The Experts below are selected from a list of 17700 Experts worldwide ranked by ideXlab platform

Simon Lacroix - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Managing Environment Models in multi-robot teams
    2016 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2016
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies first defining the proper data structures and associated mechanisms that allow both their efficient update and use by the decisional processes that require them, and second ensuring the Models consistency as the robots evolve. This article presents the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes the principles that govern the framework design, and illustrates them throughout some examples.

  • Managing Environment Models in multi-robot teams
    IEEE International Conference on Intelligent Robots and Systems, 2016
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    — Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies first defining the proper data structures and associated mechanisms that allow both their efficient update and use by the decisional processes that require them, and second ensuring the Models consistency as the robots evolve. This article presents the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes the principles that govern the framework design, and illustrates them throughout some examples.

  • A framework to manage Environment Models in multi-robot teams
    2015
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies defining the proper data structures and associated mechanisms that allow their efficient update and use by the decisional processes that require them, ensuring the Models consistency as the robots evolve. This article presents on-going work on the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes some principles for such a framework, proposes data structures to store and manage data and Models, and is illustrated by some simple examples.

  • Managing Environment Models in multi-robot teams
    2014
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Multi-robot cooperation in a dynamic Environment implies that each involved robot is able to create, update and exchange Environment Models – which are essential to the autonomy of mobile robots. The ability to exchange data, in other words to communicate, rely on the fact that robots share a common language and common frames in space and time. In this paper we present an open-source architecture dedicated to build a collection of Environment Models of a dynamic Environment with multiple robots. This work is lead with air-ground cooperation scenarios in mind, such as the ones considered within the Action project. Managing Models during the mission means defining methods and protocols to transmit and merge Models built from different sources and on different robots, while respecting time and bandwidth constraints and being robust to unavoidable inconsistencies between the Models. Our first work builds upon GDAL, a well known standard in geographic information systems (GIS). The architecture is currently able to build Models with different robots in simulation. Current work aims at detecting and solving inconsistencies between the various Models, and at porting the architecture on-board real robots.

  • Task planning and control for a multi-UAV system: Architecture and algorithms
    2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 2005
    Co-Authors: Jeremi Gancet, Rachid Alami, Gautier Hattenberger, Simon Lacroix
    Abstract:

    This paper presents a decisional architecture and the associated algorithms for multi-UAV (unmanned aerial vehicle) systems. The architecture enables different schemes of decision distribution in the system, depending on the available decision making capabilities of the UAVs and on the operational constraints related to the tasks to achieve. The paper mainly focuses on the deliberative layer of the UAVs: we detail a planning scheme where a symbolic planner relies on refinement tools that exploit UAVs and Environment Models. Integration effort related to decisional features is highlighted, and preliminary simulation results are provided.

Muhammad Zohaib Iqbal - One of the best experts on this subject based on the ideXlab platform.

  • Environment modeling and simulation for automated testing of soft real-time embedded software
    Software & Systems Modeling, 2013
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri
    Abstract:

    Given the challenges of testing at the system level, only a fully automated approach can really scale up to industrial real-time embedded systems (RTES). Our goal is to provide a practical approach to the model-based testing of RTES by allowing system testers, who are often not familiar with the system’s design but are application domain experts, to model the system Environment in such a way as to enable its black-box test automation. Environment Models can support the automation of three tasks: the code generation of an Environment simulator to enable testing on the development platform or without involving actual hardware, the selection of test cases, and the evaluation of their expected results (oracles). From a practical standpoint—and such considerations are crucial for industrial adoption—Environment modeling should be based on modeling standards (1) that are at an adequate level of abstraction, (2) that software engineers are familiar with, and (3) that are well supported by commercial or open source tools. In this paper, we propose a precise Environment modeling methodology fitting these requirements and discuss how these Models can be used to generate Environment simulators. The Environment Models are expressed using UML/MARTE and OCL, which are international standards for real-time systems and constraint modeling. The presented techniques are evaluated on a set of three artificial problems and on two industrial RTES.

  • Empirical Investigation of Search Algorithms for Environment Model-based Testing of Real-time Embedded Software
    Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA '12), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use Environment Models to automatically generate test cases and oracles and an Environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these Environment Models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the Environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results. © 2012 ACM.

  • Black-box system testing of real-time embedded systems using random and search-based testing
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
    Co-Authors: Andrea Arcuri, Muhammad Zohaib Iqbal
    Abstract:

    Testing real-time embedded systems (RTES) is in many ways challenging. Thousands of test cases can be potentially executed on an industrial RTES. Given the magnitude of testing at the system level, only a fully automated approach can really scale up to test industrial RTES. In this paper we take a black-box approach and model the RTES Environment using the UML/MARTE international standard. Our main motivation is to provide a more practical approach to the model-based testing of RTES by allowing system testers, who are often not familiar with the system design but know the application domain well-enough, to model the Environment to enable test automation. Environment Models can support the automation of three tasks: the code generation of an Environment simulator, the selection of test cases, and the evaluation of their expected results (oracles). In this paper, we focus on the second task (test case selection) and investigate three test automation strategies using inputs from UML/MARTE Environment Models: Random Testing (baseline), Adaptive Random Testing, and Search-Based Testing (using Genetic Algorithms). Based on one industrial case study and three artificial systems, we show how, in general, no technique is better than the others. Which test selection technique to use is determined by the failure rate (testing stage) and the execution time of test cases. Finally, we propose a practical process to combine the use of all three test strategies. © 2010 IFIP International Federation for Information Processing.

Lionel C Briand - One of the best experts on this subject based on the ideXlab platform.

  • Empirical Investigation of Search Algorithms for Environment Model-based Testing of Real-time Embedded Software
    Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA '12), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use Environment Models to automatically generate test cases and oracles and an Environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these Environment Models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the Environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results. © 2012 ACM.

Andrea Arcuri - One of the best experts on this subject based on the ideXlab platform.

  • Environment modeling and simulation for automated testing of soft real-time embedded software
    Software & Systems Modeling, 2013
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri
    Abstract:

    Given the challenges of testing at the system level, only a fully automated approach can really scale up to industrial real-time embedded systems (RTES). Our goal is to provide a practical approach to the model-based testing of RTES by allowing system testers, who are often not familiar with the system’s design but are application domain experts, to model the system Environment in such a way as to enable its black-box test automation. Environment Models can support the automation of three tasks: the code generation of an Environment simulator to enable testing on the development platform or without involving actual hardware, the selection of test cases, and the evaluation of their expected results (oracles). From a practical standpoint—and such considerations are crucial for industrial adoption—Environment modeling should be based on modeling standards (1) that are at an adequate level of abstraction, (2) that software engineers are familiar with, and (3) that are well supported by commercial or open source tools. In this paper, we propose a precise Environment modeling methodology fitting these requirements and discuss how these Models can be used to generate Environment simulators. The Environment Models are expressed using UML/MARTE and OCL, which are international standards for real-time systems and constraint modeling. The presented techniques are evaluated on a set of three artificial problems and on two industrial RTES.

  • Empirical Investigation of Search Algorithms for Environment Model-based Testing of Real-time Embedded Software
    Proceedings of the 2012 International Symposium on Software Testing and Analysis (ISSTA '12), 2012
    Co-Authors: Muhammad Zohaib Iqbal, Andrea Arcuri, Lionel C Briand
    Abstract:

    System testing of real-time embedded systems (RTES) is a challenging task and only a fully automated testing approach can scale up to the testing requirements of industrial RTES. One such approach, which offers the advantage for testing teams to be black-box, is to use Environment Models to automatically generate test cases and oracles and an Environment simulator to enable earlier and more practical testing. In this paper, we propose novel heuristics for search-based, RTES system testing which are based on these Environment Models. We evaluate the fault detection effectiveness of two search-based algorithms, i.e., Genetic Algorithms and (1+1) Evolutionary Algorithm, when using these novel heuristics and their combinations. Preliminary experiments on 13 carefully selected, non-trivial artificial problems, show that, under certain conditions, these novel heuristics are effective at bringing the Environment into a state exhibiting a system fault. The heuristic combination that showed the best overall performance on the artificial problems was applied on an industrial case study where it showed consistent results. © 2012 ACM.

  • Black-box system testing of real-time embedded systems using random and search-based testing
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
    Co-Authors: Andrea Arcuri, Muhammad Zohaib Iqbal
    Abstract:

    Testing real-time embedded systems (RTES) is in many ways challenging. Thousands of test cases can be potentially executed on an industrial RTES. Given the magnitude of testing at the system level, only a fully automated approach can really scale up to test industrial RTES. In this paper we take a black-box approach and model the RTES Environment using the UML/MARTE international standard. Our main motivation is to provide a more practical approach to the model-based testing of RTES by allowing system testers, who are often not familiar with the system design but know the application domain well-enough, to model the Environment to enable test automation. Environment Models can support the automation of three tasks: the code generation of an Environment simulator, the selection of test cases, and the evaluation of their expected results (oracles). In this paper, we focus on the second task (test case selection) and investigate three test automation strategies using inputs from UML/MARTE Environment Models: Random Testing (baseline), Adaptive Random Testing, and Search-Based Testing (using Genetic Algorithms). Based on one industrial case study and three artificial systems, we show how, in general, no technique is better than the others. Which test selection technique to use is determined by the failure rate (testing stage) and the execution time of test cases. Finally, we propose a practical process to combine the use of all three test strategies. © 2010 IFIP International Federation for Information Processing.

Pierrick Koch - One of the best experts on this subject based on the ideXlab platform.

  • IROS - Managing Environment Models in multi-robot teams
    2016 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2016
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies first defining the proper data structures and associated mechanisms that allow both their efficient update and use by the decisional processes that require them, and second ensuring the Models consistency as the robots evolve. This article presents the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes the principles that govern the framework design, and illustrates them throughout some examples.

  • Managing Environment Models in multi-robot teams
    IEEE International Conference on Intelligent Robots and Systems, 2016
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    — Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies first defining the proper data structures and associated mechanisms that allow both their efficient update and use by the decisional processes that require them, and second ensuring the Models consistency as the robots evolve. This article presents the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes the principles that govern the framework design, and illustrates them throughout some examples.

  • A framework to manage Environment Models in multi-robot teams
    2015
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Environment Models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same Environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of Environment Models are therefore required to evaluate and plan these actions. While the literature abounds with approaches to Environment modeling using data perceived by the robots, very few work tackle the problem of managing such Models within a team of robots. Managing Environment Models implies defining the proper data structures and associated mechanisms that allow their efficient update and use by the decisional processes that require them, ensuring the Models consistency as the robots evolve. This article presents on-going work on the definition of a framework dedicated to the managing of Environment Models within a robot team. It establishes some principles for such a framework, proposes data structures to store and manage data and Models, and is illustrated by some simple examples.

  • Managing Environment Models in multi-robot teams
    2014
    Co-Authors: Pierrick Koch, Simon Lacroix
    Abstract:

    Multi-robot cooperation in a dynamic Environment implies that each involved robot is able to create, update and exchange Environment Models – which are essential to the autonomy of mobile robots. The ability to exchange data, in other words to communicate, rely on the fact that robots share a common language and common frames in space and time. In this paper we present an open-source architecture dedicated to build a collection of Environment Models of a dynamic Environment with multiple robots. This work is lead with air-ground cooperation scenarios in mind, such as the ones considered within the Action project. Managing Models during the mission means defining methods and protocols to transmit and merge Models built from different sources and on different robots, while respecting time and bandwidth constraints and being robust to unavoidable inconsistencies between the Models. Our first work builds upon GDAL, a well known standard in geographic information systems (GIS). The architecture is currently able to build Models with different robots in simulation. Current work aims at detecting and solving inconsistencies between the various Models, and at porting the architecture on-board real robots.