Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

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

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Exploring machine learning, real-time bio-feedback, and inertial sensor accuracy for the prevention of running-related injuries

Recreational running is popular, however, incident rates of running related injuries (RRIs) are very high. Predisposition to injury can be assessed through expensive, laboratory-based biomechanical screening. Wearable wireless inertial sensors offer a potential solution, but accurate orientation data are required. This thesis examined the prevention of RRIs, by aiming to improve sensor accuracy, and investigate applications of biofeedback and machine learning. This thesis explored improving (magnetometer-free) orientation accuracy during running, through examination of (i) Z-axis de-drifting, (ii) data-loss (iii) and modifications to the Madgwick filter. Despite some accuracy improvements (i, iii), overall errors were unsuitable for running based applications. Impact loading is associated with RRIs, with thigh angle (quasi-measure of knee-flexion) potentially important in load attenuation. Loading can be altered directly (loading-based biofeedback) or indirectly (technique-based biofeedback), these two types of biofeedback were compared. A mobile phone application was developed providing audio biofeedback to reduce impact accelerations and encourage a ‘softer’ running technique. Both types of feedback reduced loading at the tibia and sacrum, however, tibia loading reduced better with impact accelerations biofeedback, and sacrum loading with thigh angle biofeedback. It would be beneficial to identify runners who may be predisposed to injury. Seven supervised machine learning models were developed to identify runners who may be likely to sustain RRIs, using inertial, kinematic and clinical data collected on 150 prospectively tracked runners. These models resulted in weak predictive accuracy (0.58-0.61 AUC). As we cannot identify runners predisposed to injury, all runners must be recommended for injury prevention interventions. Orientation accuracy was found to be sufficient for relative measures of running technique in the biofeedback app. Future work could investigate biofeedback app use in relation to reduction of RRIs. Additionally, running injury prediction could be examined further with respect to extracting different features (continuous measures) or predicting specific injuries

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Prédiction de trajectoires humaines pour la navigation de robots

Our lives are becoming increasingly influenced by robots. They are no longer limited to working in factories and increasingly appear in shared spaces with humans, to deliver goods and parcels, ferry medications, or give company to elderly people. Therefore, they need to perceive, analyze, and predict the behavior of surrounding people and take collision-free and socially-acceptable actions. In this thesis, we address the problem of (short-term) human trajectory prediction, to enable mobile robots, such as Pepper, to navigate crowded environments. We propose a novel socially-aware approach for prediction of multiple pedestrians. Our model is designed and trained based on Generative Adversarial Networks, which learn the multi-modal distribution of plausible predictions for each pedestrian. Additionally, we use a modified version of this model to perform data-driven crowd simulation. Predicting the location of occluded pedestrians is another problem discussed in this dissertation. Also, we carried out a study on common human trajectory datasets. A list of quantitative metrics is suggested to assess prediction complexity in those datasets.Nos vies sont de plus en plus influencées par les robots. Ils ne se limitent plus à travailler dans les usines et apparaissent de plus en plus dans des espaces partagés avec les humains, pour livrer des biens et des colis, transporter des médicaments ou tenir compagnie à des personnes âgées. Par conséquent, ils doivent percevoir, analyser et prévoir le comportement des personnes qui les entourent et prendre des mesures sans collision et socialement acceptables des actions sans collision et socialement acceptables. Dans cette thèse, nous abordons le problème de la prédiction de la trajectoire humaine (à court terme), afin de permettre aux robots mobiles, tels que Pepper, de naviguer dans des environnements bondés. Nous proposons une nouvelle approche socialement consciente pour la prédiction de plusieurs piétons. Notre modèle est conçu et entraîné sur la base de réseaux adversariaux génératifs, qui apprennent la distribution multimodale des prédictions plausibles pour chaque piéton. De plus, nous utilisons une version modifiée de ce modèle pour effectuer une simulation de foule basée sur des données. La prédiction de l’emplacement des piétons occultés est un autre problème abordé dans cette thèse. Nous avons également réalisé une étude sur des jeux de données courants de trajectoires humaines. Une liste de métriques quantitatives est proposée pour évaluer la complexité de la prédiction dans ces jeux de données

Evolving Fault Tolerant Robotic Controllers

Fault tolerant control and evolutionary algorithms are two different research areas. However with the development of artificial intelligence, evolutionary algorithms have demonstrated competitive performance compared to traditional approaches for the optimisation task. For this reason, the combination of fault tolerant control and evolutionary algorithms has become a new research topic with the evolving of controllers so as to achieve different fault tolerant control schemes. However most of the controller evolution tasks are based on the optimisation of controller parameters so as to achieve the fault tolerant control, so structure optimisation based evolutionary algorithm approaches have not been investigated as the same level as parameter optimisation approaches. For this reason, this thesis investigates whether structure optimisation based evolutionary algorithm approaches could be implemented into a robot sensor fault tolerant control scheme based on the phototaxis task in addition to just parameter optimisation, and explores whether controller structure optimisation could demonstrate potential benefit in a greater degree than just controller parameter optimisation. This thesis presents a new multi-objective optimisation algorithm in the structure optimisation level called Multi-objective Cartesian Genetic Programming, which is created based on Cartesian Genetic Programming and Non-dominated Sorting Genetic Algorithm 2, in terms of NeuroEvolution based robotic controller optimisation. In order to solve two main problems during the algorithm development, this thesis investigates the benefit of genetic redundancy as well as preserving neutral genetic drift in order to solve the random neighbour pick problem during crowding fill for survival selection and investigates how hyper-volume indicator is employed to measure the multi-objective optimisation algorithm performance in order to assess the convergence for Multi-objective Cartesian Genetic Programming. Furthermore, this thesis compares Multi-objective Cartesian Genetic Programming with Non-dominated Sorting Genetic Algorithm 2 for their evolution performance and investigates how Multi-objective Cartesian Genetic Programming could be performing for a more difficult fault tolerant control scenario besides the basic one, which further demonstrates the benefit of utilising structure optimisation based evolutionary algorithm approach for robotic fault tolerant control