Coordinated control of mixed robot and sensor networks in distributed area exploration

Recent advancements in wireless communication and electronics has enabled the development of multifunctional sensor nodes that are small in size and communicate untethered in short distances. In the last decade, significant advantages have been made in the field of robotics, and robots have become more feasible in systems design. Therefore, we trust that a number of open problems with wireless sensor networks can be solved or diminished by including mobility capabilities in agents

Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The content of this paper does not reflect the official opinion of the EU. Responsibility for the information and views expressed therein lies entirely with the authors. Proc. of ACM/IEEE IPSN'20, 202

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Exploiting and optimizing mobility in wireless sensor networks

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2016.Nos últimos anos, as chamadas Redes de Sensores Sem Fio (RSSF) tem sido usadas numa grande variedade de aplicações, tais como monitoramento (p.ex. poluição do ar e água, vulcões, estruturas, sinais vitais), detecção de eventos (p.ex. vigilância, incêndios, inundações, terremotos), e monitoramento de alvos (p.ex. segurança, animais silvestres, etc). RSSF são constituídas tipicamente por dezenas, as vez centenas de pequenos dispositivos alimentados por baterias, capazes de realizar medições e de transmitir tais dados para uma estação base através de um canal sem fio. Uma das formas mais promissoras para melhorar o desempenho das RSSF em termos de conectividade, tempo de vida da rede, e latência na transmissão dos dados é através de técnicas que exploram a mobilidade em um ou mais componentes da rede. A mobilidade na RSSF pode ser tanto controlável como aleatória, sendo que em ambos os casos os protocolos devem ser devidamente ajustados para responder adequadamente aos cenários em questão. No caso de mobilidade aleatória, os nodos sensores podem ser capazes de aprender os padrões de mobilidade dos nodos para poderem otimizar a operação da rede. Por outro lado, sendo os padrões de mobilidade conhecidos, é possível fazer escolhas para melhor sintonizar o desempenho da rede de acordo com os critérios estabelecidos pelo projetista. A presente tese de doutorado procura explorar as vantagens associadas com o uso de mobilidade controlada em RSSF. É possível definir mobilidade controlada como sendo a capacidade de se alterar propositalmente o posicionamento de determinados nodos da RSSF. Com isso se torna possível explorar, controlar, ou mesmo otimizar a trajetória e a velocidade dos nodos móveis da RSSF a fim de maximizar o desempenho da rede como um todo. Definitivamente, o uso de nodos que permitam o ajuste de trajetória e velocidade oferece um alto grau de flexibilidade para se explorar aspectos de mobilidade e projetar protocolos de coleta de dados otimizados. Ao se utilizar mobilidade controlada, algumas das operações realizadas pela RSSF podem ser significativamente melhoradas, de modo a tornar possível ajustar o padrão de desempenho da rede de acordo com os níveis desejados. Por exemplo, o processo de descoberta de nodos pode ser melhorado e mesmo simplificado com o controle dos nodos móveis, de modo que ele possa se aproximar dos nodos estáticos em instantes pré-determinados. Da mesma forma, o processo de coleta de dados pode ser otimizado se os nodos móveis se moverem mais rapidamente nos locais onde eles precisam coletar menos dados. Entretanto, diversos desafios aparecem neste tipo de contexto. Por exemplo, como se deve escalonar a chegada do(s) nodo(s) móvel(is) e como se deve controlar e otimizar a movimentação em termos de velocidade sem afetar a qualidade de serviço. Nesse contexto, o segundo capítulo da teseapresenta um esquema de estimação de localização de nodos estáticos espalhados ao longo de uma área predeterminada, utilizando-se para tanto de um nodo móvel com mobilidade controlada. Tal informação de posicionamento é muito importante para a organização de uma RSSF. Com isso é possível definir a sua cobertura, os protocolos de roteamento, a forma de coleta de dados e também auxiliar em aplicações de rastreamento e detecção de eventos. O esquema proposto consiste de uma técnica de localização para estimar a posição dos nodos sensor de forma eficiente, usando apenas um nodo móvel e técnicas geométricas simples. O esquema não requer hardware adicional ou mesmo comunicação entre nodos sensores, evitando assim maiores gastos de baterias. A estimativa de posição obtida é precisa e capaz de tolerar um certo grau de obstáculos. Os resultados obtidos ao longo da tese demostram que a precisão de localização pode ser bem ajustada selecionando corretamente a velocidade, o intervalo de transmissão de beacons e o padrão de varredura da área de interesse pelo nodo móvel.Já o terceiro capítulo apresentada uma técnica de otimização para fins de controle da mobilidade do nodo coletor de dados (MDC). Com isso torna-se possível desenvolver um esquema inteligente de coleta de dados na RSSF. Em primeiro lugar, são destacados os fatores que afetam o processo de coleta de dados usando um MDC. Em seguida é apresentado um algoritmo adaptativo que permite ajustar os parâmetros de controlenecessários para modificar os parâmetros de movimentação do MDC. Estes parâmetros permitem que a velocidade do MDC seja ajustada em tempo de execução para otimizaro processo de coleta de dados. Com isso o MDC pode se adaptar às diferentes taxas de coletas de dados impostas por um conjunto de nodos heterogêneos. O esquema proposto apresenta vantagens significativas para RSSF de grande escala e também heterogêneas (onde os sensores possuem taxas de amostragem variáveis). Os resultados obtidos mostram um aumento significativo na taxa de coleta de dados e a redução no tempo total de deslocamento e no número de voltas que o MDC gasta para coletar os dados dos sensores.Por fim, o capítulo 4 propõe um mecanismo de controle de acesso (MAC) adaptado ao cenário de mobilidade, que se ajusta automaticamente de acordo com o padrão de mobilidade do MDC. O mesmo foca umaredução no consumo de energia e na melhoria da coleta de dados, suportando mobilidade e evitando colisões de mensagens. Este protocolo destina-se a aplicações de coleta de dados nas quais os nós sensores têm de reportar periodicamente a um nó receptor ou estação base. O conceito básico é baseado em acesso múltiplo de divisão de tempo, onde a duração do padrão de sono-vigília é definida de acordo com o padrão de mobilidade do MDC. O esquema proposto é capaz de atender tanto mobilidade aleatória quanto controlada por parte do MDC, desde que as RSSF sejam organizadas em cluster. Uma análise de simulação detalhada é realizada para avaliar seu desempenho em cenários mais gerais e sob diferentes condições operacionais. Os resultados obtidos mostram que o nosso esquema proposto supera amplamente oprotocolo 802.15.4 com sinais (beacons) em termos de eficiência energética, tempo de deslocamento do MDC e taxas de coleta de dados.Abstract : One of the promising techniques for improving the performance of a wireless sensor network (WSN), in terms of connectivity, network lifetime, and data latency, is to introduce and exploit mobility in some of the network components. Mobility in WSN can be either uncontrollable or controllable and needs to be optimized in both cases. In the case of uncontrolled mobility, sensor nodes can learn the mobility patterns of mobile nodes to improve network performance. On the other hand, if the mobility is controllable in terms of trajectory and speed, it can be best tuned to enhance the performance of the network to the desired level. This thesis considers the problem of exploiting and optimizing mobility in wireless sensor networks in order to increase the performance and efficiency of the network.First, a location estimation scheme is discussed for static nodes within a given sensor area using a controlled mobile node. Position information of static nodes is very important in WSN. It helps in effective coverage, routing, data collection, target tracking, and event detection. The scheme discusses a localization technique for efficient position estimation of the sensor nodes using a mobile node and simple geometric techniques. The scheme does not require extra hardware or data communication and does not make the ordinary sensor nodes to spend energy on any interaction with neighboring nodes. The position estimation is accurate and efficient enough to tolerate obstacles and only requires broadcasting of beacon messages by the mobile node. Obtained simulation results show that the localization accuracy can be well adjusted by properly selecting the speed, beacon interval, and scan pattern of the mobile node.Second, an optimization technique for controlled mobility of a mobile data collector is presented in order to develop a smart data collection scheme in WSN. In this case, first, the factors affecting the data collection process using an MDC is highlighted. Then, an adaptive algorithm and control parameters that the MDC uses for autonomously controlling its motion is presented. These parameters allow the speed of the MDC to be adjusted at run time in order to adaptively improve the data collection process. Built-in intelligence helps our system adapting to the changing requirements of data collection. Our scheme shows significant advantages for sparsely deployed, large scale sensor networks and heterogeneous networks (where sensors have variable sampling rates). The simulation results show a significant increase in data collection rate and reduction in the overall traverse time and number of laps that the MDC spends for data gathering.Finally, a mobility aware adaptive medium access control (MAC) is proposed for WSNs which automatically adjusts according to the mobility pattern of the MDC, focusing on reducing energy consumption and improving data collection, while supporting mobility and collision avoidance. This protocol is targeted to data collection applications (e.g. monitoring and surveillance), in which sensor nodes have to periodically report to a sink node. The core concept is based on adaptive time division multiple access, where the sleep-wake duration is defined according to the MDC mobility pattern. The proposed scheme is described for random, predictable, and controlled arrival of MDC in cluster-based WSNs. A detailed simulation analysis is carried out to evaluate its performance in more general scenarios and under different operating conditions. The obtained results show that our scheme largely outperforms the commonly used 802.15.4 beacon-enabled and other fixed duty-cycling schemes in terms of energy efficiency, MDC traverse time, and data collection rates

Improvement Schemes for Indoor Mobile Location Estimation: A Survey

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Building Evacuation with Mobile Devices

In der Dissertation wird ein Konzept für ein Gebäudeevakuierungssystem vorgestellt, das es ermöglicht, Personen mit Hilfe mobiler Endgeräte im Evakuierungsfall aus einem Gebäude zu führen. Die Dissertation gliedert sich in drei thematische Bereiche, in denen zunächst ein Konzept für die Systemarchitektur vorgestellt wird und anschließend verschiedene Algorithmen zur Routenplanung sowie zur Lokalisierung der Geräte vorgestellt und evaluiert werden