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  • 1.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology.
    Pomareda Sese, Victor
    Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, Örebro, Sweden..
    Gasbot: A Mobile Robotic Platform for Methane Leak Detection and Emission Monitoring2012In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on Robotics for Environmental Monitoring (WREM), Vilamoura, Portugal, October 7-12, 2012, 2012Conference paper (Refereed)
    Abstract [en]

    Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor, along with a novel gas distribution algorithm to generate methane concentration maps of indoor and outdoor exploration areas. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

  • 2.
    Hernandez Bennetts, Victor Manuel
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology.
    Pomareda Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Towards Real-World Gas Distribution Mapping and Leak Localization Using a Mobile Robot with 3D and Remote Gas Sensing Capabilities2013In: 2013 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE conference proceedings, 2013, p. 2335-2340Conference paper (Refereed)
    Abstract [en]

    Due to its environmental, economical and safety implications, methane leak detection is a crucial task to address in the biogas production industry. In this paper, we introduce Gasbot, a robotic platform that aims to automatize methane emission monitoring in landfills and biogas production sites. The distinctive characteristic of the Gasbot platform is the use of a Tunable Laser Absorption Spectroscopy (TDLAS) sensor. This sensor provides integral concentration measurements over the path of the laser beam. Existing gas distribution mapping algorithms can only handle local measurements obtained from traditional in-situ chemical sensors. In this paper we also describe an algorithm to generate 3D methane concentration maps from integral concentration and depth measurements. The Gasbot platform has been tested in two different scenarios: an underground corridor, where a pipeline leak was simulated and in a decommissioned landfill site, where an artificial methane emission source was introduced.

  • 3.
    Khaliq, Ali Abdul
    Örebro University, School of Science and Technology.
    From Ants to Service Robots: an Exploration in Stigmergy-Based Navigation Algorithms2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Navigation is a core functionality of mobile robots. To navigate autonomously, a mobile robot typically relies on internal maps, self-localization, and path planning. Reliable navigation usually comes at the cost of expensive sensors and often requires significant computational overhead.

    Many insects in nature perform robust, close-to-optimal goal directed navigation without having the luxury of sophisticated sensors, powerful computational resources, or even an internally stored map. They do so by exploiting a simple but powerful principle called stigmergy: they use their environment as an external memory to store, read and share information. In this thesis, we explore the use of stigmergy as an alternative route to realize autonomous navigation in practical robotic systems.

    In our approach, we realize a stigmergic medium using RFID (Radio Frequency Identification) technology by embedding a grid of read-write RFID tags in the floor. A set of mobile robots, then, build and store maps used for navigation in the stigmergic medium itself. These maps are of three types: (1) goal maps which guide robots to known locations; (2) clearance maps which help robots avoid obstacles; (3) feature maps which can be used to store observable properties, such as light intensity or gas concentration. We show how these maps can be built both in static and in dynamic environments and used for navigation of heterogeneous robots. We also show that goal maps can be used for navigation to previously unknown and/or dynamic locations, and that feature maps can be used to navigate towards specific features, e.g., places with high gas concentration that are beyond the sensor’s range. We address the issue of perceptual errors (e.g., broken tags) during navigation. We further study the use of the built navigation maps to enable different types of human-aware robot navigation on the RFID floor.

    We define several stigmergic algorithms for building maps and navigating on these maps. We formally analyse the properties of the main algorithms, and empirically evaluate all the algorithms both in simulation and with multiple physical robots. Results collected from tens of hours of real experiments and thousands of simulated runs demonstrate the effectiveness of our approach.

  • 4.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Stigmergic algorithms for multiple minimalistic robots on an RFID floor2014In: Swarm Intelligence, ISSN 1935-3820, Vol. 8, no 3, p. 199-225Article in journal (Refereed)
    Abstract [en]

    Stigmergy is a powerful principle in nature, which has been shown to have interesting applications to robotic systems. By leveraging the ability to store information in the environment, robots with minimal sensing, memory, and computational capabilities can solve complex problems like global path planning. In this paper, we discuss the use of stigmergy in minimalist multi-robot systems, in which robots do not need to use any internal model, long-range sensing, or position awareness. We illustrate our discussion with three case studies: building a globally optimal navigation map, building a gradient map of a sensed feature, and updating the above maps dynamically. All case studies have been implemented in a real environment with multiple ePuck robots, using a floor with 1,500 embedded radio frequency identification tags as the stigmergic medium. Results collected from tens of hours of real experiments and thousands of simulated runs demonstrate the effectiveness of our approach.

  • 5.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Di Rocco, Maurizio
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Stigmergic Algorithms for Simple Robotic Devices (Extended abstract)2013In: Workshop on Unconventional Approaches to Robotics, Automation and Control Inspired by Nature (ICRA 2013), 2013Conference paper (Refereed)
    Abstract [en]

    This position paper is meant to discuss the use of stigmergy in minimalist robotic systems, and to argue for a methodological approach based on the combination of formal analysis and empirical evaluation. In the full paper we will illustrate this approach in three case studies: building a globally optimal navigation map, building a gas concentration gradient map, and updating the above maps dynamically. All case studies have been implemented in a real environment with inexpensive robots, using an RFID floor as the stigmergic medium.

  • 6.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Inexpensive, reliable and localization-free navigation using an RFID floor2015In: 2015 European Conference on Mobile Robots (ECMR), New York: IEEE conference proceedings , 2015, article id 7324204Conference paper (Refereed)
    Abstract [en]

    Stigmergy is a principle observed in nature, in which animals store in the environment information to be used for communication or navigation. Stigmergy has recently been exploited in robotics: simple robots store a goal distance field in read-write RFID tags embedded in the floor, and later follow the gradient of this field to navigate optimally to that goal. Stigmergic navigation is localization-free, since robots only rely on the values read from the tags and do not need to know their own location. This makes navigation inexpensive (no ranging sensors) and reliable (no localization failures). To make this approach viable in practice, two issues need to be addressed: how to simplify the installation of an RFID floor; and how to follow the field gradient in a reliable way. This paper presents solutions to both problems. The solutions are validated through experiments performed on simulated and on real robots.

  • 7.
    Khaliq, Ali Abdul
    et al.
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Stigmergy at work: Planning and navigation for a service robot on an RFID floor2015In: IEEE International Conference on Robotics and Automation, IEEE Computer Society, 2015, p. 1085-1092Conference paper (Refereed)
    Abstract [en]

    Many species in nature store information in the environment to facilitate the performance of tasks and enable cooperation. This principle is known as stigmergy. Stigmergy has been widely studied in robotic systems, but so far mostly in simulation or in laboratory proofs of concept. In this paper, we propose a stigmergic approach to goal-directed navigation that can be used for navigation of a full-scale robotic system in a real apartment. A team of small ePuck robots build a set of navigation maps directly onto an RFID floor, where each map is associated to one predefined goal. The information stored in the floor can then used by a mid-size robot or by a larger domestic robot to perform safe navigation toward the predefined goals. To navigate, robots only rely on the information read from the RFID tags: in particular, they do not need to use an internal map or to perform self-localization. This results in robust and repeatable navigation with minimal hardware and software requirements.

  • 8.
    Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Bringing Artificial Olfaction and Mobile Robotics Closer Together: An Integrated 3D Gas Dispersion Simulator in ROS2015In: Proceedings of the 16th International Symposium on Olfaction and Electronic Noses, 2015Conference paper (Refereed)
    Abstract [en]

    Despite recent achievements, the potential of gas-sensitive mobile robots cannot be realized due to the lack of research on fundamental questions. A key limitation is the difficulty to carry out evaluations against ground truth. To test and compare approaches for gas-sensitive robots a truthful gas dispersion simulator is needed. In this paper we present a unified framework to simulate gas dispersion and to evaluate mobile robotics and gas sensing algorithms using ROS. Gas dispersion is modeled as a set of particles affected by diffusion, turbulence, advection and gravity. Wind information is integrated as time snapshots computed with any fluid dynamics computation tool. In addition, response models for devices such as Metal Oxide (MOX) sensors can be integrated in the framework.

  • 9.
    Pomareda, Victor
    et al.
    Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Abdul Khaliq, Ali
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Marco, Santiago
    Intelligent Signal Processing, Department of Electronics, University of Barcelona, Barcelona, Spain.
    Chemical source localization in real environments integrating chemical concentrations in a probabilistic plume mapping approach2013In: Proceedings of the 15th International Symposium on Olfaction and Electronic Nose (ISOEN 2013), 2013Conference paper (Refereed)
    Abstract [en]

    Chemical plume source localization algorithms can be classified either as reactive plume tracking or gas distribution mapping approaches. Here, we focus on gas distribution mapping methods where the robot does not need to track the plume to find the source and can be used for other tasks. Probabilistic mapping approaches have been previously applied to real-world data successfully; e.g., in the approach proposed by Pang and Farrell. Instead of the quasi-continuous gas measurement values, this algorithm considers events (detections and non-detections) based on whether the sensor response is above or below a threshold to update recursively a source probability grid map; thus, discarding important information. We developed an extension of this event-based approach, integrating chemical concentrations directly instead of binary information. In this work, both algorithms are compared using real-world data obtained from a photo-ionization detector (PID), a non-selective gas sensor, and an anemometer in real environments. We validate simulation results and demonstrate that the concentration-based approach is more accurate in terms of a higher probability at the ground truth source location, a smaller distance between the probability maximum and the source location, and a more peaked probability distribution, measured in terms of the overall entropy.

1 - 9 of 9
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  • ieee
  • modern-language-association-8th-edition
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  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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  • text
  • asciidoc
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