A Programming Framework for Multi-agent Coordination of Robotic Ecologies

Dragone, Mauro; Abdel-Naby, Sameh; Swords, David; O’Hare, Gregory M. P.; Broxvall, Mathias

doi:10.1007/978-3-642-38700-5_5

Cited by 8 publications

(2 citation statements)

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“…Our monitor components are implemented using the Self ‐OSGi framework, a Java‐based tool designed to supervise component‐based systems. Specifically, we have used Self ‐OSGi to implement monitoring components for the most important subsystems of the ROS navigation stack.…”

Section: Approachmentioning

confidence: 99%

An ambient intelligence approach for learning in smart robotic environments

Bacciu

Rocco

Dragone

et al. 2019

Computational Intelligence

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Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.

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Section: Approachmentioning

confidence: 99%

An ambient intelligence approach for learning in smart robotic environments

Bacciu

Rocco

Dragone

et al. 2019

Computational Intelligence

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“…The RUBICON Control Layer is a hierarchical multiagent system that models each device in the ecology as an autonomous agent with sensing and acting capabilities, and which uses a central configuration planner to find ways for these agents to cooperate to achieve a number of useful services [80]. Figure 8 shows how the Control Layer is organized in two levels: (i) a coordination level and (ii) a proxy and an agent-based service level .…”

Section: Control Layermentioning

confidence: 99%

Robotic Ubiquitous Cognitive Ecology for Smart Homes

Amato

Bacciu

Broxvall

et al. 2015

J Intell Robot Syst

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Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agentbased control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.

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