A Bio-inspired Autonomous Navigation Controller for Differential Mobile Robots Based on Crowd Dynamics

This article extends ideas from crowd dynamics to a navigationcontroller for mobile robots. Each mobile robot is consideredas an agent, associated to a comfort zone with a certain radius, whichcreates a repulsive force when this comfort zone is violated by its environmentor by another agent, therefore avoiding collisions. Meanwhile,attractive forces drive the agents from their instantaneous position toa goal position. The resulting navigation controller is tested by simulationsand experiments. It is found that simulations capture the globaldynamic behavior that is shown in experiments, showing robustness ofthe proposed navigation controller.This article extends ideas from crowd dynamics to a navigation controller for mobile robots. Each mobile robot is considered as an agent, associated to a comfort zone with a certain radius, which creates a repulsive force when this comfort zone is violated by its environment or by another agent, therefore avoiding collisions. Meanwhile, attractive forces drive the agents from their instantaneous position to a goal position. The resulting navigation controller is tested by simulations and experiments. It is found that simulations capture the global dynamic behavior that is shown in experiments, showing robustness of the proposed navigation controller

A review on initialization methods for nonnegative matrix factorization: Towards omics data experiments

Nonnegative Matrix Factorization (NMF) has acquired a relevant role in the panorama of knowledge extraction, thanks to the peculiarity that non-negativity applies to both bases and weights, which allows meaningful interpretations and is consistent with the natural human part-based learning process. Nevertheless, most NMF algorithms are iterative, so initialization methods affect convergence behaviour, the quality of the final solution, and NMF performance in terms of the residual of the cost function. Studies on the impact of NMF initialization techniques have been conducted for text or image datasets, but very few considerations can be found in the literature when biological datasets are studied, even though NMFs have largely demonstrated their usefulness in better understanding biological mechanisms with omic datasets. This paper aims to present the state-of-the-art on NMF initialization schemes along with some initial considerations on the impact of initialization methods when microarrays (a simple instance of omic data) are evaluated with NMF mechanisms. Using a series of measures to qualitatively examine the biological information extracted by a given NMF scheme, it preliminary appears that some information (e.g., represented by genes) can be extracted regardless of the initialization scheme used

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Factors Influencing the Effectiveness of Managing Human–Robot Teams

Certain factors can influence the capabilities of a robot–human team by affecting their social and behavioral dynamics in a work environment. But these factors were not known due to the progressive nature of human–robot partnerships and a lack of peer-reviewed literature on the topic. This e-Delphi study aimed to identify and understand these unknown influential factors based on the participants’ insights. The overarching research question asked about the need to determine factors that might influence the effectiveness of managing human-robot teams. The basis for the conceptual framework for this study was the theory of communication used in organizational management. Twelve participants with backgrounds in management, software engineering, robotics, or a combination answered open-ended and closed-ended questions in three data rounds through SurveyMonkey. Excel and Python were used to analyze the data. Eight factors, and 10 subfactors emerged from the analysis and showed a relationship to the influential dynamics in communication, trust, sociostructural entanglement, and decision-making, which are integral to organizational and human–robot workforce management. Human–robot workforce management is a new paradigm in organization management. The results of this study may engender positive social change by augmenting human capabilities, such as assisting vulnerable or challenged individuals who require continuous assistance, performing activities detrimental to human life, and performing lifesaving measures, such as search and rescue

Wayfinding and Perception Abilities for Pedestrian Simulations

Computer simulations of pedestrian dynamics are common and reliable tools in order to evaluate safety risks of facilities. However, still many soft- ware frameworks for evacuation simulations imply the assumption that all simulated pedestrians are familiar with their environment and therefore take the shortest path to the outside. In fact, the spatial knowledge of people generally varies. Thus, the assumption that all persons of a build- ing possess comprehensive spatial knowledge is a rough approximation of the reality. Especially for simulations in complex buildings the reliability of this approximation is questionable. In order to make simulations of pedestrian dynamics more reliable in this regard, this thesis introduces a new software framework. This framework provides the possibility to predict route choices of a group of people with varying spatial knowledge degrees. Therefor, the framework also considers selected wayfinding strategies that are applied beside the use of spatial memories. These are using signage, using generalized knowledge about the structure of buildings, and search strategies. In addition, three studies have been conducted in order to investigate wayfinding abilities and strategies of people in office buildings and subway stations. The results of the studies are discussed and are used to calibrate and test the models of the new software framework. Finally, the framework is applied to conduct a case study of an evacuation scenario in a subway station. The case study turns out that the egress time in the station is strongly dependent on the wayfinding strategies and abilities of the occupants. This outcome suggests that the proper consideration and prediction of route choices is relevant and necessary for reliable evacuation simulations.Computersimulationen von Fußgängerströmen sind heutzutage ein gängiges Hilfsmittel, wenn es darum geht, Sicherheitsrisiken eines geplanten Neubaus oder Bestandsobjektes im Vorfeld zu erkennen und zu analysieren. Die Mehrheit der Modelle für die Routenwahl von Fußgängern legt die Annahme zugrunde, dass Menschen sich für einen Weg entscheiden, deren zurückzulegende Strecke möglichst kurz ist oder deren Reisezeit möglichst klein ist. Dies impliziert, dass sämtliche Räume, Ausgänge, Korridore, etc. jedem Fußgänger bekannt sind. Diese Annahme kann im Besonderem bei der Betrachtung von komplexen Gebäuden nur als starke Vereinfachung der menschlichen Orientierung bzw. Wegfindung angesehen werden. Um Evakuierungssimulation diesbezüglich zu verbessern bzw. belastbarer zu machen, stellt die vorliegende Thesis ein neues Software-Framework vor. Dieses bietet die Möglichkeit, auch Fußgänger bzw. deren Routenwahl abzubilden, die nur Teile des Gebäudes kennen oder denen das Gebäude unbekannt ist. Die Modelle des Frameworks berücksichtigen hierbei die Anwendung von räumlichem Wissen (kognitive Karte), die Nutzung der Fluchtwegsbeschilderung und die Verwendung von generalisiertem Wissen über Gebäudestrukturen. Des Weiteren wurden drei Studien zur Untersuchung der Wegewahl von Personen in Bürogebäuden und U-Bahnhöfen durchgeführt. Die Ergebnisse der Studien werden in dieser Thesis diskutiert und zur Kalibrierung und Prüfung der Modelle herangezogen. Schließlich wird das Framework im Rahmen einer Simulationsstudie in einer U-Bahnstation angewendet. Diese Studie zeigt, dass die Räumungszeit der Station in Abhängigkeit der Wegfindungsstrategien und -fähigkeiten der Personen stark variieren kann und daher die Berücksichtigung menschlicher Wegfindung in Evakuierungssimulationen relevant ist

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia