Abstract-Networked robotics is an area that integrates multi-robot and network technology. The characteristics and the reliability of the communication environment play a fundamental role shaping and affecting behavior and performance of a mobile multi-robot system. In this context, two basic questions arise: how much the overall performance is affected and how can we investigate this influence? Addressing these two questions, in this paper we present the architecture of an integrated simulation environment that allows for realistic simulation of networked robotic systems. The proposed framework integrates two simulators: a network simulator and a multi-robot simulator. We present two implementations based on the ARGoS simulator for the robotic side, and with ns-2 and ns-3 employed as network simulators. We evaluate the proposed tools, both in isolation and integration, and show that they are able to efficiently simulate systems consisting of hundreds of robots. Moreover, we use the proposed framework to demonstrate the effects of communication on the performance of a mobile multi-robot system performing distributed coordination and task assignment. We compare realistic network simulation with simplified communication models and we study the resulting behavior and performance of the robotic system.
Part 1: Knowledge-Based SustainabilityInternational audienceThe human dimension is growing in importance in the cul- tural and scientific debate surrounding the arising of workplace and fac- tory of the future visions. Having people at the centre of the factory is already recognized as a main enabler for making the most out of their skills and capacities while at the same time achieving an environment that can both motivate employed workers and attract new skilled ones. The present paper proposes a novel concept aimed at defining new so- cially sustainable workplaces that adapt to workers’ anthropometric di- mensions within worker-aware production systems that are designed and operated to capitalize on workers’ skills and experience while at the same time promoting their development. Moreover it envisions the integration of the factory in the social and environmental context by promoting the creation and provision of worker-centric services that turn the factory from a society-affecting entity into an integration-promotion body
The new point of view in which factory and workers are seen is the person at the centre of the production system, so employees should be involved in job design and task balancing processes. The advantages coming by this paradigm shift, from the task-centric organization to the worker-centric factory is doubtless the high correlation among job and worker in terms of skill, experience, and worker's features. Human-centric system is useful to improve the knowledge and the capabilities of workers regardless of age and role, and in this kind of model the job suits the worker and his needs. In this context, it is of paramount importance to design and develop a worker-centric job allocator tool in which the human dimension is a key factor. This study therefore addresses the requirements and design aspects of a worker-centric job allocator as an enabler for human-centric workplaces of the future.
Besides pursuing the economic goals of low costs and high profits, companies are becoming more and more aware of the environmental and social impact of their actions. Companies striving for the integrated optimization of environmental and economic perspectives within their production processes, need to be supported by tools helping to understand the effects of the decision making process. In this context, this paper describes a Decision Support System (DSS) enabling the early identification of problems occurring on manufacturing lines thus suggesting related recovery actions, together with the potential repercussions of their adoption, at economic and environmental level. The decision making process beneath the DSS starts from the aggregation of production lines sensors data in Key Performance Indicators (KPI). The data are then processed by means of an Artificial Neural Networks (ANN) based knowledge system which enables to suggest preventive maintenance interventions. The proposed maintenance activities, elaborated throughout a scheduling engine, are integrated within the weekly production schedule, according to the selected optimization policy.
Preliminary tests have been carried out in manufacturing plants of IKEA industries and Brembo.
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