18 research outputs found

    Control Architecture for Cooperative Mobile Robots using Multi-Agent based Coordination Approach

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    National audienceThis paper is about a Multi-Agent based solution to control and coordinate team-working mobile robots moving in unstructured environments. Two main contributions are considered in our approach. The rst contribution of this paper is about the Multi-Agents System to Control and Coordinate teAmworking Robots (MAS2CAR) architecture, a new architecture to control a group of coordinated autonomous robots in unstructured environments. MAS2CAR covers three main layers: (i) the Physical Layer (ii) the Control Layer and (iii) the Coordination Layer. The second contribution of this paper is about the multi-agent system (MAS) organisational models aiming to solve the key cooperation issues in the coordination layer, the software components designed based on Utopia a MAS framework which automatically build software agents, thanks to a multi-agent based organisational model called MoiseInst . We provide simulation results that exhibit robotics cooperative behavior related to our scenario, such as multi-robots navigation in presence of obstacles (including trajectory planning, and reactive aspects) via a hybrid control

    Control and management architecture for distributed autonomous systems : application to multi-vehicles based platform

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    La complexitĂ© associĂ©e Ă  la coordination d’un groupe de robots mobiles est traitĂ©e dans cette thĂšse en investiguant plus avant les potentialitĂ©s des architectures de commande multi-contrĂŽleurs dont le but est de briser la complexitĂ© des tĂąches Ă  exĂ©cuter. En effet, les robots mobiles peuvent Ă©voluer dans des environnements trĂšs complexes et nĂ©cessitent de surcroĂźt une coopĂ©ration prĂ©cise et sĂ©curisĂ©e pouvant rapidement devenir inextricable. Ainsi, pour maĂźtriser cette complexitĂ©, le contrĂŽleur dĂ©diĂ© Ă  la rĂ©alisation d’une tĂąche est dĂ©composĂ© en un ensemble de comportements/contrĂŽleurs Ă©lĂ©mentaires (Ă©vitement d’obstacles et de collision entre les robots, attraction vers une cible, planification, etc.) qui lient les informations capteurs (provenant des capteurs locaux du robot, etc.) aux actionneurs des diffĂ©rentes entitĂ©s robotiques. La tĂąche considĂ©rĂ©e dans cette thĂšse correspond Ă  la navigation d’un groupe de robots mobiles dans des environnements peu ou pas connus en prĂ©sence d’obstacles (statiques et dynamiques). La spĂ©cificitĂ© de l’approche thĂ©orique consiste Ă  allier les avantages des architectures multi-contrĂŽleurs Ă  ceux des systĂšmes multi-agents et spĂ©cialement les modĂšles organisationnels afin d’apporter un haut niveau de coordination entre les agents/robots mobiles. Le groupe de robots mobiles est alors coordonnĂ© suivant les diffĂ©rentes normes et spĂ©cifications du modĂšle organisationnel. Ainsi, l’activation d’un comportement Ă©lĂ©mentaire en faveur d’un autre se fait en respectant les contraintes structurelles des robots en vue d’assurer le maximum de prĂ©cision et de sĂ©curitĂ© des mouvements coordonnĂ©s entre les diffĂ©rentes entitĂ©s mobiles. La coopĂ©ration se fait Ă  travers un agent superviseur (centralisĂ©) de façon Ă  atteindre plus rapidement la destination dĂ©sirĂ©e, les Ă©vĂ©nements inattendus sont gĂ©rĂ©s quant Ă  eux individuellement par les agents/robots mobiles de façon distribuĂ©e. L’élaboration du simulateur ROBOTOPIA nous a permis d’illustrer chacune des contributions de la thĂšse par un nombre important de simulations.The difficulty of coordinating a group of mobile robots is adressed in this thesis by investigating control architectures which aim to break task complexity. In fact, multi-robot navigation may become rapidly inextricable, specifically if it is made in hazardous and dynamical environment requiring precise and secure cooperation. The considered task is the navigation of a group of mobile robots in unknown environments in presence of (static and dynamic) obstacles. To overcome its complexity, it is proposed to divide the overall task into a set of basic behaviors/controllers (obstacle avoidance, attraction to a dynamical target, planning, etc.). Applied control is chosen among these controllers according to sensors information (camera, local sensors, etc.). The specificity of the theoretical approach is to combine the benefits of multi-controller control architectures to those of multi-agent organizational models to provide a high level of coordination between mobile agents-robots systems. The group of mobile robots is then coordinated according to different norms and specifications of the organizational model. Thus, activating a basic behavior in favor of another is done in accordance with the structural constraints of the robots in order to ensure maximum safety and precision of the coordinated movements between robots. Cooperation takes place through a supervisor agent (centralized) to reach the desired destination faster ; unexpected events are individually managed by the mobile agents/robots in a distributed way. To guarantee performance criteria of the control architecture, hybrid systems tolerating the control of continuous systems in presence of discrete events are explored. In fact, this control allows coordinating (by discrete part) the different behaviors (continuous part) of the architecture. The development of ROBOTOPIA simulator allowed us to illustrate each contribution by many results of simulations

    Architecture de COntrĂŽle/COmmande dĂ©diĂ©e aux systĂšmes DistribuĂ©s Autonomes (ACOÂČDA) : application Ă  une plate-forme multi-vĂ©hicules

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    The difficulty of coordinating a group of mobile robots is adressed in this thesis by investigating control architectures which aim to break task complexity. In fact, multi-robot navigation may become rapidly inextricable, specifically if it is made in hazardous and dynamical environment requiring precise and secure cooperation. The considered task is the navigation of a group of mobile robots in unknown environments in presence of (static and dynamic) obstacles. To overcome its complexity, it is proposed to divide the overall task into a set of basic behaviors/controllers (obstacle avoidance, attraction to a dynamical target, planning, etc.). Applied control is chosen among these controllers according to sensors information (camera, local sensors, etc.). The specificity of the theoretical approach is to combine the benefits of multi-controller control architectures to those of multi-agent organizational models to provide a high level of coordination between mobile agents-robots systems. The group of mobile robots is then coordinated according to different norms and specifications of the organizational model. Thus, activating a basic behavior in favor of another is done in accordance with the structural constraints of the robots in order to ensure maximum safety and precision of the coordinated movements between robots. Cooperation takes place through a supervisor agent (centralized) to reach the desired destination faster ; unexpected events are individually managed by the mobile agents/robots in a distributed way. To guarantee performance criteria of the control architecture, hybrid systems tolerating the control of continuous systems in presence of discrete events are explored. In fact, this control allows coordinating (by discrete part) the different behaviors (continuous part) of the architecture. The development of ROBOTOPIA simulator allowed us to illustrate each contribution by many results of simulations.La complexitĂ© associĂ©e Ă  la coordination d’un groupe de robots mobiles est traitĂ©e dans cette thĂšse en investiguant plus avant les potentialitĂ©s des architectures de commande multi-contrĂŽleurs dont le but est de briser la complexitĂ© des tĂąches Ă  exĂ©cuter. En effet, les robots mobiles peuvent Ă©voluer dans des environnements trĂšs complexes et nĂ©cessitent de surcroĂźt une coopĂ©ration prĂ©cise et sĂ©curisĂ©e pouvant rapidement devenir inextricable. Ainsi, pour maĂźtriser cette complexitĂ©, le contrĂŽleur dĂ©diĂ© Ă  la rĂ©alisation d’une tĂąche est dĂ©composĂ© en un ensemble de comportements/contrĂŽleurs Ă©lĂ©mentaires (Ă©vitement d’obstacles et de collision entre les robots, attraction vers une cible, planification, etc.) qui lient les informations capteurs (provenant des capteurs locaux du robot, etc.) aux actionneurs des diffĂ©rentes entitĂ©s robotiques. La tĂąche considĂ©rĂ©e dans cette thĂšse correspond Ă  la navigation d’un groupe de robots mobiles dans des environnements peu ou pas connus en prĂ©sence d’obstacles (statiques et dynamiques). La spĂ©cificitĂ© de l’approche thĂ©orique consiste Ă  allier les avantages des architectures multi-contrĂŽleurs Ă  ceux des systĂšmes multi-agents et spĂ©cialement les modĂšles organisationnels afin d’apporter un haut niveau de coordination entre les agents/robots mobiles. Le groupe de robots mobiles est alors coordonnĂ© suivant les diffĂ©rentes normes et spĂ©cifications du modĂšle organisationnel. Ainsi, l’activation d’un comportement Ă©lĂ©mentaire en faveur d’un autre se fait en respectant les contraintes structurelles des robots en vue d’assurer le maximum de prĂ©cision et de sĂ©curitĂ© des mouvements coordonnĂ©s entre les diffĂ©rentes entitĂ©s mobiles. La coopĂ©ration se fait Ă  travers un agent superviseur (centralisĂ©) de façon Ă  atteindre plus rapidement la destination dĂ©sirĂ©e, les Ă©vĂ©nements inattendus sont gĂ©rĂ©s quant Ă  eux individuellement par les agents/robots mobiles de façon distribuĂ©e. L’élaboration du simulateur ROBOTOPIA nous a permis d’illustrer chacune des contributions de la thĂšse par un nombre important de simulations

    Mobile Robot Navigation and Obstacles Avoidance based on Planning and Re-Planning Algorithm

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    International audienceThis paper deals with a multi-mode control architecture for robot navigation and obstacle avoidance. It presents an adaptive and flexible algorithm of control which guarantees the stability and the smoothness of mobile robot navigation dealing with unexpected events. Moreover, the proposed Planning and Re-Planning (PRP) algorithm combine the two schools of thought, the one based on the path planning to avoid obstacles and reach the target, described as cognitive, and the second using the reactive algorithms. In fact the mix of these two approaches allows us to develop a very reliable algorithm. It provides us a scalable mobile robot navigation and obstacle avoidance, with less processing. It is accomplished by making an initial path planning, then to resolve the problem of unexpected static or dynamic obstacles while tracking the trajectory. A system of hierarchical action selection allows us to switch to a reactive avoidance, then to re-plan a new and safe trajectory to reach the target. A large number of simulations in different environments are performed to show the efficiency of the proposed PRP algorithm

    Architecture Controlling Multi-Robot System using Multi-Agent based CoordinationApproach

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    International audience—This paper is about a Multi-Agent based solution to control and coordinate team-working mobile robots moving in unstructured environments. Two main contributions are considered in our approach. The first contribution of this paper is about the Multi-Agents System to Control and Coordinate teAmworking Robots (MAS2CAR) architecture, a new architecture to control a group of coordinated autonomous robots in unstructured environments. MAS2CAR covers three main layers: (i) the Physical Layer (ii) the Control Layer and (iii) the Coordination Layer. The second contribution of this paper is about the multi-agent system (MAS) or-ganisational models aiming to solve the key cooperation issues in the coordination layer, the software components designed based on U TOPIA a MAS framework which automatically build software agents, thanks to a multi-agent based organisational model called MOISE Inst. We provide simulation results that exhibit robotics cooperative behavior related to our scenario, such as multi-robots navigation in presence of obstacles (including trajectory planning, and reactive aspects) via a hybrid control

    Multi-agents based system to coordinate mobile teamworking robots

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    International audienceThis paper aims at presenting the Multi-Agents System to Control and Coordinate teAmworking Robots (MAS2CAR), a new architecture to control a group of coordinated autonomous robots in un-structured environments. MAS2CAR covers two main layers: (i) the Control Layer and we focus on (ii) the Coordination Layer. The control module is responsible for a part of the decision making process taking into account robot's structural constraints. Despite this autonomy possibility, the Coordination Layer manages the robots in order to bring cooperative behavior and to allow teamwork. In this paper we present a scenario validating our approach based upon the multi-agent systems (MAS). Thanks to its reliability we have chosen the Moise Inst organizational model and we will present how it can be used for this use-case. Moreover, regarding to the implementation part, we have retained Utopia, a framework which automatically build a MAS thanks to a Moise Inst specification. We will present key problematics of the Cooperation Layer implementation solved thanks to Utopia and exhibit robotic cooperative behavior related to our scenario through simulation results

    Revealing the Influence of Electrolyte Additives in Reducing the Heat Generation of High Voltage Lithium‐Ion Batteries using Operando Accelerating Rate Calorimetry (ARC)

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    Abstract Lithium‐ion batteries operating at high voltage generally endure drastic capacity fading and serious safety issues. Working on the electrolytes’ stability can be a solution to mitigate these problems related to high voltage. Herein, the beneficial impact of functional electrolyte additives in a state‐of‐the‐art carbonate‐based electrolyte is demonstrated. The combination of fluoroethylene carbonate (FEC) with succinonitrile (SN) as additives was used to enhance the thermal stability of the electrolyte reference 1 M LiPF 6 in EC:DMC (1 : 1, by weight) and cycling stability of a high voltage lithium‐ion device, consisting of a LiMn 1.5 Ni 0.5 O 4 cathode and a metallic lithium anode. The electrolyte using the FEC/SN mixture displayed a wider electrochemical stability window (ESW) exhibited by linear sweep voltammetry (LSV). Furthermore, this electrolyte allowed the device to exhibit better rate capability and a capacity retention of 75 % after 100 cycles. Interestingly, the FEC+SN‐based electrolyte exhibited better thermal stability using operando accelerating rate calorimetry (ARC) by virtue of the lower heat quantity generated by the battery device. The remarkable improvements can be ascribed to the formation of a protective cathode‐electrolyte interface (CEI) produced by interfacial reactions between the cathode surface and electrolyte compounds
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