125 research outputs found

    Robot at factory 4.0: an auto-referee proposal based on artificial vision

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    The robotization and automation of tasks are relevant processes and of great relevance to be considered nowadays. This work aims to turn the manual action of assigning the score for the robotic competition Robot at Factory 4.0 by an automatic referee. Specifically, the aim is to represent the real space in a set of computational information using computer vision, localization and mapping techniques. One of the crucial processes to achieve this goal involved the adaptive calibration of the parameters of a digital camera through visual references and tracking of objects, which resulted in a fully functional, robust and dynamic system that is capable of mapping the competition’s objects accurately and correctly performing the referee’s tasks.This work is financed by National Funds through the Portuguese funding agency, FCT - Funda¾c˜ao para a Ciˆencia e a Tecnologia, within projects LA/P/0063/2020 and POCI-01-0247-FEDER-072638, co-funded by FEDER through COMPETE 2020. Authors acknowledge 5DPO RobotAtFactory team for making their time and robot available to conduct tests. The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI20/11780028info:eu-repo/semantics/publishedVersio

    Sampling-Based Exploration Strategies for Mobile Robot Autonomy

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    A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later

    Expectations and expertise in artificial intelligence: specialist views and historical perspectives on conceptualisation, promise, and funding

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    Artificial intelligence’s (AI) distinctiveness as a technoscientific field that imitates the ability to think went through a resurgence of interest post-2010, attracting a flood of scientific and popular expectations as to its utopian or dystopian transformative consequences. This thesis offers observations about the formation and dynamics of expectations based on documentary material from the previous periods of perceived AI hype (1960-1975 and 1980-1990, including in-between periods of perceived dormancy), and 25 interviews with UK-based AI specialists, directly involved with its development, who commented on the issues during the crucial period of uncertainty (2017-2019) and intense negotiation through which AI gained momentum prior to its regulation and relatively stabilised new rounds of long-term investment (2020-2021). This examination applies and contributes to longitudinal studies in the sociology of expectations (SoE) and studies of experience and expertise (SEE) frameworks, proposing a historical sociology of expertise and expectations framework. The research questions, focusing on the interplay between hype mobilisation and governance, are: (1) What is the relationship between AI practical development and the broader expectational environment, in terms of funding and conceptualisation of AI? (2) To what extent does informal and non-developer assessment of expectations influence formal articulations of foresight? (3) What can historical examinations of AI’s conceptual and promissory settings tell about the current rebranding of AI? The following contributions are made: (1) I extend SEE by paying greater attention to the interplay between technoscientific experts and wider collective arenas of discourse amongst non-specialists and showing how AI’s contemporary research cultures are overwhelmingly influenced by the hype environment but also contribute to it. This further highlights the interaction between competing rationales focusing on exploratory, curiosity-driven scientific research against exploitation-oriented strategies at formal and informal levels. (2) I suggest benefits of examining promissory environments in AI and related technoscientific fields longitudinally, treating contemporary expectations as historical products of sociotechnical trajectories through an authoritative historical reading of AI’s shifting conceptualisation and attached expectations as a response to availability of funding and broader national imaginaries. This comes with the benefit of better perceiving technological hype as migrating from social group to social group instead of fading through reductionist cycles of disillusionment; either by rebranding of technical operations, or by the investigation of a given field by non-technical practitioners. It also sensitises to critically examine broader social expectations as factors for shifts in perception about theoretical/basic science research transforming into applied technological fields. Finally, (3) I offer a model for understanding the significance of interplay between conceptualisations, promising, and motivations across groups within competing dynamics of collective and individual expectations and diverse sources of expertise

    Learning Motion Skills for a Humanoid Robot

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    This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters. Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed. Diese Dissertation beschĂ€ftigt sich mit dem Lernen von BewegungsfĂ€higkeiten fĂŒr humanoide Roboter. Als Grundlage wurde zunĂ€chst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene AnsĂ€tze fĂŒr die Erstellung von BewegungsfĂ€higkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt. Danach wurde ein Ansatz basierend auf bestĂ€rkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide AnsĂ€tze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die FĂ€higkeit des Gehens gelegt, aber auch Aufsteh- und SchussfĂ€higkeiten werden diskutiert

    A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights

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    This article provides a detailed analysis of the assessment of unmanned ground vehicle terrain traversability. The analysis is categorized into terrain classification, terrain mapping, and cost-based traversability, with subcategories of appearance-based, geometry-based, and mixed-based methods. The article also explores the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) and other based end-to-end methods as crucial components for advanced terrain traversability analysis. The investigation indicates that a mixed approach, incorporating both exteroceptive and proprioceptive sensors, is more effective, optimized, and reliable for traversability analysis. Additionally, the article discusses the vehicle platforms and sensor technologies used in traversability analysis, making it a valuable resource for researchers in the field. Overall, this paper contributes significantly to the current understanding of traversability analysis in unstructured environments and provides insights for future sensor-based research on advanced traversability analysis

    World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator

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    Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur during the execution, instead of classical manual pre-programmed solutions. In this paper, we describe the core ingredients of the proposed robot system, including visual recognition, object manipulation, and motion planning. Our robot system won the second prize, verifying the effectiveness and potential of data-driven robot systems for mobile manipulation in home environments

    Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

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    Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

    Mobile Robots Navigation

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    Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

    Mobile Manipulation Hackathon: Moving into Real World Applications

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    The Mobile Manipulation Hackathon was held in late 2018 during the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) to showcase the latest applications of wheeled robotic manipulators. The challenge had an open format, where teams developed an application using simulation tools and integrated it into a robotic platform. This article presents the competition and analyzes the results, with information gathered during the event and from a survey circulated among the finalist teams. We provide an overview of the mobile manipulation field, identify key areas required for further development to facilitate the implementation of mobile manipulators in real applications, and discuss ideas about how to structure future hackathon-style competitions to enhance their impact on the scientific and industrial communities.Peer ReviewedPostprint (published version
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