Omnidirectional Control of the Hexapod Robot TigerBug

TigerBug is a six legged, hexapod robot built and designed by students in the Rochester Institute of Technology\u27s (RIT) Multi Agent Bio-Robotics Laboratory (MABL). TigerBug is comprised of 18 servo motors, 3 degrees of freedom (DOF) per leg, supported by carbon fiber wrapped foam legs placed in a circular pattern around its hexagon shaped body. In order to control such a complex system, much research has been done in the field of kinematics. There exist two derivations of kinematic solutions, forward and inverse. The forward kinematic (FK) solution tends to be much simpler than its inverse kinematic (IK) counterpart. There has been many methods developed to quickly, and efficiently solve the IK in order to control the position and orientation of a robot. This thesis details the process of developing the IK solution and two gait algorithms for TigerBug. The IK solution was developed by first solving for the FK solution of TigerBug using Denavit-Hartenberg (DH) Parameters. After the FK solution was solved, differentials were applied to each equation in order to solve for the IK solution. Once the IK solution was tested, a fixed gait algorithm was developed in order to understand basic motion control of hexapod locomotion. Once the fixed gait was implemented successfully a rule-based free gait algorithm was developed. The rule-based free gait was accomplished using the rule set governed by restrictiveness to determine when leg state transitions were to occur, as described in the literature. Once implemented, the different combinations of gait parameters were tested for quickness of convergence and efficiency to determine the most optimal set of walking parameters for TigerBug

The kinematics of hyper-redundant robot locomotion

This paper considers the kinematics of hyper-redundant (or “serpentine”) robot locomotion over uneven solid terrain, and presents algorithms to implement a variety of “gaits”. The analysis and algorithms are based on a continuous backbone curve model which captures the robot's macroscopic geometry. Two classes of gaits, based on stationary waves and traveling waves of mechanism deformation, are introduced for hyper-redundant robots of both constant and variable length. We also illustrate how the locomotion algorithms can be used to plan the manipulation of objects which are grasped in a tentacle-like manner. Several of these gaits and the manipulation algorithm have been implemented on a 30 degree-of-freedom hyper-redundant robot. Experimental results are presented to demonstrate and validate these concepts and our modeling assumptions

In silico case studies of compliant robots: AMARSI deliverable 3.3

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

Stance Control Inspired by Cerebellum Stabilizes Reflex-Based Locomotion on HyQ Robot

Advances in legged robotics are strongly rooted in animal observations. A clear illustration of this claim is the generalization of Central Pattern Generators (CPG), first identified in the cat spinal cord, to generate cyclic motion in robotic locomotion. Despite a global endorsement of this model, physiological and functional experiments in mammals have also indicated the presence of descending signals from the cerebellum, and reflex feedback from the lower limb sensory cells, that closely interact with CPGs. To this day, these interactions are not fully understood. In some studies, it was demonstrated that pure reflex-based locomotion in the absence of oscillatory signals could be achieved in realistic musculoskeletal simulation models or small compliant quadruped robots. At the same time, biological evidence has attested the functional role of the cerebellum for predictive control of balance and stance within mammals. In this paper, we promote both approaches and successfully apply reflex-based dynamic locomotion, coupled with a balance and gravity compensation mechanism, on the state-of-art HyQ robot. We discuss the importance of this stability module to ensure a correct foot lift-off and maintain a reliable gait. The robotic platform is further used to test two different architectural hypotheses inspired by the cerebellum. An analysis of experimental results demonstrates that the most biologically plausible alternative also leads to better results for robust locomotion

MOTION CONTROL SIMULATION OF A HEXAPOD ROBOT

This thesis addresses hexapod robot motion control. Insect morphology and locomotion patterns inform the design of a robotic model, and motion control is achieved via trajectory planning and bio-inspired principles. Additionally, deep learning and multi-agent reinforcement learning are employed to train the robot motion control strategy with leg coordination achieves using a multi-agent deep reinforcement learning framework. The thesis makes the following contributions: First, research on legged robots is synthesized, with a focus on hexapod robot motion control. Insect anatomy analysis informs the hexagonal robot body and three-joint single robotic leg design, which is assembled using SolidWorks. Different gaits are studied and compared, and robot leg kinematics are derived and experimentally verified, culminating in a three-legged gait for motion control. Second, an animal-inspired approach employs a central pattern generator (CPG) control unit based on the Hopf oscillator, facilitating robot motion control in complex environments such as stable walking and climbing. The robot\u27s motion process is quantitatively evaluated in terms of displacement change and body pitch angle. Third, a value function decomposition algorithm, QPLEX, is applied to hexapod robot motion control. The QPLEX architecture treats each leg as a separate agent with local control modules, that are trained using reinforcement learning. QPLEX outperforms decentralized approaches, achieving coordinated rhythmic gaits and increased robustness on uneven terrain. The significant of terrain curriculum learning is assessed, with QPLEX demonstrating superior stability and faster consequence. The foot-end trajectory planning method enables robot motion control through inverse kinematic solutions but has limited generalization capabilities for diverse terrains. The animal-inspired CPG-based method offers a versatile control strategy but is constrained to core aspects. In contrast, the multi-agent deep reinforcement learning-based approach affords adaptable motion strategy adjustments, rendering it a superior control policy. These methods can be combined to develop a customized robot motion control policy for specific scenarios

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

Climbing and Walking Robots

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

Streamlined sim-to-real transfer for deep-reinforcement learning in robotics locomotion

Legged robots possess superior mobility compared to other machines, yet designing controllers for them can be challenging. Classic control methods require engineers to distill their knowledge into controllers, which is time-consuming and limiting when approaching dynamic tasks in unknown environments. Conversely, learning- based methods that gather knowledge from data can potentially unlock the versatility of legged systems. In this thesis, we propose a novel approach called CPG-Actor, which incor- porates feedback into a fully differentiable Central Pattern Generator (CPG) formulation using neural networks and Deep-Reinforcement Learning (RL). This approach achieves approximately twenty times better training performance compared to previous methods and provides insights into the impact of training on the distribution of parameters in both the CPGs and MLP feedback network. Adopting Deep-RL to design controllers comes at the expense of gathering extensive data, typically done in simulation to reduce time. However, controllers trained with data collected in simulation often lose performance when deployed in the real world, referred to as the sim-to-real gap. To address this, we propose a new method called Extended Random Force Injection (ERFI), which randomizes only two parameters to allow for sim-to-real transfer of locomotion controllers. ERFI demonstrated high robustness when varying masses of the base, or attaching a manipulator arm to the robot during testing, and achieved competitive performance comparable to standard randomization techniques. Furthermore, we propose a new method called Roll-Drop to enhance the robustness of Deep-RL policies to observation noise. Roll-Drop introduces dropout during rollout, achieving an 80% success rate when tested with up to 25% noise injected in the observations. Finally, we adopted model-free controllers to enable omni-directional bipedal lo- comotion on point feet with a quadruped robot without any hardware modification or external support. Despite the limitations posed by the quadruped’s hardware, the study considers this a perfect benchmark task to assess the shortcomings of sim- to-real techniques and unlock future avenues for the legged robotics community. Overall, this thesis demonstrates the potential of learning-based methods to design dynamic and robust controllers for legged robots while limiting the effort needed for sim-to-real transfer

Biped locomotion control through a biologically-inspired closed-loop controller

Dissertação de mestrado integrado em Engenharia BiomédicaCurrently motor disability in industrialized countries due to neural and physical impairments is an increasingly worrying phenomenon and the percentage of patients is expected to be increasing continuously over the coming decades due to a process of ageing the world is undergoing. Additionally, rising retirement ages, higher demand of elderly people for an independent, dignified life and mobility, huge cost in the provision of health care are some other determinants that motivate the restoration of motor function as one of the main goals of rehabilitation. Modern concepts of motor learning favor a task-specific training in which all movements in daily life should be trained/assisted repetitively in a physically correct fashion. Considering the functional activity of the neuronal circuits within the spinal cord, namely the central pattern generator (CPG), as the foundation to human locomotion, motor relearning should be based on intensive training strategies directed to the stimulation and reorganization of such neural pathways through mechanisms addressed by neural plasticity. To this end, neuromodelings are required to simulate the human locomotion control to overcome the current technological challenges such as developing smaller, intelligent and cost-effective devices for home and work rehabilitation scenarios which can enable a continuous therapy/ assistance to guide the impaired limbs in a gentle manner, avoiding abrupt perturbations and providing as little assistance as necessary. Biomimetic models, taking neurological and biomechanical inspiration from biological animals, have been embracing these challenges and developing effective solutions on refining the locomotion models in terms of energy efficiency, simplicity in the structure and robust adaptability to environment changes and unexpected perturbations. Thus, the aim target of this work is to study the applicability of the CPG model for gait rehabilitation, either for assistance and/or therapy purposes. Focus is developed on the locomotion control to increase the knowledge of the underlying principles useful for gait restoration, exploring the brainstem-spinal-biomechanics interaction more fully. This study has great application in the project of autonomous robots and in the rehabilitation technology, not only in the project of prostheses and orthoses, but also in the searching of procedures that help to recuperate motor functions of human beings. Encouraging results were obtained which pave the way towards the simulation of more complex behaviors and principles of human locomotion, consequently contributing for improved automated motor rehabilitation adapted to the rehabilitation emerging needs.Actualmente a debilidade motora em países industrializados devido a deficiências neurais e físicas é um fenómeno crescente de apreensão sendo expectável um contínuo aumento do rácio de pacientes nas próximas décadas devido ao processo de envelhecimento. Inclusivé, o aumento da idade de reforma, a maior procura por parte dos idosos para uma mobilidade e vida autónoma e condigna, o elevado custo nos cuidados de saúde são incentivos para a restauração da função motora como um dos objectivos principais da reabilitação. Conceitos recentes de aprendizagem motora apoiam um treino de tarefas específicas no qual movimentos no quotidiano devem ser treinados/assistidos de forma repetitiva e fisicamente correcta. Considerando a actividade funcional dos circuitos neurais na medula, nomeadamente o gerador de padrão central (CPG), como a base da locomoção, a reaprendizagem motora deve-se basear em estratégias intensivas de treino visando a estimulação e reorganização desses vias neurais através de mecanismos abordados pela plasticidade neural. Assim, são necessários modelos neurais para simular o controlo da locomoção humana de modo a superar desafios tecnológicos actuais tais como o desenvolvimento de dispositivos mais compactos, inteligentes e económicos para os cenários de reabilitação domiciliar e laboral que podem permitir uma terapia/assistência contínua na guia dos membros debilitados de uma forma suave, evitando perturbações abruptas e fornecendo assistência na medida do necessário. Modelos biomiméticos, inspirando-se nos princípios neurológicos e biomecânicos dos animais, têm vindo a abraçar esses desafios e a desenvolver soluções eficazes na refinação de modelos de locomoção em termos da eficiência de energia, da simplicidade na estrutura e da adaptibilidade robusta face a alterações ambientais e perturbações inesperadas. Então, o objectivo principal do trabalho é estudar a aplicabilidade do modelo de CPG para a reabilitação da marcha, para efeitos de assistência e/ou terapia. É desenvolvido um foco no controlo da locomoção para maior entendimento dos princípios subjacentes úteis para a recuperação da marcha, explorando a interacção tronco cerebral-espinal medula-biomecânica de forma mais detalhada. Este estudo tem potencial aplicação no projecto de robôs autónomos e na tecnologia de reabilitação, não só no desenvolvimento de ortóteses e próteses, mas também na procura de procedimentos úteis para a recuperação da função motora. Foram obtidos resultados promissores susceptíveis de abrir caminho à simulação de comportamentos e princípios mais complexos da marcha, contribuindo consequentemente para uma aprimorada reabilitação motora automatizada adaptada às necessidades emergentes