Self-adaptive Coordination for Robot Teams Accomplishing Critical Activities

Georgé, Jean‐Pierre; Gleizes, Marie Pierre; Garijo, Francisco J.; Noël, Victor; Arcangeli, Jean‐Paul

doi:10.1007/978-3-642-12384-9_18

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Indeed, some initial experiments have started implementing the AMAS cooperation model so far where robots are considered AMAS agents with same capabilities (e.g. [17,18,21]). Team members have neither information nor explicit representation of the team's objectives.…”

Section: Developing Team Cooperation Models In the Experimental Framementioning

confidence: 99%

Deliberative control components for eldercare robot team cooperation

Gascueòa¹,

Garijo

Fernández-Caballero

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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One important research area in autonomous mobile robotics is to create companions that live in our ambience and perform tasks to help in everyday life. On the other hand, Ambient Intelligence (AmI) seeks to create a network of helpful intelligent devices. This paper describes an approach for the development of cooperation models for eldercare robot teams using goal-driven control components. The framework and the approach are illustrated through the development and assessment of task allocation in multi-robot teams. Two cooperation models are implemented: (i) a team model based on the adaptive multiagent systems theory where task responsibility is agreed among team peers by exchanging individual estimations of the degree of difficulty and priority to achieve the task; (ii) a hierarchical model where a robot manager asks for the estimations of its team members and then assigns the task. Experimentation for team cooperation assessment is performed through considering environmental changes, as well as communications and internal failures. The proposal is simulated in an AmI-oriented elderly care center to assist seniors in need.

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Section: Developing Team Cooperation Models In the Experimental Framementioning

confidence: 99%

Deliberative control components for eldercare robot team cooperation

Gascueòa¹,

Garijo

Fernández-Caballero

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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“…In the scenario, the objective of the AMAS AGVs is to plan and allocate tasks (to move to a destination to "rescue" a victim) among them. For that, each AMAS AGV follows a perceive-decide-act lifecycle and exchange messages with other AMAS AGVs by following a cooperative task allocation protocol defined using the AMAS theory (see [5]). Tweaks of the behaviour are focused on the evaluation of tasks criticality and their acceptation.…”

Section: Identification Of the Speciesmentioning

confidence: 99%

Engineering Agent Frameworks: An Application in Multi-Robot Systems

Lacouture

Noël

Arcangeli

et al. 2011

Advances in Intelligent and Soft Computing

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In this paper, we present a novel development process called SPEARAF (Species to Engineer Architectures for Agent Frameworks) and evaluate its relevance to ease the implementation of Multi-Agent Systems in the context of a multirobot project for crisis management. SPEARAF allows to build component-based architectures for agents and their infrastructure. We show the advantages of using an architecture-based process to realise an application-specific agent framework adapted to the requirements of such a system. SPEARAF gives guidelines to enables the use of architecture-oriented practices for agent implementation.

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“…They can be applied to a wide range of applications thanks to their intrinsic properties and features such as simplicity, flexibility, reliability, self-organization/emergent phenomena, low cost agent design and adaptation capacity. It has been shown that reactive multi-agent system are efficient to tackle complex problems [14], cooperation of situated agents/robots [15], data fusion and problem/game-solving [13]. In this context, our proposal consists in decision making by evaluating emergent properties of agent's organization.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Autonomous Navigation using Reactive Multi-agent System for Control Law Merging

Dafflon¹,

Vilca²,

Gechter³

et al. 2015

Procedia Computer Science

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International audienceThis paper deals with intelligent autonomous navigation of a vehicle in cluttered environment. We present a control architecture for safe and smooth navigation of a Unmanned Ground Vehicles (UGV). This control architecture is designed to allow the use of a single control law for different vehicle contexts (attraction to the target, obstacle avoidance, etc.) [4]. The reactive obstacle avoidance strategy is based on the limit-cycle approach [2]. To manage the interaction between the controllers according to the context, the multi-agent system is proposed. Multi-agent systems are an efficient approach for problem solving and decision making. They can be applied to a wide range of applications thanks to their intrinsic properties such as self-organization/emergent phenomena. Merging approach between control laws is based on their properties to adapt the control to the environment. Different simulations on cluttered environments show the performance and the efficiency of our proposal, to obtain fully reactive and safe control strategy, for the navigation of a UGV

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Self-adaptive Coordination for Robot Teams Accomplishing Critical Activities

Cited by 6 publications

References 5 publications

Deliberative control components for eldercare robot team cooperation

Deliberative control components for eldercare robot team cooperation

Engineering Agent Frameworks: An Application in Multi-Robot Systems

Adaptive Autonomous Navigation using Reactive Multi-agent System for Control Law Merging

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