Low-Cost Skewed Redundant IMU Configuration for State-Space Recovery in a Saturated Environment

Low-cost sensors for state space determination can be used successfully for ground vehicles, robots, unmanned aerial vehicles, and Internet-of-Things applications. When a high fidelity Inertial Measurement Unit (IMU) cannot be obtained for state space determination, low-cost sensors can be used to satisfactory standards, despite their limitations in capabilities, by using various implementation techniques. The research group was experimentally investigating state space information of an unstable flying vehicle for motion simulation validation. The high fidelity motion capture system would intermittently lose track of the flight vehicle which lost critical flight data. The goal was to determine the potential of low-cost off-the-shelf sensors to provide a lower fidelity backup source of data. There were periods during the flight test where the flying vehicle was known to experience rotation rates higher than the saturation limit of the low-cost sensors. The purpose of the experiment was to analyze the ability of a skewed-redundant IMU (SRIMU) configuration to extend the dynamic range of the MEMS gyroscope and to reconstruct body axis rotation rates that would have otherwise been saturated. The experiment was able to determine the potential of low-cost off the shelf IMU sensors in a skewed redundant IMU configuration to reconstruct saturated values. There was success in extending the dynamic range of the sensors in cases where a rotation matrix could be utilized to transform data between reference frames. However there were instances where the dynamic range could not be extended due to relative differences in time between sensors which incurred over the duration of the flight tests

A Novel Path Planning Optimization Algorithm for Semi-Autonomous UAV in Bird Repellent Systems Based in Particle Swarm Optimization

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting became inefficient in the long run, keeping high maintenance and reduced mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this dissertation. This technique was used due to the need for an easy implementation optimization algorithm to start the initial tests. The PSO algorithm is simple and has few control parameters while maintaining a good performance. This path planning optimization algorithm aims to manage the drone's distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm's performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time. However, it is necessary to point out that the path planning optimization algorithm may have difficulty finding a suitable solution if there is a bad ratio between the total distance for path planning and points of interest. The field tests were also essential to understand the algorithm's behavior of the path planning algorithm in the UAV, showing that there is less energy discharged with fewer points of interest, but that do not correlates with the flight time. Also, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted.Anualmente, os danos causados pelas aves em pomares criam perdas monetárias significativas aos agricultores. A aplicação de métodos tradicionais de dispersão de aves, como canhões repelentes de aves e redes nas árvores, torna-se ineficiente a longo prazo, sendo ainda de alta manutenção e de mobilidade reduzida. Devido à sua versatilidade, os Veículos Aéreos Não Tripulados (VANT) podem ser benéficos para resolver este problema. No entanto, devido à baixa capacidade das suas baterias, que se traduz num baixo tempo de voo, é necessário otimizar o planeamento dos caminhos. Nesta dissertação, é apresentado um algoritmo de otimização para planeamento de caminhos para VANT baseado no Particle Swarm Optimization (PSO). Para se iniciarem os primeiros testes do algoritmo proposto, a técnica utilizada foi a supracitada devido à necessidade de um algoritmo de otimização fácil de implementar. O algoritmo PSO é simples e possuí poucos parâmetros de controlo, mantendo um bom desempenho. Este algoritmo de otimização de planeamento de caminhos propõe-se a gerir a distância e o tempo de voo do drone, aplicando técnicas de otimização e de aleatoriedade para superar a sua desvantagem relativamente aos sistemas tradicionais. O desempenho do algoritmo de planeamento de caminhos foi testado em três casos de estudo: dois deles em simulação para testar a variação de cada parâmetro e outro em campo para testar a capacidade da bateria. Todos os casos foram testados nas três situações possíveis: mesma taxa de incidência, taxas diferentes e taxas diferentes sem danos de aves. Os resultados apresentados pelo algoritmo proposto demonstram um erro médio muto reduzido na distância total para o planeamento de caminhos obtido e baixo tempo de execução. Porém, é necessário destacar que o algoritmo pode ter dificuldade em encontrar uma solução adequada se houver uma má relação entre a distância total para o planeamento de caminhos e os pontos de interesse. Os testes de campo também foram essenciais para entender o comportamento do algoritmo na prática, mostrando que há menos energia consumida com menos pontos de interesse, sendo que este parâmetro não se correlaciona com o tempo de voo. Além disso, não há associação entre a velocidade horizontal máxima e o tempo da missão, o que significa que a função de cálculo da distância total para o planeamento de caminhos requer ser ajustada

Computational intelligence for industrial and environmental applications

Computational Intelligence (CI) techniques are being increasingly used for automatic monitoring and control systems, especially regarding industrial and environmental applications, due to their performance, their capabilities in fusing noisy or incomplete data obtained from heterogeneous sensors, and the ability in adapting to variations in the operational conditions. Moreover, the increase in the computational power and the decrease of the size of the computing architectures allowed a more pervasive use of CI techniques in a great variety of situations. In this paper, we propose a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describe how these techniques are integrated in the development of efficient industrial and environmental applications

CONTROL STRATEGY OF MULTIROTOR PLATFORM UNDER NOMINAL AND FAULT CONDITIONS USING A DUAL-LOOP CONTROL SCHEME USED FOR EARTH-BASED SPACECRAFT CONTROL TESTING

Over the last decade, autonomous Unmanned Aerial Vehicles (UAVs) have seen increased usage in industrial, defense, research, and academic applications. Specific attention is given to multirotor platforms due to their high maneuverability, utility, and accessibility. As such, multirotors are often utilized in a variety of operating conditions such as populated areas, hazardous environments, inclement weather, etc. In this study, the effectiveness of multirotor platforms, specifically quadrotors, to behave as Earth-based satellite test platforms is discussed. Additionally, due to concerns over system operations under such circumstances, it becomes critical that multirotors are capable of operation despite experiencing undesired conditions and collisions which make the platform susceptible to on-board hardware faults. Without countermeasures to account for such faults, specifically actuator faults, a multirotors will experience catastrophic failure. In this thesis, a control strategy for a quadrotor under nominal and fault conditions is proposed. The process of defining the quadrotor dynamic model is discussed in detail. A dual-loop SMC/PID control scheme is proposed to control the attitude and position states of the nominal system. Actuator faults on-board the quadrotor are interpreted as motor performance losses, specifically loss in rotor speeds. To control a faulty system, an additive control scheme is implemented in conjunction with the nominal scheme. The quadrotor platform is developed via analysis of the various subcomponents. In addition, various physical parameters of the quadrotor are determined experimentally. Simulated and experimental testing showed promising results, and provide encouragement for further refinement in the future

Assessment of Global Orientation Estimation Using Redundant Inertial and Magnetic Sensors

Não consta

Towards adaptive and autonomous humanoid robots: from vision to actions

Although robotics research has seen advances over the last decades robots are still not in widespread use outside industrial applications. Yet a range of proposed scenarios have robots working together, helping and coexisting with humans in daily life. In all these a clear need to deal with a more unstructured, changing environment arises. I herein present a system that aims to overcome the limitations of highly complex robotic systems, in terms of autonomy and adaptation. The main focus of research is to investigate the use of visual feedback for improving reaching and grasping capabilities of complex robots. To facilitate this a combined integration of computer vision and machine learning techniques is employed. From a robot vision point of view the combination of domain knowledge from both imaging processing and machine learning techniques, can expand the capabilities of robots. I present a novel framework called Cartesian Genetic Programming for Image Processing (CGP-IP). CGP-IP can be trained to detect objects in the incoming camera streams and successfully demonstrated on many different problem domains. The approach requires only a few training images (it was tested with 5 to 10 images per experiment) is fast, scalable and robust yet requires very small training sets. Additionally, it can generate human readable programs that can be further customized and tuned. While CGP-IP is a supervised-learning technique, I show an integration on the iCub, that allows for the autonomous learning of object detection and identification. Finally this dissertation includes two proof-of-concepts that integrate the motion and action sides. First, reactive reaching and grasping is shown. It allows the robot to avoid obstacles detected in the visual stream, while reaching for the intended target object. Furthermore the integration enables us to use the robot in non-static environments, i.e. the reaching is adapted on-the- fly from the visual feedback received, e.g. when an obstacle is moved into the trajectory. The second integration highlights the capabilities of these frameworks, by improving the visual detection by performing object manipulation actions

New Technologies for On-Demand Hand Rehabilitation in the Living Environment after Neurologic Injury

High-dosage rehabilitation therapy enhances neuroplasticity and motor recovery after neurologic injuries such as stroke and spinal cord injury. The optimal exercise dosage necessary to promote upper extremity (UE) recovery is unknown. However, occupational and physical therapy sessions are currently orders of magnitude too low to optimally drive recovery. Taking therapy outside of the clinic and into the living environment using sensing and computer technologies is attractive because it could result in a more cost efficient and effective way to extend therapy dosage. This dissertation developed innovative wearable sensing algorithms and a novel robotic system to enhance hand rehabilitation. We used these technologies to provide on-demand exercise in the living environment in ways not previously achieved, as well as to gain new insights into UE use and recovery after neurologic injuries.Currently, the standard-of-practice for wearable sensing of UE movement after stroke is bimanual wrist accelerometry. While this approach has been validated as a way to monitor amount of UE activity, and has been shown to be correlated with clinical assessments, it is unclear what new information can be obtained with it. We developed two new kinematic metrics of movement quality obtainable from bimanual wrist accelerometry. Using data from stroke survivors, we applied principal component analysis to show that these metrics encode unique information compared to that typically carried by conventional clinical assessments. We presented these results in a new graphical format that facilitates the identification of limb use asymmetries.Wrist accelerometry has the limitation that it cannot isolate functional use of the hand. Previously, we had developed a sensing system, the Manumeter, that quantifies finger movement by sensing magnetic field changes induced by movement of a ring worn on the finger, using a magnetometer array worn at the wrist. We developed, optimized, and validated a calibration-free algorithm, the “HAND” algorithm, for real-time counting of isolated, functional hand movements with the Manumeter. Using data from a robotic wrist simulator, unimpaired volunteers and stroke survivors, we showed that HAND counted movements with ~85% accuracy, missing mainly smaller, slower movements. We also showed that HAND counts correlated strongly with clinical assessments of hand function, indicating validity across a range of hand impairment levels.To date, there have been few attempts to increase hand use and recovery of individuals with a stroke by providing real-time feedback from wearable sensors. We used HAND and the Manumeter to perform a first-of-its-kind randomized controlled trial of the effect of real-time hand movement feedback on hand use and recovery after chronic stroke. We found that real-time feedback on hand movement was ineffective in increasing hand use intensity and improving hand function. We also showed for the first time the non-linear relationship between hand capacity, measured in the laboratory, and actual hand use, measured at-home. Even people with a moderate level of clinical hand function exhibit very low hand use at home. Finally, the challenge of improving hand function for people with moderate to severe injuries highlights the need for novel approaches to rehabilitation. One emerging technique is regenerative rehabilitation, in which regenerative therapies, such as stem cell engraftment, are coupled with intensive rehabilitation. In collaboration with the Department of Veteran Affairs Gordon Mansfield Spinal Cord Injury Translational Collaborative Consortium, we developed a robot for promoting on-demand, hand rehabilitation in a non-human primate model of hemiparetic spinal cord injury that is being used to synergize hand rehabilitation with novel regenerative therapies. Using an innovative bimanual manipulation paradigm, we show that subjects engaged with the device at a similar rate before and after injury across a range of hand impairment severity. We also demonstrate that we could shape relative use of the arm and increase the number of exercise repetitions per reward by changing parameters of the robot. We then evaluated how the peak grip force that the subjects applied to the robot decreased after SCI, demonstrating that it can serve as a potential marker of recovery.These developments provide a foundation for future work in technologies for therapeutic movement rehabilitation in the living environment by establishing: 1) new metrics of upper extremity movement quality; 2) a validated algorithm for achieving a “pedometer for the hand” using wearable magnetometry; 3) a negative clinical trial result on the therapeutic effect of real-time hand feedback after stroke, which begs the question of what can be improved in future trials; 4) the nonlinear relationship between hand movement ability and at-home use, supporting the concept of learned non-use; and 5) the first example of robotic regenerative rehabilitation