Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

Design revolutions: IASDR 2019 Conference Proceedings. Volume 1: Change, Voices, Open

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 1 includes papers from Change, Voices and Open tracks of the conference

Automotive Interior Sensing - Anomaly Detection

Com o surgimento dos veículos autónomos partilhados não haverá condutores nos veículos capazes de manter o bem-estar dos passageiros. Por esta razão, é imperativo que exista um sistema preparado para detetar comportamentos anómalos, por exemplo, violência entre passageiros, e que responda de forma adequada. O tipo de anomalias pode ser tão diverso que ter um "dataset" para treino que contenha todas as anomalias possíveis neste contexto é impraticável, implicando que algoritmos tradicionais de classificação não sejam ideais para esta aplicação. Por estas razões, os algoritmos de deteção de anomalias são a melhor opção para construir um bom modelo discriminativo. Esta dissertação foca-se na utilização de técnicas de "deep learning", mais precisamente arquiteturas baseadas em "Spatiotemporal auto-encoders" que são treinadas apenas com sequências de "frames" de comportamentos normais e testadas com sequências normais e anómalas dos "datasets" internos da Bosch. O modelo foi treinado inicialmente com apenas uma categoria das ações não violentas e as iterações finais foram treinadas com todas as categorias de ações não violentas. A rede neuronal contém camadas convolucionais dedicadas à compressão e descompressão dos dados espaciais; e algumas camadas dedicadas à compressão e descompressão temporal dos dados, implementadas com células LSTM ("Long Short-Term Memory") convolucionais, que extraem informações relativas aos movimentos dos passageiros. A rede define como reconstruir corretamente as sequências de "frames" normais e durante os testes, cada sequência é classificada como normal ou anómala de acordo com o seu erro de reconstrução. Através dos erros de reconstrução são calculados os "regularity scores" que indicam a regularidade que o modelo previu para cada "frame". A "framework" resultante é uma adição viável aos algoritmos tradicionais de reconhecimento de ações visto que pode funcionar como um sistema que serve para detetar ações desconhecidas e contribuir para entender o significado de tais interações humanas.With the appearance of SAVs (Shared Autonomous Vehicles) there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviours, e.g., violence between passengers, and trigger the appropriate response. Furthermore, the type of anomalous activities can be so diverse, that having a dataset that incorporates most use cases is unattainable, making traditional classification algorithms not ideal for this kind of application. In this sense, anomaly detection algorithms are a good approach in order to build a discriminative model. Taking this into account, this work focuses on the use of deep learning techniques, more precisely Spatiotemporal auto-encoder based frameworks, which are trained on human behavior video sequences and tested on use cases with normal and abnormal human interactions from Bosch's internal datasets. Initially, the model was trained on a single non-violent action category. Final iterations considered all of the identified non-violent actions as normal data. The network architecture presents a group of convolutional layers which encode and decode spatial data; and a temporal encoder/decoder structure, implemented as a convolutional Long Short Term Memory network, responsible for learning motion information. The network defines how to properly reconstruct the 'normal' frame sequences and during testing, each sequence is classified as normal or abnormal based on its reconstruction error. Based on these values, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/abnormal behaviours and aid in understanding the meaning of such human interactions

Evaluation of cupboard door sensors for improving activity recognition in the kitchen

International audienc

Crowd Scene Analysis in Video Surveillance

There is an increasing interest in crowd scene analysis in video surveillance due to the ubiquitously deployed video surveillance systems in public places with high density of objects amid the increasing concern on public security and safety. A comprehensive crowd scene analysis approach is required to not only be able to recognize crowd events and detect abnormal events, but also update the innate learning model in an online, real-time fashion. To this end, a set of approaches for Crowd Event Recognition (CER) and Abnormal Event Detection (AED) are developed in this thesis. To address the problem of curse of dimensionality, we propose a video manifold learning method for crowd event analysis. A novel feature descriptor is proposed to encode regional optical flow features of video frames, where adaptive quantization and binarization of the feature code are employed to improve the discriminant ability of crowd motion patterns. Using the feature code as input, a linear dimensionality reduction algorithm that preserves both the intrinsic spatial and temporal properties is proposed, where the generated low-dimensional video manifolds are conducted for CER and AED. Moreover, we introduce a framework for AED by integrating a novel incremental and decremental One-Class Support Vector Machine (OCSVM) with a sliding buffer. It not only updates the model in an online fashion with low computational cost, but also adapts to concept drift by discarding obsolete patterns. Furthermore, the framework has been improved by introducing Multiple Incremental and Decremental Learning (MIDL), kernel fusion, and multiple target tracking, which leads to more accurate and faster AED. In addition, we develop a framework for another video content analysis task, i.e., shot boundary detection. Specifically, instead of directly assessing the pairwise difference between consecutive frames over time, we propose to evaluate a divergence measure between two OCSVM classifiers trained on two successive frame sets, which is more robust to noise and gradual transitions such as fade-in and fade-out. To speed up the processing procedure, the two OCSVM classifiers are updated online by the MIDL proposed for AED. Extensive experiments on five benchmark datasets validate the effectiveness and efficiency of our approaches in comparison with the state of the art

The Archaeology of an Ancient Seaside Town: Performance and Community at Samanco, Nepeña Valley, Peru Volume 1

Studies of social complexity increasingly recognize the role of maritime communities in the development of large sociopolitical systems. The Central Andes present an ideal region for understanding maritime aspects of ancient social complexity, due to one of the most productive sea biomasses in the world. In this study I investigate Samanco, an ancient seaside town, and its contribution to urban transformations along the North-Central coast of Peru during the mid-1st millennium BCE. I consult a theoretical framework of performance and its influence on community organization as a framework for analyzing sociopolitical development. Mapping and excavation at Samanco documented a densely occupied settlement. Materials recovered imply that ancient Samanco was a community of low status inhabitants focused on day-to-day subsistence and trade. The discovery of animal enclosures, diverse cultigens, primarily domestic ceramics, and most importantly a dense array of marine goods support the inference of an early urban town centered on food production. I argue that trade to inland residential centers of Samanco’s vast food products, likely through the aid of camelid caravans, played an important part in early urban political economy and the overall success of the community. Daily interactions, seen as culturally important performances, promoted subsistence industry as a site identity. Patios at the heart of neighborhood compounds served as venues for learning, socialization, and communal interactions which shaped and negotiated Samanco society. Neighborhood compounds were built and materialized in a way that emphasized exclusion and autonomy among various co-resident groups living in separate compounds. Limited social hierarchies were enacted through public performances inside monumental plazas. Results bring new insights into social complexity, arguing for non-state urban societies which challenge neo-evolutionary ideas of state formation. The results also advocate exploration of past ii experiences and communal interactions as a way of bringing humans back to the forefront of archaeological inquiry. This thesis also advocates approaching sites as biographies by detailing various site performances at Samanco up to the present. One example includes site re-use for tombs during the 16th century CE ascribed to a performance of ancestor veneration associated with site ruins. The thesis also analyzes contemporary engagements with Samanco’s archaeological heritage to understand how local Andean communities experience and perform with archaeological ruins. I argue that local communities conceive of sites as dangerous but also fortuitous places inseparable from the rural and mystical Andean landscape, commanding a performance of respect. Moreover, interactions with archaeological sites and the stories told about them are integral in the construction of rural mestizo identities. These results emphasize the importance of collaboration and the promotion of local knowledge in archaeological research

Investigation and Quantification of FES Exercise – Isometric Electromechanics and Perceptions of Its Usage as an Exercise Modality for Various Populations

Functional Electrical Stimulation (FES) is the triggering of muscle contraction by use of an electrical current. It can be used to give paralyzed individuals several health benefits, through allowing artificial movement and exercise. Although many FES devices exist, many aspects require innovation to increase usability and home translation. In addition, the effect of changing electrical parameters on limb biomechanics is not entirely understood; in particular with regards to stimulation duty cycle. This thesis has two distinct components. In the first (public health component), interview studies were conducted to understand several issues related to FES technology enhancement, implementation and home translation. In the second (computational biomechanics component), novel signal processing algorithms were designed that can be used to measure mechanical responses of muscles subjected to electrical stimulation. These experiments were performed by changing duty cycle and measuring its effect on quadriceps-generated knee torque. The studies of this thesis have presented several ideas, toolkits and results which have the potential to guide future FES biomechanics studies and the translatability of systems into regular usage for patients. The public health studies have provided conceptual frameworks upon which FES may be used in the home by patients. In addition, they have elucidated a range of issues that need to be addressed should FES technology reach its true potential as a therapy. The computational biomechanics studies have put forward novel data analysis techniques which may be used for understanding how muscle responds to electrical stimulation, as measured via torque. Furthermore, the effect of changing the electrical stimulation duty cycle on torque was successfully described, adding to an understanding of how electrical stimulation parameter modulation can influence joint biomechanics