salamander-like robots with simple rotational limbs have been created, not much research has been done on kinematically redundant bio-mimetic robots that can closely replicate kinematics of sprawling animal gaits. Being bio-mimetic could allow a robot to have some of the locomotion skills observed in those animals, expanding its potential applications in challenging scenarios. At the same time, the robot could be used to answer questions about the animal's locomotion. This thesis is focused on developing locomotion controllers for such robots. Due to their high number of degrees of freedom (DoF), the control is based on solving the limb and spine inverse kinematics to properly coordinate different body parts. It is demonstrated how active use of a spine improves the robot's walking and turning performance. Further performance improvement across a variety of gaits is achieved by using model predictive control (MPC) methods to dictate the motion of the robot's center of mass (CoM). The locomotion controller is reused on an another robot (OroBOT) with similar morphology, designed to mimic the kinematics of a fossil belonging to Orobates, an extinct early tetrapod. Being capable of generating different gaits and quantitatively measuring their characteristics, OroBOT was used to find the most probable way the animal moved. This is useful because understanding locomotion of extinct vertebrates helps to conceptualize major transitions in their evolution. To tackle field applications, e.g. in disaster response missions, a new generation of field-oriented sprawling posture robots was built. The robustness of their initial crocodile-inspired design was tested in the animal's natural habitat (Uganda, Africa) and subsequently enhanced with additional sensors, cameras and computer. The improvements to the software framework involved a smartphone user interface visualizing the robot's state and camera feed to improve the ease of use for the operator. Using force sensors, the locomotion controller is expanded with a set of reflex control modules. It is demonstrated how these modules improve the robot's performance on rough and unstructured terrain. The robot's design and its low profile allow it to traverse low passages. To also tackle narrow passages like pipes, an unconventional crawling gait is explored. While using it, the robot lies on the ground and pushes against the pipe walls to move the body. To achieve such a task, several new control and estimation modules were developed. By exploring these problems, this thesis illustrates fruitful interactions that can take place between robotics, biology and paleontology