Human and robot arm control using the minimum variance principle

Many computational models of human upper limb movement successfully capture some features of human movement, but often lack a compelling biological basis. One that provides such a basis is Harris and Wolpert’s minimum variance model. In this model, the variance of the hand at the end of a movement is minimised, given that the controlling signal is subject to random noise with zero mean and standard deviation proportional to the signal’s amplitude. This criterion offers a consistent explanation for several movement characteristics. This work formulates the minimum variance model into a form suitable for controlling a robot arm. This implementation allows examination of the model properties, specifically its applicability to producing human-like movement. The model is subsequently tested in areas important to studies of human movement and robotics, including reaching, grasping, and action perception. For reaching, experiments show this formulation successfully captures the characteristics of movement, supporting previous results. Reaching is initially performed between two points, but complex trajectories are also investigated through the inclusion of via- points. The addition of a gripper extends the model, allowing production of trajectories for grasping an object. Using the minimum variance principle to derive digit trajectories, a quantitative explanation for the approach of digits to the object surface is provided. These trajectories also exhibit human-like spatial and temporal coordination between hand transport and grip aperture. The model’s predictive ability is further tested in the perception of human demonstrated actions. Through integration with a system that performs perception using its motor system offline, in line with the motor theory of perception, the model is shown to correlate well with data on human perception of movement. These experiments investigate and extend the explanatory and predictive use of the model for human movement, and demonstrate that it can be suitably formulated to produce human-like movement on robot arms.Open acces

Pressure forces on sediment particles in turbulent open-channel flow : a laboratory study

Acknowledgements This research was sponsored by EPSRC grant EP/G056404/1 and their financial support is greatly appreciated. We also acknowledge Dr S. Cameron, who developed the PIV system and its algorithms. The design and construction of pressure sensors was carried out at the workshop and the experiments were conducted in the fluids laboratory at the University of Aberdeen. We therefore express our gratitude to the workshop and laboratory technicians and also to Mr M. Witz and Mr S. Gretland for their assistance in carrying out these experiments. The authors would also like to thank Professor J. Frohlich, Professor M. Uhlmann, Dr C.-B. Clemens and Mr B. Vowinckel for their useful suggestions and discussions throughout the course of this project. The Associate Editor Professor I. Marusic and four anonymous reviewers provided many useful and insightful comments and suggestions that have been gratefully incorporated into the final version.Peer reviewedPublisher PD

Learning the Optimal Control of Coordinated Eye and Head Movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements

The Fourier analysis of saccadic eye movements

This thesis examines saccadic eye movements in the frequency domain and develops sensitive tools for characterising their dynamics. It tests a variety of saccade models and provides the first strong empirical evidence that saccades are time-optimal. By enabling inferences on the neural command, it also allows for better clinical differentiation of abnormalities and the evaluation of putative mechanisms for the development of congenital nystagmus. Chapters 3 and 4 show how Fourier transforms reveal sharp minima in saccade frequency spectra, which are robust to instrument noise. The minima allow models based purely on the output trajectory, purely on the neural input, or both, to be directly compared and distinguished. The standard, most commonly accepted model based on bang-bang control theory is discounted. Chapter 5 provides the first empirical evidence that saccades are time-optimal by demonstrating that saccade bandwidths overlap across amplitude onto a single slope at high frequencies. In Chapter 6, the overlap also allows optimal (Wiener) filtering in the frequency domain without a priori assumptions. Deconvolution of the aggregate neural driving signal is then possible for current models of the oculomotor plant. The final two chapters apply these Fourier techniques to the quick phases of physiological (optokinetic) nystagmus and of pathological (congenital) nystagmus. These quick phases are commonly assumed to be saccadic in origin. This assumption is thoroughly tested and found to hold, but with subtle differences implying that the smooth pursuit system interacts with the saccade system during the movement. This interaction is taken into account in Chapter 8 in the assessment of congenital nystagmus quick phases, which are found to be essentially normal. Congenital nystagmus models based on saccadic abnormalities are appraised

Intrinsic and Extrinsic Biomechanical Factors in a Co-adaptive ECoG-based Brain Computer Interface

Paralysis, due to spinal cord injury, amyotrophic lateral sclerosis (ALS), or stroke, is the result of severed communication between the brain and the motor periphery. Brain computer interfaces (BCIs) are neuroprosthetic devices that create novel communication pathways by measuring and transforming neural activity into operational commands. State of the art BCI systems measure brain activity using penetrating electrode arrays able to record from hundreds of individual cortical neurons simultaneously. Unfortunately, these systems are highly susceptible to signal degradation which limits their efficacy to 1-2 years. However, electrocorticography (ECoG) signals recorded from the surface of the brain deliver a more competitive balance between surgical risk, long-term stability, signal bandwidth, and signal-to-noise ratio when compared to both the aforementioned intracortical systems and the more common non-invasive electroencephalography (EEG) technologies. Historically, neural signals for controlling a computer cursor or robotic arm have been mapped to extrinsic, kinematic (i.e. position or velocity) variables. Although this strategy is adequate for use in simple environments, it may not be ideal for control of real-world prosthetic devices that are subject to external and unexpected forces. When reaching for an object, the trajectory of the hand through space can be defined in either extrinsic (e.g. Cartesian) or intrinsic (e.g. joint angles, muscle forces) frames of reference. During this movement, the brain has to perform a series of sensorimotor transformations that involve solving a complex, 2nd order differential equation (i.e. musculoskeletal biomechanics) in order to determine the appropriate muscle activations. Functional neuromuscular stimulation (FNS) is a desirable BCI application because it attempts to restore motor function to paralyzed limbs through electrical excitation of muscles. Rather than applying the conventional extrinsic kinematic control signals to such a system, it may be more appropriate to map neural activity to muscle activation directly and allow the brain to develop its own transfer function. This dissertation examines the application of intrinsic decoding schemes to control an upper limb using ECoG in non-human primates. ECoG electrode arrays were chronically implanted in rhesus monkeys over sensorimotor cortex. A novel multi-joint reaching task was developed to train the subjects to control a virtual arm simulating muscle and inertial forces. Utilizing a co-adaptive algorithm (where both the brain adapts via biofeedback and the decoding algorithm adapts to improve performance), new decoding models were initially built over the course of the first 3-5 minutes of each daily experimental session and then continually adapted throughout the day. Three subjects performed the task using neural control signals mapped to 1) joint angular velocity, 2) joint torque, and 3) muscle forces of the virtual arm. Performance exceeded 97%, 93%, and 89% accuracy for the three control paradigms respectively. Neural control features in the upper gamma frequency bands (70-115 and 130-175 Hz) were found to be directionally tuned in an ordered fashion, with preferred directions varying topographically in the mediolateral direction without distinction between motor and sensory areas. Long-term stability was demonstrated by all three monkeys, which maintained performance at 42, 55, and 57 months post-implantation. These results provide insights into the capabilities of sensorimotor cortex for control of non-linear multi-joint reaching dynamics and present a first step toward design of intrinsic, force-based BCI systems suitable for long-term FNS applications

CFD-DEM Modeling of Spouted Beds With Internal Devices Using PTV

195 p.Esta tesis se centra en la extracción de perfiles de velocidad de sólidos, tanto esféricos como irregulares, en un spouted bed y el análisis de estos valores bajo la influencia de diferentes dispositivos internos en el contactor y caudales. El análisis se ha centrado en un contactor cónico mientras que un contactor de perfil prismático también ha sido utilizado para analizar el efecto de esta geometría en la dinámica del sistema. Estos valores experimentales de sólidos regulares e irregulares han sido modelados y simulados a través de un modelo CFD-DEM en el que la fase continua y discreta se han acoplado, a fin de garantizar simulaciones realistas y capaces de predecir parámetros difíciles de obtener de manera experimental y cruciales para el diseño y escalado de estos tipos de lechos; como son los tiempos de ciclo de los sólidos y la distribución de tiempos de residencia del gas bajo diferentes condiciones. Estos parámetros determinan la capacidad de un sistema y la eficacia a la hora de utilizar el volumen del reactor

The dynamics of finite-size settling particles

Direct numerical simulations of the gravity-induced settling of finite-size particles in triply periodic domains has been performed under dilute conditions. We consider rigid, heavy, finite-size spherical particles. Depending on the particle settling regime the particles may exhibit strong clustering. The particles inside clusters experience larger settling velocities than the average. The mean particle wake is significantly attenuated by the motion of the particles

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion