Adaptive Control of Arm Movement based on Cerebellar Model

This study is an attempt to take advantage of a cerebellar model to control a biomimetic arm. Aware that a variety of cerebellar models with different levels of details has been developed, we focused on a high-level model called MOSAIC. This model is thought to be able to describe the cerebellar functionality without getting into the details of the neural circuitry. To understand where this model exactly fits, we glanced over the biology of the cerebellum and a few alternative models. Certainly, the arm control loop is composed of other components. We reviewed those elements with emphasis on modeling for our simulation. Among these models, the arm and the muscle system received the most attention. The musculoskeletal model tested independently and by means of optimization techniques, a human-like control of arm through muscle activations achieved. We have discussed how MOSAIC can solve a control problem and what drawbacks it has. Consequently, toward making a practical use of MOSAIC model, several ideas developed and tested. In this process, we borrowed concepts and methods from the control theory. Specifically, known schemes of adaptive control of a manipulator, linearization and approximation were utilized. Our final experiment dealt with a modified/adjusted MOSAIC model to adaptively control the arm. We call this model ORF-MOSAIC (Organized by Receptive Fields MOdular Selection And Identification for Control). With as few as 16 modules, we were able to control the arm in a workspace of 30 x 30 cm. The system was able to adapt to an external field as well as handling new objects despite delays. The discussion section suggests that there are similarities between microzones in the cerebellum and the modules of this new model

Detection of Neuraminidase Activity in Pseudomonas aeruginosa PAO1

Objective(s)Some properties of neuraminidase produced by Pseudomonas aeruginosa PAO1 growth in a defined medium (BHI) were examined and evaluated for its features.Materials and MethodsThe obtained supernatant enzyme of P. aeruginosa PAO1 cultures was used in a sensitive fluorometric assay by using 2'-(4-methylumbelliferyl) α-D-N acetylneuraminic acid as substrate. As hydrolyzing MUN with neuraminidase; free N-acetylneuraminic acid and 4-methylumbelliferone were formed with a shift in the fluorescence spectra from 315/374 nm (substrate) to 365/450 nm (product). Enzyme activity was then measured by the fluorescence of 4-methylumbelliferone at 450 nm.ResultsAmong the culture media to determine the enzyme production, the highest production of P. aeruginosa PAO1 neuraminidase was found in BHI culture media. Neuraminidase production in P. aeruginosa PAO1 paralleled bacterial growth in defined medium (BHI) and was maximal in the late logarithmic phase of growth but decreased during the stationary phase, probably due to protease production or thermal instability. The neuraminidase of P. aeruginosa PAO1 possessed an optimum temperature of 56 °C and the activity was pH-dependent with maximal activity at pH 5. Heating the enzyme at 56 °C for 45 min in the presence of bovine serum albumin destroyed 33.1% of the activity while the addition of Ca+2, EDTA and N-acetyl neuraminic acid (NANA) decreased activity markedly. ConclusionOverall, the results indicated that neuraminidase of P. aeruginosa PAO1 is more an extracellular enzyme than K. pneumonia neuraminidase is

An Analytic Solution to Fixed-Time Point-to-Point Trajectory Planning

We derive an analytic solution to the problem of fixed-time trajectory generation with a quadratic cost function under velocity and acceleration constraints. This problem has a wide range of applications within motion planning. The advantage of the analytic solution compared to numerical optimization of the discretized problem is the unlimited resolution of the solution and the efficiency of the calculation, allowing sensor-based replanning and on-line trajectory generation

Ball-and-finger system: modeling and optimal trajectories

A rigid-body model of a finger interacting with a trackball is considered. The proposed system is a suitable candidate for studying trajectory generation when interaction plays an important role, such as in assembly and manipulation tasks. The mathematical model consists of a ball with a spherical joint constraint, a finger with three degrees of freedom, and the Coulomb friction model. From first principles, we derive a hybrid, high-index differential-algebraic equation for modeling the system dynamics, which is used for both simulation and finding optimal trajectories. For this problem, task planning, path planning, and trajectory generation are strongly interrelated, which makes using an integrated approach to trajectory generation inevitable. Moreover, the trajectory generation algorithm has to handle a number of important features, e.g., unilateral and non-holonomic constraints

Online Minimum-Jerk Trajectory Generation

Robotic trajectory generation is reformulated as a controller design problem. For minimum-jerk trajectories, an optimal controller using the Hamilton-Jacobi-Bellman equation is derived. The controller instantaneously updates the trajectory in a closed-loop system as a result of the changes in the reference signal. The resulting trajectories coincide with piece-wise fifth-order polynomial trajectories for piece-wise constant target states. Since having hard constraints on the final time poses certain robustness issues, a smooth transition between the finite-horizon and an infinite-horizon problem is developed. This enables to switch softly to a tracking mode when a moving target is reached

Deep Vision for Prosthetic Grasp

Ph. D. ThesisThe loss of the hand can limit the natural ability of individuals in grasping and manipulating objects and affect their quality of life. Prosthetic hands can aid the users in overcoming these limitations and regaining their ability. Despite considerable technical advances, the control of commercial hand prostheses is still limited to few degrees of freedom. Furthermore, switching a prosthetic hand into a desired grip mode can be tiring. Therefore, the performance of hand prostheses should improve greatly. The main aim of this thesis is to improve the functionality, performance and flexibility of current hand prostheses by augmentation of current commercial hand prosthetics with a vision module. By offering the prosthesis the capacity to see objects, appropriate grip modes can be determined autonomously and quickly. Several deep learning-based approaches were designed in this thesis to realise such a vision-reinforced prosthetic system. Importantly, the user, interacting with this learning structure, may act as a supervisor to accept or correct the suggested grasp. Amputee participants evaluated the designed system and provided feedback. The following objectives for prosthetic hands were met: 1. Chapter 3: Design, implementation and real-time testing of a semi-autonomous vision-reinforced prosthetic control structure, empowered with a baseline convolutional neural network deep learning structure. 2. Chapter 4: Development of advanced deep learning structure to simultaneously detect and estimate grasp maps for unknown objects, in presence of ambiguity. 3. Chapter 5: Design and development of several deep learning set-ups for concurrent depth and grasp map as well as human grasp type prediction. Publicly available datasets, consisting of common graspable objects, namely Amsterdam library of object images (ALOI) and Cornell grasp library were used within this thesis. Moreover, to have access to real data, a small dataset of household objects was gathered for the experiments, that is Newcastle Grasp Library.EPSRC, School of Engineering Newcastle University

Trajectory Generation for Assembly Tasks Via Bilateral Teleoperation

Abstract in UndeterminedFor assembly tasks, the knowledge of both trajectory and forces are usually required. Consequently, we may use kinesthetics or teleoperation for recording human demonstrations. In order to have a more natural interaction, the operator has to be provided with a sense of touch. We propose a bilateral teleoperation system which is customized for this purpose. We introduce different coordinate frames to make the design of a 6-DOF teleoperation straightforward. Moreover, we suggest using tele-admittance, which simplifies instructing the robot. The compliance due to the slave controller allows the robot to react quickly and reduces the risk of damaging the workpiece