3D locomotion biomimetic robot fish with haptic feedback

This thesis developed a biomimetic robot fish and built a novel haptic robot fish system based on the kinematic modelling and three-dimentional computational fluid dynamic (CFD) hydrodynamic analysis. The most important contribution is the successful CFD simulation of the robot fish, supporting users in understanding the hydrodynamic properties around it

Understanding upper-limb movements via neurocomputational models of the sensorimotor system and neurorobotics: where we stand

Roboticists and neuroscientists are interested in understanding and reproducing the neural and cognitive mechanisms behind the human ability to interact with unknown and changing environments as well as to learn and execute fine movements. In this paper, we review the system-level neurocomputational models of the human motor system, and we focus on biomimetic models simulating the functional activity of the cerebellum, the basal ganglia, the motor cortex, and the spinal cord, which are the main central nervous system areas involved in the learning, execution, and control of movements. We review the models that have been proposed from the early of 1970s, when the first cerebellar model was realized, up to nowadays, when the embodiment of these models into robots acting in the real world and into software agents acting in a virtual environment has become of paramount importance to close the perception-cognition-action cycle. This review shows that neurocomputational models have contributed to the comprehension and reproduction of neural mechanisms underlying reaching movements, but much remains to be done because a whole model of the central nervous system controlling musculoskeletal robots is still missing

Robotic design and modelling of medical lower extremity exoskeletons

This study aims to explain the development of the robotic Lower Extremity Exoskeleton (LEE) systems between 1960 and 2019 in chronological order. The scans performed in the exoskeleton system’s design have shown that a modeling program, such as AnyBody, and OpenSim, should be used first to observe the design and software animation, followed by the mechanical development of the system using sensors and motors. Also, the use of OpenSim and AnyBody musculoskeletal system software has been proven to play an essential role in designing the human-exoskeleton by eliminating the high costs and risks of the mechanical designs. Furthermore, these modeling systems can enable rapid optimization of the LEE design by detecting the forces and torques falling on the human muscles

Design and implementation of balance control in a humanoid robot

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaf 28).A proportional derivative control strategy was developed for the purpose of achieving balance in a humanoid robot. An artificial muscle model was adapted which modified physiological parameters for the purpose of controlling a lightweight robot skeleton. Gains were modified as a function of joint angles to permit low gain near the equilibrium point, and consequently to promote a human-like swaying behavior that is energy-efficient. The control strategy was testing by placing a non-zero initial condition on the ankle joint angle and observing the robot, both physically and in simulation, attempt to achieve a stable swaying pattern. This was achieved successfully in a simulation of the robot's mass and inertial parameters, but further efforts must be made to obtain the same behavior in the robot. The ability of a robot to successfully balance using a human-like sway pattern adds another successful biomimetic feature to humanoid robot control and in addition should improve the efficiency of such systems.by Brendan J. Englot.S.B

Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control

Bio­-inspired approaches to the control and modelling of an anthropomimetic robot

Introducing robots into human environments requires them to handle settings designed specifically for human size and morphology, however, large, conventional humanoid robots with stiff, high powered joint actuators pose a significant danger to humans. By contrast, “anthropomimetic” robots mimic both human morphology and internal structure; skeleton, muscles, compliance and high redundancy. Although far safer, their resultant compliant structure presents a formidable challenge to conventional control. Here we review, and seek to address, characteristic control issues of this class of robot, whilst exploiting their biomimetic nature by drawing upon biological motor control research. We derive a novel learning controller for discovering effective reaching actions created through sustained activation of one or more muscle synergies, an approach which draws upon strong, recent evidence from animal and humans studies, but is almost unexplored to date in musculoskeletal robot literature. Since the best synergies for a given robot will be unknown, we derive a deliberately simple reinforcement learning approach intended to allow their emergence, in particular those patterns which aid linearization of control. We also draw upon optimal control theories to encourage the emergence of smoother movement by incorporating signal dependent noise and trial repetition. In addition, we argue the utility of developing a detailed dynamic model of a complete robot and present a stable, physics-­‐‑based model, of the anthropomimetic ECCERobot, running in real time with 55 muscles and 88 degrees of freedom. Using the model, we find that effective reaching actions can be learned which employ only two sequential motor co-­‐‑activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-­‐‑activations can be reconstructed to significant accuracy using weighted combinations of only 13 common fragments, labelled “candidate synergies”. Using these synergies as drivable units the same controller learns the same task both faster and better, however, other reaching tasks perform less well, proportional to dissimilarity; we therefore propose that modifications enabling emergence of a more generic set of synergies are required. Finally, we propose a continuous controller for the robot, based on model predictive control, incorporating our model as a predictive component for state estimation, delay-­‐‑ compensation and planning, including merging of the robot and sensed environment into a single model. We test the delay compensation mechanism by controlling a second copy of the model acting as a proxy for the real robot, finding that performance is significantly improved if a precise degree of compensation is applied and show how rapidly an un-­‐‑compensated controller fails as the model accuracy degrades

Soft manipulators and grippers: A review

Soft robotics is a growing area of research which utilizes the compliance and adaptability of soft structures to develop highly adaptive robotics for soft interactions. One area in which soft robotics has the ability to make significant impact is in the development of soft grippers and manipulators. With an increased requirement for automation, robotics systems are required to perform task in unstructured and not well defined environments; conditions which conventional rigid robotics are not best suited. This requires a paradigm shift in the methods and materials used to develop robots such that they can adapt to and work safely in human environments. One solution to this is soft robotics, which enables soft interactions with the surroundings while maintaining the ability to apply significant force. This review paper assesses the current materials and methods, actuation methods and sensors which are used in the development of soft manipulators. The achievements and shortcomings of recent technology in these key areas are evaluated, and this paper concludes with a discussion on the potential impacts of soft manipulators on industry and society

Dynamic Simulation and Neuromechanical Coordination of Subject-Specific Balance Recovery to Prevent Falls

Falls are the leading cause of fatal and nonfatal injuries in elderly people, resulting in approximately $31 billion in medical costs annually in the U.S. These injuries motivate balance control studies focused on improving stability by identifying prevention strategies for reducing the number of fall events. Experiments provide data about subjects’ kinematic response to loss of balance. However, simulations offer additional insights, and may be used to make predictions about functional outcomes of interventions. Several approaches already exist in biomechanics research to generate accurate models on a subject-by-subject basis. However, these representations typically lack models of the central nervous system, which provides essential feedback that humans use to make decisions and alter movements. Interdisciplinary methods that merge biomechanics with other fields of study may be the solution to fill this gap by developing models that accurately reflect human neuromechanics.Roboticists have developed control systems approaches for humanoid robots simultaneously accomplishing complex goals by coordinating component tasks under priority constraints. Concepts such as the zero-moment point and extrapolated center of mass have been thoroughly evaluated and are commonly used in the design and execution of dynamic robotic systems in order to maintain stability. These established techniques can benefit biomechanical simulations by replacing biological sensory feedback that is unavailable in the virtual environment. Subject-specific simulations can be generated by synthesizing techniques from both robotics and biomechanics and by creating comprehensive models of task-level coordination, including neurofeedback, of movement patterns from experimental data. In this work, we demonstrate how models built on robotic principles that emulate decision making in response to feedback can be trained by biomechanical motion capture data to produce a subject-specific fit. The resulting surrogate can predict a subject’s particular solution to accomplishing the movement goal of recovering balance by controlling component tasks. This research advances biomechanics simulations as we move closer towards the development of a tool capable of anticipating the results of rehabilitation interventions aimed at correcting movement disorders. The novel platform presented here marks the first step towards that goal, and may benefit engineers, researchers, and clinicians interested in balance control and falls in human subjects