Development of a Neuromechanical Model for Investigating Sensorimotor Interactions During Locomotion

Recently it has been suggested that the use of neuromechanical simulations could be used to further our understanding of the neural control mechanisms involved in the control of animal locomotion. The models used to carry out these neuromechanical simulations typically consist of a representation of the neural control systems involved in walking and a representation of the mechanical locomotor apparatus. These separate models are then integrated to produce motion of the locomotor apparatus based on signals that are generated by the neural control models. Typically in past neuromechanical simulations of human walking the parameters of the neural control model have been specifically chosen to produce a walking pattern that resembles the normal human walking pattern as closely as possible. Relatively few of these studies have systematically tested the effect of manipulating the control parameters on the walking pattern that is produced by the locomotor apparatus. The goal of this thesis was to develop models of the locomotor control system and the human locomotor apparatus and systematically manipulate several parameters of the neural control system and determine what effects these parameters would have on the walking pattern of the mechanical model. Specifically neural control models were created of the Central Pattern Generator (CPG), feedback mechanisms from muscle spindles and contact sensors that detect when the foot was contact with the ground. Two models of the human locomotor apparatus were used to evaluate the outputs of the neural control systems; the first was a rod pendulum, which represented a swinging lower-limb, while the second was a 5-segment biped model, which included contact dynamics with the ground and a support system model to maintain balance. The first study of this thesis tested the ability of a CPG model to control the frequency and amplitude of the pendulum model of the lower-limb, with a strictly feedforward control mechanism. It was found that the frequency of the pendulum’s motion was directly linked (or entrained) to the frequency of the CPG’s output. It was also found that the amplitude of the pendulum’s motion was affected by the frequency of the CPG’s output, with the greatest amplitude of motion occurring when the frequency of the CPG matched the pendulum’s natural frequency. The effects of altering several other parameters of the pendulum model, such as the initial angle, the magnitude of the applied viscous damping or the moment arms of the muscles, were also analyzed. The second study again used the pendulum model, and added feedback to the neural control model, via output from simulated muscle spindles. The output from these spindle models was used to trigger a simulated stretch reflex. It was found that the addition of feedback led to sensory entrainment of the CPG output to the natural frequency of the pendulum. The effects of altering the muscle spindle’s sensitivity to length and velocity changes were also examined. The ability of this type of feedback system to respond to mechanical perturbations was also analyzed. The third and fourth studies used a biped model of the musculoskeletal system to assess the effects of altering the parameters of the neural control systems that were developed in the first two studies. In the third study, the neural control system consisted only of feedforward control from the CPG model. It was found that the walking speed of the biped model could be controlled by altering the frequency of the CPG’s output. It was also observed that variability of the walking pattern was decreased when there was a moderate level of inhibition between the CPGs of the left and right hip joints. The final study added feedback from muscle receptors and from contact sensors with the ground. It was found that the most important source of feedback was from the contact sensors to the extensor centres of the CPG. This feedback increased the level of extensor activity and produced significantly faster walking speeds when compared to other types of feedback. This thesis was successful in testing the effects of several control parameters of the neural control system on the movement of mechanical systems. Particularly important findings included the importance of connectivity between the CPGs of the left and right hip joints and positive feedback regarding the loading of the limb for establishing an appropriate forward walking speed. It is hoped that the models developed in this thesis can form the basis of future neuromechanical models and that the simulations carried out in this thesis help provide a better understanding of the interactions between neural and mechanical systems during the control of locomotion

Hierarchical neural control of human postural balance and bipedal walking in sagittal plane

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 177-192).The cerebrocerebellar system has been known to be a central part in human motion control and execution. However, engineering descriptions of the system, especially in relation to lower body motion, have been very limited. This thesis proposes an integrated hierarchical neural model of sagittal planar human postural balance and biped walking to 1) investigate an explicit mechanism of the cerebrocerebellar and other related neural systems, 2) explain the principles of human postural balancing and biped walking control in terms of the central nervous systems, and 3) provide a biologically inspired framework for the design of humanoid or other biomorphic robot locomotion. The modeling was designed to confirm neurophysiological plausibility and achieve practical simplicity as well. The combination of scheduled long-loop proprioceptive and force feedback represents the cerebrocerebellar system to implement postural balance strategies despite the presence of signal transmission delays and phase lags. The model demonstrates that the postural control can be substantially linear within regions of the kinematic state-space with switching driven by sensed variables.(cont.) A improved and simplified version of the cerebrocerebellar system is combined with the spinal pattern generation to account for human nominal walking and various robustness tasks. The synergy organization of the spinal pattern generation simplifies control of joint actuation. The substantial decoupling of the various neural circuits facilitates generation of modulated behaviors. This thesis suggests that kinematic control with no explicit internal model of body dynamics may be sufficient for those lower body motion tasks and play a common role in postural balance and walking. All simulated performances are evaluated with respect to actual observations of kinematics, electromyogram, etc.by Sungho JoPh.D

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

Modular Hopping and Running via Parallel Composition

Though multi-functional robot hardware has been created, the complexity in its functionality has been constrained by a lack of algorithms that appropriately manage flexible and autonomous reconfiguration of interconnections to physical and behavioral components. Raibert pioneered a paradigm for the synthesis of planar hopping using a composition of ``parts\u27\u27: controlled vertical hopping, controlled forward speed, and controlled body attitude. Such reduced degree-of-freedom compositions also seem to appear in running animals across several orders of magnitude of scale. Dynamical systems theory can offer a formal representation of such reductions in terms of ``anchored templates,\u27\u27 respecting which Raibert\u27s empirical synthesis (and the animals\u27 empirical performance) can be posed as a parallel composition. However, the orthodox notion (attracting invariant submanifold with restriction dynamics conjugate to a template system) has only been formally synthesized in a few isolated instances in engineering (juggling, brachiating, hexapedal running robots, etc.) and formally observed in biology only in similarly limited contexts. In order to bring Raibert\u27s 1980\u27s work into the 21st century and out of the laboratory, we design a new family of one-, two-, and four-legged robots with high power density, transparency, and control bandwidth. On these platforms, we demonstrate a growing collection of $\{$body, behavior$\}$ pairs that successfully embody dynamical running / hopping ``gaits\u27\u27 specified using compositions of a few templates, with few parameters and a great deal of empirical robustness. We aim for and report substantial advances toward a formal notion of parallel composition---embodied behaviors that are correct by design even in the presence of nefarious coupling and perturbation---using a new analytical tool (hybrid dynamical averaging). With ideas of verifiable behavioral modularity and a firm understanding of the hardware tools required to implement them, we are closer to identifying the components required to flexibly program the exchange of work between machines and their environment. Knowing how to combine and sequence stable basins to solve arbitrarily complex tasks will result in improved foundations for robotics as it goes from ad-hoc practice to science (with predictive theories) in the next few decades

Optimality, Objectives, and Trade-Offs in Motor Control under Uncertainty

Biological motor control involves multiple objectives and constraints. In this thesis, I investigated the influence of uncertainty on biological sensorimotor control and decision-making, considering various objectives. In the first study, I used a simple biped walking model simulation to study the control of a rhythmic movement under uncertainty. Uncertainty necessitates a more sophisticated form of motor control involving internal model and sensing, and their effective integration. The optimality of the neural pattern generator incorporating sensory information was shown to be dependent on the relative amount of physical disturbance and sensor noise. When the controller was optimized for state estimation, other objectives of improved energy efficiency, reduced variability, and reduced number of falls were also satisfied. In the second study, human participants performed regression and classification tasks on visually presented scatterplot data. The tasks involved a trade-off between acting on small but prevalent errors and acting on big but scarce errors. We used inverse optimization to characterize the loss function used by humans in these regression and classification tasks, and found that these loss functions change systematically as the data sparsity changed. Despite being highly variable, there were overall shifts towards compensating for prevalent small errors more when the sparsity of the visual data decreased. In the third study, I extended the pattern recognition tasks to include visually mediated force tracking. When participants tracked force targets with visual noise, we observed a slight yet consistent force tracking bias. This bias, which increased with noise, was not explained by commonly hypothesized objectives such as a tendency to reduce effort while regulating error. Additional experiments revealed that a model balancing error reduction and transition reduction tendencies effectively explained and predicted experimental data. Transition reduction tendency was further separated into recency bias and central tendency bias. Notably, this bias disappeared when the task became purely visual, suggesting that such biases could be task-dependent. These findings across the three studies provide useful insights into understanding how uncertainty changes objectives and their trade-offs in biological motor control, and in turn, results in a different control strategy and behaviors

Humanoid Robots

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

Simulating a Flexible Robotic System based on Musculoskeletal Modeling

Humanoid robotics offers a unique research tool for understanding the human brain and body. The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research, with the recent advent of complex humanoid systems. This work presents the design and development of a new-generation bipedal robot. Its modeling and simulation has been realized by using an open-source software to create and analyze dynamic simulation of movement: OpenSim. Starting from a study by Fuben He, our model aims to be used as an innovative approach to the study of a such type of robot in which there are series elastic actuators represented by active and passive spring components in series with motors. It has provided of monoarticular and biarticular joint in a very similar manner to human musculoskeletal model. This thesis is only the starting point of a wide range of other possible future works: from the control structure completion and whole-body control application, to imitation learning and reinforcement learning for human locomotion, from motion test on at ground to motion test on rough ground, and obviously the transition from simulation to practice with a real elastic bipedal robot biologically-inspired that can move like a human bein

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