Real-time auditing of domotic robotic cleaners

Domotic Robotic Cleaners are autonomous devices that are designed to operate almost entirely unattended. In this paper we propose a system that aims to evaluate the performance of such devices by analysis of their trails. This concept of trails is central to our approach, and it encompasses the traditional notion of a path followed by a robot between arbitrary numbers of points in a physical space. We enrich trails with context-specific metadata, such as proximity to landmarks, frequency of visitation, duration, etc. We then process the trail data collected by the robots, we store it an appropriate data structure and derive useful statistical information from the raw data. The usefulness of the derived information is twofold: it can primarily be used to audit the performance of the robotic cleaner –for example, to give an accurate indication of how well a space is covered (cleaned). And secondarily information can be analyzed in real-time to affect the behavior of specific robots – for example to notify a robot that specific areas have not been adequately covered. Towards our first goal, we have developed and evaluated a prototype of our system that uses a particular commercially available robotic cleaner. Our implementation deploys adhoc wireless local networking capability available through a surrogate device mounted onto this commodity robot; the device senses relative proximity to a grid of RFID tags attached to the floor. We report on the performance of this system in experiments conducted in a laboratory environment, which highlight the advantages and limitations of our approach

Analysis, ranking and prediction in pervasive computing trails

Many pervasive computing applications involve the recording of user interaction with physical and digital resources in the environment. Such records can be used to establish context histories that can be subsequently used for user behaviour analysis, pattern recognition, prediction, and the provision of context aware services. In this paper we use trails as the principal data processing primitive for analysis and prediction. We define a trail as the sequence of recorded interactions with the pervasive computing space. Trails contain patterns of space usage and they can be used for the provision of different services, space usage analysis or sociological information of people using the environment simultaneously. Trail analysis requires considerable storage and computational resources to discover such patterns. Moreover no single method exists that identifies significant trails based on different metrics for a variety of different pervasive computing application. In this paper, we introduce a trail based analysis approach, an associated model for the representation of trails and trail aggregates, and suitable data structures for efficient storage, filtering and retrieval. Also, we propose several related algorithms and associated metrics for ranking and identifying significant trails. We use these techniques in 2 different case studies to extract valuable information about the pervasive system environment usage and evaluate the summarizability and the predictive power of our model

Shared Memories: A Trail-based Coordination Server for Robot Teams

Abstract—Robust, dependable and concise coordination between members of a robot team is a critical ingredient of any such collective activity. Depending on the availability and the characteristics of the particular communication infrastructure, coordination mechanisms can take varied forms, leading to distinct system behaviors. In this paper, we consider the case of robot teams operating within relatively sparse wireless sensor network deployments. We introduce Shared Memories, a trailbased coordination engine, that analyzes interaction patterns between participating team members and sensor network nodes capable to discover significant aggregate patterns, which are made available to the team. To this end, we propose a model for the representation of captured interactions and their sensory context developed as a probabilistic grammar, as well as associated metrics used to rank trails and quantify their significance. Such trails are used as the basis for coordinated operation in team tasks and are made available by the engine to all team members. Our implementation deploys ad-hoc wireless local networking capability available through surrogate devices to commodity robots and RFID proximity sensors. We report on the performance of this system in experiments conducted in a laboratory environment, which highlight the advantages and limitations of our approach. I

Shared memories: a trail-based coordination server for robot teams

Robust, dependable and concise coordination between members of a robot team is a critical ingredient of any such collective activity. Depending on the availability and the characteristics of the particular communication infrastructure, coordination mechanisms can take varied forms, leading to distinct system behaviors. In this paper, we consider the case of robot teams operating within relatively sparse wireless sensor network deployments. We introduce Shared Memories, a trail-based coordination engine, that analyzes interaction patterns between participating team members and sensor network nodes capable to discover significant aggregate patterns, which are made available to the team. To this end, we propose a model for the representation of captured interactions and their sensory context developed as a probabilistic grammar, as well as associated metrics used to rank trails and quantify their significance. Such trails are used as the basis for coordinated operation in team tasks and are made available by the engine to all team members. Our implementation deploys ad-hoc wireless local networking capability available through surrogate devices to commodity robots and RFID proximity sensors. We report on the performance of this system in experiments conducted in a laboratory environment, which highlight the advantages and limitations of our approach

Urban social tapestries

Urban Tapestries is an exploration into the potential costs and benefits of public authoring, that is, mapping and sharing of local knowledge using pervasive user-generated media. The aim of this investigation is to reveal the potential of pervasive computing to create and support relationships that extend beyond established social and cultural boundaries and enable the development of new practices based around place, identity and community. In this paper, we report on the work carried out within UT since its inception in 2002 discussing all relevant aspects from its background, approach and its technical development. We also identify the main findings of this work related to the use of pervasive computing to support pervasive user-generated content and identify some of the main questions that require further investigation

Urban computing and mobile devices: MyCornr

This Works in Progress department features 12 urban computing projects that span a range of computing and social areas. The first entry examines how an urban environment could operate as a large-scale, real-time control system. One project focuses on annotating public spaces and sharing the tags with others. Two projects tie together social networking in cyberspace with local urban communities. Two projects examine computing and social interactions in physical spaces. Two entries explore how to combine synthetic and physical views of urban environments. Four entries investigate how we explore urban spaces, interact with technology in those spaces, and create shared community histories. This department is part of a special issue on urban computing