An Obstacle-Free and Power Efficient Deployment Algorithm for Wireless Sensor Networks

[[abstract]]This paper proposes a robot-deployment algorithm that overcomes unpredicted obstacles and employs full-coverage deployment with a minimal number of sensor nodes. Without the location information, node placement and spiral movement policies are proposed for the robot to deploy sensors efficiently to achieve power conservation and full coverage, while an obstacle surrounding movement policy is proposed to reduce the impacts of an obstacle upon deployment. Simulation results reveal that the proposed robot-deployment algorithm outperforms most existing robot-deployment mechanisms in power conservation and obstacle resistance and therefore achieves a better deployment performance.[[notice]]補正完

Z-path trajectory mechanism for mobile beacon-assisted localization in wireless sensor networks

A wireless sensor network consists of many sensors that communicate wirelessly to monitor a physical region. In many applications such as warning systems or healthcare services, it is necessary to enhance the captured data with location information. Determining the coordinates of the randomly deployed sensors is known as the problem of localization. A promising solution for statically deployed sensors is to benefit from a mobile beacon-assisted localization. The main challenge is planning an optimum path for the mobile beacon to ensure the full coverage, increase the accuracy of the estimated position and decrease the required time for localization of resource-constrained sensors. So, this research aims at developing a superior trajectory mechanism for mobile beacon-assisted localization to help unknown sensors to efficiently localize themselves. To achieve this purpose; first, a novel trajectory named Z-path is proposed to guarantee fully localized deployed sensors with higher precision since the path reduces collinear beacon positions and promises shorter localization time; second, Z-path transmission power adjustment scheme named Zpower is developed to dynamically and optimally adjust the transmission power for a reliable transmission while conserving the energy consumption for localization by mobile beacon and unknown sensors; third, Z-path obstacle-handling trajectory mechanism is designed to improve the effectiveness of the proposed path toward obstacles which obstruct the path. Finally, the proposed Z-path obstacle handling mechanism is integrated with the developed power adjustment scheme to improve the energy efficiency of the designed obstacle tolerance mechanism. The performance of the proposed trajectory is evaluated by comparing the efficiency with five benchmark trajectories in terms of localization success, accuracy, energy efficiency, time and ineffective position rate, which is a newly introduced metric by this research to measure the collinearity of the trajectories. Simulation results show that Z-path has successfully localized all 250 deployed sensors with higher precision by at least 5.88% improvement than Localization with a Mobile Anchor based on Trilateration (LMAT) trajectory and 58% improvement than random way point. It also serves as a benchmark path with 93 ineffective positions per node localization as compared with LMAT as a second efficient path by 100 collinear positions and faster trajectory for localization. Furthermore, results revealed that Z-power accomplishes better performance in terms of energy consumption as an average 34% for unknown sensors and 25% for mobile beacon than Z-path. In case of obstacle tolerance mechanism, it ensures higher localization performance in terms of accuracy, time and success around 37.5%, 13% and 11% respectively, as compared to Z-path at the presence of obstacles. The handling mechanism integrated with the power control scheme has reduced energy consumption and improved ineffective position rate compared with Z-path handling trajectory by 35.7% and 54.4%, respectively

Development of an Underground Mine Scout Robot

Despite increased safety and improved technology in the mining industry, fatal disasters still occur. Robots have the potential to be an invaluable resource for search and rescue teams to scout dangerous or difficult situations. Existing underground mine search and rescue robots have demonstrated limited success. Identified through literature, the two primary concerns are unreliable locomotion systems and a lack of underground mine environment consideration. HADES, an underground mine disaster scout, addresses these issues with a unique chassis and novel locomotion. A system level design is carried out, addressing the difficulties of underground mine environments. To operate in an explosive atmosphere, a purge and pressurisation system is applied to a fibre glass chassis, with intrinsic safety incorporated into the sensor design. To prevent dust, dirt and water damaging the electronics, ingress protection is applied through sealing. The chassis is invertible, with a low centre of gravity and a roll-axis pivot. This chassis design, in combination with spoked-wheels allows traversal of the debris and rubble of a disaster site. Electrochemical gas sensors are incorporated, along with RGB-D cameras, two-way audio and various other environment sensors. A communication system combining a tether and mesh network is designed, with wireless nodes to increase wireless range and reliability. Electronic hardware and software control are implemented to produce an operational scout robot. HADES is 0.7 × 0.6 × 0.4 m, with a sealed IP65 chassis. The locomotion system is robust and effective, able to traverse most debris and rubble, as tested on the university grounds and at a clean landfill. Bottoming out is the only problem encountered, but can be avoided by approaching obstacles correctly. The motor drive system is able to drive HADES at walking speed (1.4 m/s) and it provides more torque than traction allows. Six Lithium-Polymer batteries enable 2 hours 28 minutes of continuous operation. At 20 kg and ~$7000, HADES is a portable, inexpensive scout robot for underground mine disasters

Subsea inspection and monitoring challenges

Master's thesis in Offshore technology : industrial asset managementThis paper uncovers and suggests solutions for the challenges to control change over time more reliable and cost effective. Front-end concept engineering, design, inspection and monitoring strategies, technologies, systems and methods for Life-of-Field are recommended. Autonomous underwater vehicles (AUV) are identified as a possible cost- efficient opportunity to reduce cost of inspections and monitoring operations while safeguarding asset integrity. A recognized design spiral methodology is used to perform a front-end concept evaluation of an AUV system. Investigation of key technological limitations and new developments within underwater communication, energy storage and wireless power transmission is performed. It further enables opportunities such as AUV recharging station on the seafloor for better utilization. One major learning point is through the use of numerical models and the outcome being a better and more hydro effective hull design. One expectation from this paper may be the aid to collaborating partners in their design work

Evolutionary Robot Swarms Under Real-World Constraints

Tese de doutoramento em Engenharia Electrotécnica e de Computadores, na especialidade de Automação e Robótica, apresentada ao Departamento de Engenharia Electrotécnica e de Computadores da Faculdade de Ciências e Tecnologia da Universidade de CoimbraNas últimas décadas, vários cientistas e engenheiros têm vindo a estudar as estratégias provenientes da natureza. Dentro das arquiteturas biológicas, as sociedades que vivem em enxames revelam que agentes simplistas, tais como formigas ou pássaros, são capazes de realizar tarefas complexas usufruindo de mecanismos de cooperação. Estes sistemas abrangem todas as condições necessárias para a sobrevivência, incorporando comportamentos de cooperação, competição e adaptação. Na “batalha” sem fim em prol do progresso dos mecanismos artificiais desenvolvidos pelo homem, a ciência conseguiu simular o primeiro comportamento em enxame no final dos anos oitenta. Desde então, muitas outras áreas, entre as quais a robótica, beneficiaram de mecanismos de tolerância a falhas inerentes da inteligência coletiva de enxames. A área de investigação deste estudo incide na robótica de enxame, consistindo num domínio particular dos sistemas robóticos cooperativos que incorpora os mecanismos de inteligência coletiva de enxames na robótica. Mais especificamente, propõe-se uma solução completa de robótica de enxames a ser aplicada em contexto real. Nesta ótica, as operações de busca e salvamento foram consideradas como o caso de estudo principal devido ao nível de complexidade associado às mesmas. Tais operações ocorrem tipicamente em cenários dinâmicos de elevadas dimensões, com condições adversas que colocam em causa a aplicabilidade dos sistemas robóticos cooperativos. Este estudo centra-se nestes problemas, procurando novos desafios que não podem ser ultrapassados através da simples adaptação da literatura da especialidade em algoritmos de enxame, planeamento, controlo e técnicas de tomada de decisão. As contribuições deste trabalho sustentam-se em torno da extensão do método Particle Swarm Optimization (PSO) aplicado a sistemas robóticos cooperativos, denominado de Robotic Darwinian Particle Swarm Optimization (RDPSO). O RDPSO consiste numa arquitetura robótica de enxame distribuída que beneficia do particionamento dinâmico da população de robôs utilizando mecanismos evolucionários de exclusão social baseados na sobrevivência do mais forte de Darwin. No entanto, apesar de estar assente no caso de estudo do RDPSO, a aplicabilidade dos conceitos aqui propostos não se encontra restrita ao mesmo, visto que todos os algoritmos parametrizáveis de enxame de robôs podem beneficiar de uma abordagem idêntica. Os fundamentos em torno do RDPSO são introduzidos, focando-se na dinâmica dos robôs, nos constrangimentos introduzidos pelos obstáculos e pela comunicação, e nas suas propriedades evolucionárias. Considerando a colocação inicial dos robôs no ambiente como algo fundamental para aplicar sistemas de enxames em aplicações reais, é assim introduzida uma estratégia de colocação de robôs realista. Para tal, a população de robôs é dividida de forma hierárquica, em que são utilizadas plataformas mais robustas para colocar as plataformas de enxame no cenário de forma autónoma. Após a colocação dos robôs no cenário, é apresentada uma estratégia para permitir a criação e manutenção de uma rede de comunicação móvel ad hoc com tolerância a falhas. Esta estratégia não considera somente a distância entre robôs, mas também a qualidade do nível de sinal rádio frequência, redefinindo assim a sua aplicabilidade em cenários reais. Os aspetos anteriormente mencionados estão sujeitos a uma análise detalhada do sistema de comunicação inerente ao algoritmo, para atingir uma implementação mais escalável do RDPSO a cenários de elevada complexidade. Esta elevada complexidade inerente à dinâmica dos cenários motivaram a ultimar o desenvolvimento do RDPSO, integrando para o efeito um mecanismo adaptativo baseado em informação contextual (e.g., nível de atividade do grupo). Face a estas considerações, o presente estudo pode contribuir para expandir o estado-da-arte em robótica de enxame com algoritmos inovadores aplicados em contexto real. Neste sentido, todos os métodos propostos foram extensivamente validados e comparados com alternativas, tanto em simulação como com robôs reais. Para além disso, e dadas as limitações destes (e.g., número limitado de robôs, cenários de dimensões limitadas, constrangimentos reais limitados), este trabalho contribui ainda para um maior aprofundamento do estado-da-arte, onde se propõe um modelo macroscópico capaz de capturar a dinâmica inerente ao RDPSO e, até certo ponto, estimar analiticamente o desempenho coletivo dos robôs perante determinada tarefa. Em suma, esta investigação pode ter aplicabilidade prática ao colmatar a lacuna que se faz sentir no âmbito das estratégias de enxames de robôs em contexto real e, em particular, em cenários de busca e salvamento.Over the past decades, many scientists and engineers have been studying nature’s best and time-tested patterns and strategies. Within the existing biological architectures, swarm societies revealed that relatively unsophisticated agents with limited capabilities, such as ants or birds, were able to cooperatively accomplish complex tasks necessary for their survival. Those simplistic systems embrace all the conditions necessary to survive, thus embodying cooperative, competitive and adaptive behaviours. In the never-ending battle to advance artificial manmade mechanisms, computer scientists simulated the first swarm behaviour designed to mimic the flocking behaviour of birds in the late eighties. Ever since, many other fields, such as robotics, have benefited from the fault-tolerant mechanism inherent to swarm intelligence. The area of research presented in this Ph.D. Thesis focuses on swarm robotics, which is a particular domain of multi-robot systems (MRS) that embodies the mechanisms of swarm intelligence into robotics. More specifically, this Thesis proposes a complete swarm robotic solution that can be applied to real-world missions. Although the proposed methods do not depend on any particular application, search and rescue (SaR) operations were considered as the main case study due to their inherent level of complexity. Such operations often occur in highly dynamic and large scenarios, with harsh and faulty conditions, that pose several problems to MRS applicability. This Thesis focuses on these problems raising new challenges that cannot be handled appropriately by simple adaptation of state-of-the-art swarm algorithms, planning, control and decision-making techniques. The contributions of this Thesis revolve around an extension of the Particle Swarm Optimization (PSO) to MRS, denoted as Robotic Darwinian Particle Swarm Optimization (RDPSO). The RDPSO is a distributed swarm robotic architecture that benefits from the dynamical partitioning of the whole swarm of robots by means of an evolutionary social exclusion mechanism based on Darwin’s survival-of-the-fittest. Nevertheless, although currently applied solely to the RDPSO case study, the applicability of all concepts herein proposed is not restricted to it, since all parameterized swarm robotic algorithms may benefit from a similar approach The RDPSO is then proposed and used to devise the applicability of novel approaches. The fundamentals around the RDPSO are introduced by focusing on robots’ dynamics, obstacle avoidance, communication constraints and its evolutionary properties. Afterwards, taking the initial deployment of robots within the environment as a basis for applying swarm robotics systems into real-world applications, the development of a realistic deployment strategy is proposed. For that end, the population of robots is hierarchically divided, wherein larger support platforms autonomously deploy smaller exploring platforms in the scenario, while considering communication constraints and obstacles. After the deployment, a way of ensuring a fault-tolerant multi-hop mobile ad hoc communication network (MANET) is introduced to explicitly exchange information needed in a collaborative realworld task execution. Such strategy not only considers the maximum communication range between robots, but also the minimum signal quality, thus refining the applicability to real-world context. This is naturally followed by a deep analysis of the RDPSO communication system, describing the dynamics of the communication data packet structure shared between teammates. Such procedure is a first step to achieving a more scalable implementation by optimizing the communication procedure between robots. The highly dynamic characteristics of real-world applications motivated us to ultimate the RDPSO development with an adaptive strategy based on a set of context-based evaluation metrics. This thesis contributes to the state-of-the-art in swarm robotics with novel algorithms for realworld applications. All of the proposed approaches have been extensively validated in benchmarking tasks, in simulation, and with real robots. On top of that, and due to the limitations inherent to those (e.g., number of robots, scenario dimensions, real-world constraints), this Thesis further contributes to the state-of-the-art by proposing a macroscopic model able to capture the RDPSO dynamics and, to some extent, analytically estimate the collective performance of robots under a certain task. It is the author’s expectation that this Ph.D. Thesis may shed some light into bridging the reality gap inherent to the applicability of swarm strategies to real-world scenarios, and in particular to SaR operations.FCT - SFRH/BD /73382/201

INVESTIGATING TECHNIQUES AND RESEARCH TRENDS IN RF ENERGY HARVESTING

ABSTRACT For more than two decades, there were extensive research activities towards harnessing the energy captured from the natural resources and use it for human utility. This field of research, commonly known as energy harvesting, has various characteristics and subsidiary applications. Presently, it is seen that use of mobile phones are increasing due to the advancement of communication systems and numerous services incorporated within it make the availability of free RF signals. This paper discusses various techniques and attempts initiated by research community in harnessing this free RF signals for the purpose of energy harvesting to power up low-powered electronic devices and embedded systems. It also presents some of the most significant research procedures that use RF signals for harvesting energy and their implausible research gap

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID