13,894 research outputs found
Human-computer cooperation platform for developing real-time robotic applications
[EN] This paper presents a human-computer cooperation platform, which permits the coordination between the user and the tool to improve the development of real-time control applications (e.g., mobile robots). These applications have functional (robot objectives) and temporal requirements to accomplish (deadlines guarantee of tasks). The simulation tool has been designed in order to permit the testing and validation of these two requirements. To this end, the tool is composed of two independent simulators interconnected through a shared memory: the robot simulator (functional level) and the real-time task scheduler simulator (task execution level). Robotic applications can be defined with the robot simulator while the real-time scheduler simulator permits to analyze the schedulability of the robotic tasks. The real-time task simulator incorporates a flexible task model where the task temporal parameters (e.g., computation time) adapt to the requirements of the application (e.g., number of objects in scenes); thus, the use of the CPU is not overestimated. A key issue of the framework is the human-computer interface, which allows the monitoring of different parameters of the application: robot objectives, task schedule, robot speed, computation time, CPU utilization, deadline misses. The usefulness of the simulation tool is shown through different robotic navigation experiments. Finally, the simulation tool has been used to evaluate the proposed flexible model of tasks compared to a traditional fixed temporal parameters task model. Results show that the robot fulfills the objectives earlier, about 32% on average, and consumes on average about 15% less CPU to accomplish the objectives.DomĂnguez Montagud, CP.; MartĂnez-Rubio, J.; Busquets Mataix, JV.; Hassan Mohamed, H. (2019). Human-computer cooperation platform for developing real-time robotic applications. The Journal of Supercomputing. 75(4):1849-1868. https://doi.org/10.1007/s11227-018-2343-4S18491868754Dominguez C, Hassan H, Crespo A (2007) Real-time embedded architecture for pervasive robots. In: The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), pp 531â536Audsley NC, Burns A, Davis RI, Tindell KW, Wellings AJ (1995) Fixed priority pre-emptive scheduling: an historical perspective. Real Time Syst 8(2â3):173â198Stankovic JA, Lee I, Mok A, Rajkumar R (2005) Opportunities and obligations for physical computing systems. Computer 38(11):23â31Zhen Z, Qixin C, Lo C, Lei Z (2009) A CORBA-based simulation and control framework for mobile robots. Robotica 27(3):459Ferretti G, Magnani G, Porrati P, Rizzi G, Rocco P, Rusconi A (2008) Real-time simulation of a space robotic arm. In: IROSQadi A, Goddard S, Huang J, Farritor S (2005) A performance and schedulability analysis of an autonomous mobile robot. In: 17th Euromicro Conference on Real-Time Systems (ECRTSâ05), pp 239â248Goud GR, Sharma N, Ramamritham K, Malewar S (2006) Efficient real-time support for automotive applications: a case study. In: 12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSAâ06), pp 335â341Pedreiras P, Luis A (2003) The flexible time-triggered (FTT) paradigm: an approach to QoS management in distributed real-time systems. In: Proceedings International Parallel and Distributed Processing Symposium, p 9Li H, Sweeney J, Ramamritham K, Grupen R, Shenoy P (2003) Real-time support for mobile robotics. In: The 9th IEEE Real-Time and Embedded Technology and Applications Symposium. Proceedings, pp 10â18Chetto H, Chetto M (1989) Some results of the earliest deadline scheduling algorithm. IEEE Trans Softw Eng 15(10):1261â1269Liu R, Zhang X (2017) Systems of natural-language-facilitated human-robot cooperation: a review. arXiv:1701.08269v2Tsarouchi P, Makris S, Chryssolouris G (2016) Humanârobot interaction review and challenges on task planning and programming. Int J Comput Integr Manuf 29(8):916â931Moniz A (2013) Organizational concepts and interaction between humans and robots in industrial environments. In: IEEE-RAS-IARP Joint Workshop on Technical Challenges for Dependable Robots in Human Environment, TokyoMayer MP, Odenthal B, Faber M, Winkelholz C, Schlick CM (2014) Cognitive engineering of automated assembly processes. Hum Factors Ergon Manuf Serv Ind 24(3):348â368Agostini A, Torras C, Wörgötter F (2011) Integrating task planning and interactive learning for robots to work in human environments. In: IJCAIKwon W, Suh I (2014) Planning of proactive behaviors for humanârobot cooperative tasks under uncertainty. Knowl Based Syst 72:81â95Chen F, Sekiyama K, Sasaki H, Huang J, Sun B, Fukuda T (2011) Assembly strategy modeling and selection for human and robot coordinated cell assembly. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 4670â4675Gombolay M, Wilcox R, Diaz A, Yu F (2013) Towards successful coordination of human and robotic work using automated scheduling tools: an initial pilot study. In: Proceedings of Robotics: Science and Systems, HumanâRobot Collaboration WorkshopGombolay MC, Gutierrez RA, Clarke SG, Sturla GF, Shah JA (2015) Decision-making authority, team efficiency and human worker satisfaction in mixed humanârobot teams. Auton Robots 39(3):293â312Frontoni E, Mancini A, Caponetti F, Zingaretti P (2006) A framework for simulations and tests of mobile robotics tasks. In: 2006 14th Mediterranean Conference on Control and Automation, pp 1â6I. Embarcadero Technologies, C++ Builder 10.2. https://www.embarcadero.com
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
An Open-Source Simulator for Cognitive Robotics Research: The Prototype of the iCub Humanoid Robot Simulator
This paper presents the prototype of a new computer simulator for the humanoid robot iCub. The iCub is a new open-source humanoid robot developed as a result of the âRobotCubâ project, a collaborative European project aiming at developing a new open-source cognitive robotics platform. The iCub simulator has been developed as part of a joint effort with the European project âITALKâ on the integration and transfer of action and language knowledge in cognitive robots. This is available open-source to all researchers interested in cognitive robotics experiments with the iCub humanoid platform
On the Integration of Adaptive and Interactive Robotic Smart Spaces
© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the userâs acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree â to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving usersâ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe
The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah
(ABRIDGED) In previous work, two platforms have been developed for testing
computer-vision algorithms for robotic planetary exploration (McGuire et al.
2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been
tested at geological and astrobiological field sites in Spain (Rivas
Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a
geological field site in Malta. In this work, we (i) apply a Hopfield
neural-network algorithm for novelty detection based upon color, (ii) integrate
a field-capable digital microscope on the wearable computer platform, (iii)
test this novelty detection with the digital microscope at Rivas Vaciamadrid,
(iv) develop a Bluetooth communication mode for the phone-camera platform, in
order to allow access to a mobile processing computer at the field sites, and
(v) test the novelty detection on the Bluetooth-enabled phone-camera connected
to a netbook computer at the Mars Desert Research Station in Utah. This systems
engineering and field testing have together allowed us to develop a real-time
computer-vision system that is capable, for example, of identifying lichens as
novel within a series of images acquired in semi-arid desert environments. We
acquired sequences of images of geologic outcrops in Utah and Spain consisting
of various rock types and colors to test this algorithm. The algorithm robustly
recognized previously-observed units by their color, while requiring only a
single image or a few images to learn colors as familiar, demonstrating its
fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International
Journal of Astrobiolog
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