Computer simulation of a pilot in V/STOL aircraft control loops

The objective was to develop a computerized adaptive pilot model for the computer model of the research aircraft, the Harrier II AV-8B V/STOL with special emphasis on propulsion control. In fact, two versions of the adaptive pilot are given. The first, simply called the Adaptive Control Model (ACM) of a pilot includes a parameter estimation algorithm for the parameters of the aircraft and an adaption scheme based on the root locus of the poles of the pilot controlled aircraft. The second, called the Optimal Control Model of the pilot (OCM), includes an adaption algorithm and an optimal control algorithm. These computer simulations were developed as a part of the ongoing research program in pilot model simulation supported by NASA Lewis from April 1, 1985 to August 30, 1986 under NASA Grant NAG 3-606 and from September 1, 1986 through November 30, 1988 under NASA Grant NAG 3-729. Once installed, these pilot models permitted the computer simulation of the pilot model to close all of the control loops normally closed by a pilot actually manipulating the control variables. The current version of this has permitted a baseline comparison of various qualitative and quantitative performance indices for propulsion control, the control loops and the work load on the pilot. Actual data for an aircraft flown by a human pilot furnished by NASA was compared to the outputs furnished by the computerized pilot and found to be favorable

Integração de dados visuais e inerciais para o equilíbrio de um robô humanóide

Mestrado em Engenharia MecânicaEsta dissertação aborda o problema que consiste na medição do movimento da cabeça de um robot humanóide fundindo dados inerciais e visuais, com o objetivo de obter o output que melhor descreve o movimento da cabeça do humanóide. O seu principal objectivo é perceber e desenvolver um algoritmo usando o Filtro de Kalman, que irá fundir ambas as fontes de dados com o propósito de obter uma nova fonte de informação com um maior grau de confiança. Para cumprir os objectivos, um modelo da cabeça do humanóide, juntamente com as câmaras e os sensores inerciais, vão ser movidos na ponta de um braço robótico industrial, que é usado como grupo de controle (ground truth) no que toca a posição angular. Pontos-chave nos frames obtidos através da câmara, são extra dos e usados para calcular a diferença na posição angular que ocorreu entre frames, que vão mais tarde, juntamente com os dados inerciais obtidos de giroscópios, servir de input a um modelo de um Filtro de Kalman. Uma vez que este dissertação assenta em ferramentas como o Filtro de Kalman, que tem como propósito unir dados de origens diferentes, é essencial que se conheçam os tipos de dados e ferramentas que irão ser utilizados. Assim, várias experiências foram desenvolvidas e estudadas com o intuito de desenvolver o conhecimento nessas matérias. Adicionalmente, erros foram acrescentados aos dados, artificialmente, com o objectivo de emular sensores sensíveis a ruído. No entanto, o sistema continua a ter uma performance positiva.This thesis addresses the problem of measuring a humanoid robot head motion by merging inertial and visual data, in order to obtain an output that will describe the head motion of the robot. Its primary goal is the understanding and development of an algorithm using the Kalman Filter tool, which will merge inertial and visual data, resulting in a more reliable source of information. To accomplish this, a model of a humanoid robot head, including a camera and inertial sensors, are moved on the tip of an industrial robot's arm which is used as ground truth for angular position. Visual features are extracted from the camera images and used to calculate angular displacement and velocity of the camera, which is then merged with angular velocities from a gyroscope and fed into a Kalman Filter, in order to obtain an output. Since this thesis is expected to merge two di erent kinds of data using the Kalman Filter tool, the need to understand both types of data arises, as well as the way the Kalman Filter operates. Therefore, many experiments were developed and studied with the intent of deepening the knowledge on those matters. The results are quite interesting. Additionally, errors are introduced arti cially into the data to emulate noisy sensors, and the system still performs very well

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Vision-based Autonomous Tracking of a Non-cooperative Mobile Robot by a Low-cost Quadrotor Vehicle

The goal of this thesis is the detection and tracking of a ground vehicle, in particular a car-like robot, by a quadrotor. The first challenge to address in any pursuit or tracking scenario is the detection and unique identification of the target. From this first challenge, comes the need to precisely localize the target in a coordinate system that is common to the tracking and tracked vehicles. In most real-life scenarios, the tracked vehicle does not directly communicate information such as its position to the tracking one. From this fact, arises a non-cooperative constraint problem. The autonomous tracking aspect of the mission requires, for both the aerial and ground vehicles, robust pose estimation during the mission. The primary and crucial functions to achieve autonomous behaviors are control and navigation. The principal-agent being the quadrotor, this thesis explains in detail the derivation and analysis of the equations of motion that govern its natural behavior along with the control methods that permit to achieve desired performances. The analysis of these equations reveals a naturally unstable system, subject to non-linearities. Therefore, we explored three different control methods capable of guaranteeing stability while mitigating non-linearities. The first two control methods operate in the linear region and consist of the intuitive Proportional Integrate Derivative controller (PID). The second linear control strategy is represented by an optimal controller that is the Linear Quadratic Regulator controller (LQR). The last and final control method is a nonlinear controller designed from the Sliding Mode Control Theory. In addition to the in-depth analysis, we provide assets and limitations of each control method. In order to achieve the tracking mission, we address the detection and localization problems using respectively visual servoing and frame transform techniques. The pose estimation challenge for the aerial robot is cleared up using Kalman Filtering estimation methods that are also explored in depth. The same estimation method is used to mitigate the ground vehicle’s real-time pose estimation and tracking problem. Analysis results are illustrated using Matlab. A simulation and a real implementation using the Robot Operating System are used to support the obtained results

A State Estimation Framework for Fatigue Monitoring and Prognosis of Minimally Instrumented Structural and Biomechanical Systems

Fatigue damage is the continuous degradation of a material, primarily due to the formation of microcracks and resulting from the repeated application of stress cycles. Traditionally a fatigue analysis was performed during the structural design stage of a machine or structure; however, more recently there has been increased interest in the monitoring and prognosis of fatigue damage in existing and operating structures. In monitoring, the structure already exists and its mechanical properties can be estimated by processing sensor measurements and non-destructive testing. The traditional approach to fatigue monitoring is to carry out a visual inspection, find macroscopic cracks and then predict their growth. This was often carried out by finding changes in dynamic properties of the system, i.e. changes in modal frequencies, mode shapes, and modal damping. Yet in many cases, by the time the cracks grow to a point where they are detectable, the load bearing capacity of the structure has been greatly reduced. Therefore, a preferable approach is to track fatigue damage on the whole structure prior to the appearance of macroscopic cracks. This would allow for higher levels of reliability, larger lead times and reduced risk. Although no exact figures are available, it is estimated that upwards of 50% of mechanical failures in metallic structures can be attributed to fatigue. Structural health monitoring has been extensively studied for structural systems but hasn\u27t been applied to biomechanical systems where biomechanical failure is consistent with the process of mechanical fatigue. The objective of this dissertation is to show that state estimation algorithms, i.e. the Kalman filter, can be successfully formulated to estimate fatigue damage in near-real time for structural and biomechanical systems. The Kalman filter combines dynamic response measurements at minimal spatial locations with a structural model to estimate the response of the dynamical system at all model degrees-of-freedom. The estimates of the dynamic response of the instrumented structural systems are subsequently used for fatigue damage diagnosis and prognosis in combination with an empirical S-N curve. By quantifying the uncertainty in both the state estimate and S-N curve, the fatigue damage index becomes bounded based on a user-defined allowable probability of failure. The main contributions of this dissertation are summarized as follows: i) Development of a fatigue monitoring framework for structural and biomechanical systems; ii) Experimental validation of service life fatigue monitoring in near-real time for statically determinant structures; iii) Uncertainty quantification and propagation of system response and fatigue damage estimates using Kalman filters

Learning Interaction Primitives for Biomechanical Prediction

abstract: This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features. This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.Dissertation/ThesisMasters Thesis Electrical Engineering 201