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arXiv · 1806.00367

An Ontology to Support Collective Intelligence in Decentralised Multi-Robot Systems

Pragna Das·Vincent Hilaire·Lluis Ribas-Xirgo
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Abstract

In most multi-robot systems, conditions of the floor, battery and mechanical parts are important and impact cost-efficiency. The costs are generally interpreted through performance times. The relation between performance times andthese factors are not directly derivable, though, performance time has a direct correlation with discharge of batteries. Inroute planning, travel time of an edge is the performance time and may be required to be estimated for multiple times.These estimated travel times are different than heuristics costs as they depict the real states which are impossible toknow from heuristics. This facilitates path planning algorithms to choose the edges with least real travel times or coststo form the path. Nevertheless, a good estimation is dependent on historical data which are close in time. But, there aresituations when all the travel times for one or more edge(s) are not available for the entire duration of operation of theMRS to an individual robot. Then, it is imperative for that robot to gather the necessary travel times from others inthe system as a reference observation. The mechanism of information sharing between one robot to others in the systemhas been devised in a form of a common ontology-based knowledge. This ontology helps to fetch and share informationforming a collective knowledge base facilitating a comprehensive control and planning for the system. This greatly helpsthe MR to estimate travel times more accurately and precisely. Also, accurate estimation affects route planning to bemore precise with reduced cost. The total cost of paths generated through the travel times estimated through sharingis 40% less on average than that of paths generated through travel times without sharing.

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Pragna Das, Vincent Hilaire, Lluis Ribas-Xirgo. 2018-05-31. An Ontology to Support Collective Intelligence in Decentralised Multi-Robot Systems. https://arxiv.org/abs/1806.00367

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