2,722 research outputs found

    Robot Navigation in Unseen Spaces using an Abstract Map

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    Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.Comment: 15 pages, published in IEEE Transactions on Cognitive and Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see https://btalb.github.io/abstract_map/ for access to softwar

    Spatial representation for planning and executing robot behaviors in complex environments

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    Robots are already improving our well-being and productivity in different applications such as industry, health-care and indoor service applications. However, we are still far from developing (and releasing) a fully functional robotic agent that can autonomously survive in tasks that require human-level cognitive capabilities. Robotic systems on the market, in fact, are designed to address specific applications, and can only run pre-defined behaviors to robustly repeat few tasks (e.g., assembling objects parts, vacuum cleaning). They internal representation of the world is usually constrained to the task they are performing, and does not allows for generalization to other scenarios. Unfortunately, such a paradigm only apply to a very limited set of domains, where the environment can be assumed to be static, and its dynamics can be handled before deployment. Additionally, robots configured in this way will eventually fail if their "handcrafted'' representation of the environment does not match the external world. Hence, to enable more sophisticated cognitive skills, we investigate how to design robots to properly represent the environment and behave accordingly. To this end, we formalize a representation of the environment that enhances the robot spatial knowledge to explicitly include a representation of its own actions. Spatial knowledge constitutes the core of the robot understanding of the environment, however it is not sufficient to represent what the robot is capable to do in it. To overcome such a limitation, we formalize SK4R, a spatial knowledge representation for robots which enhances spatial knowledge with a novel and "functional" point of view that explicitly models robot actions. To this end, we exploit the concept of affordances, introduced to express opportunities (actions) that objects offer to an agent. To encode affordances within SK4R, we define the "affordance semantics" of actions that is used to annotate an environment, and to represent to which extent robot actions support goal-oriented behaviors. We demonstrate the benefits of a functional representation of the environment in multiple robotic scenarios that traverse and contribute different research topics relating to: robot knowledge representations, social robotics, multi-robot systems and robot learning and planning. We show how a domain-specific representation, that explicitly encodes affordance semantics, provides the robot with a more concrete understanding of the environment and of the effects that its actions have on it. The goal of our work is to design an agent that will no longer execute an action, because of mere pre-defined routine, rather, it will execute an actions because it "knows'' that the resulting state leads one step closer to success in its task

    Robot Compatible Environment and Conditions

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    Service robot technology is progressing at a fast pace. Accurate robot-friendly indoor localization and harmonization of built environ-ment in alignment with digital, physical, and social environment becomes emphasized. This paper proposes the novel approach of Robot Compatible Environment (RCE) within the architectural space. Evolution of service robotics in connection with civil engineering and architecture is discussed, whereas optimum performance is to be achieved based on robots’ capabilities and spatial affordances. For ubiquitous and safe human-robot interaction, robots are to be integrated into the living environment. The aim of the research is to highlight solutions for various interconnected challenges within the built environment. Our goal is to reach findings on comparison of robotic and accessibility standards, synthesis of navigation, access to information and social acceptance. Checklists, recommendations, and design process are introduced within the RCE framework, proposing a holistic approach

    Unifying terrain awareness for the visually impaired through real-time semantic segmentation.

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    Navigational assistance aims to help visually-impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision-based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel-wise semantic segmentation to cover navigation-related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short-range obstacles, fast-approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state-of-the-art methods while maintaining real-time speed. We also present a closed-loop field test involving real visually-impaired users, demonstrating the effectivity and versatility of the assistive framework

    Augmented Reality in Smart Cities: A Multimedia Approach

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    Intro: This paper presents an advance overview of utilizing Augmented Reality (AR) in smart cities. Although, Smart cities contain six major aspects (mobility, economy, government, environment, living, and people), this paper focuses on three of them that have more potentiality in using virtual assistant (mobility, environment, and living). Methodology: Presenting a state-of-the-art review studies undertake between 2013 and 2017, which is driven from highlighted libraries is the aim of this research. After exact examine, 15 emphasized studies are chosen to divide the main aspects while 120 selective articles are supporting them. These categorizes have been critically compared with an aim, method and chronological perspectives. Results: First of All, Environmental issues (Museums industry) attract the most attention of researchers while the living issues (maintenance) have lower significant compare t latter and mobility (indoor-outdoor navigation) attract the least. Moreover, a close connection between academic and industry fields is going to be created. Conclusions: it has been concluded that, because of economic advantages, utilizing AR technology has improved in the tourism and maintenance. Moreover, until now, most of studies try to prove their concept rather than illustrate well stablished analytic approach. Because of hardware and software improvement, it is essential for the future studies to evaluate their hypothesis in a real urban context

    A context-sensitive conceptual framework for activity modeling

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    Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place along the way, such as reading on the bus, are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., holidaying in a country, could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts

    Prototypes of productivity tools for the jadescript programming language

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    Jadescript is an agent-oriented programming language built on top of JADE. So far, the focus of the development of the language was on design choices, on syntax refinements, and on the introduction of expressions and constructs for agent-related abstractions and tasks. In this paper, a proposal to achieve the crucial goal of making Jadescript suitable for professional use is presented. The success of Jadescript, as a solid language to build real-world agent-based software systems, is necessarily related to its effective integration with mainstream development tools. In this paper, some of the productivity tools developed to integrate Jadescript with a mainstream development environment are presented as a way to promote the successful adoption of the language towards the community of JADE users
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