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Analyzing and improving multi-robot missions by using process mining

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Abstract

Multi-robot missions can be compared to industrial processes or public services in terms of complexity, agents and interactions. Process mining is an emerging discipline that involves process modeling, analysis and improvement through the information collected by event logs. Currently, this discipline is successfully used to analyze several types of processes, but is hardly applied in the context of robotics. This work proposes a systematic protocol for the application of process mining to analyze and improve multi-robot missions. As an example, this protocol is applied to a scenario of fire surveillance and extinguishing with a fleet of UAVs. The results show the potential of process mining in the analysis of multi-robot missions and the detection of problems such as bottlenecks and inefficiencies. This work opens the way to an extensive use of these techniques in multi-robot missions, allowing the development of future systems for optimizing missions, allocating tasks to robots, detecting anomalies or supporting operator decisions.

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Acknowledgements

This work is framed on SAVIER (Situational Awareness Virtual EnviRonment) Project, which is both supported and funded by Airbus Defence & Space. The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU, and from the DPI2014-56985-R project (Protección robotizada de infraestructuras críticas) funded by the Ministerio de Economía y Competitividad of Gobierno de España. The experiments were performed in the facilities of Interdisciplinary Centre for Security, Reliability and Trust (SnT) of the University of Luxembourg (uni.lu).

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  1. Centre for Automation and Robotics (CAR, UPM-CSIC), José Gutiérrez Abascal, 2, 28006, Madrid, Spain

    Juan Jesús Roldán, Jaime del Cerro & Antonio Barrientos

  2. Interdisciplinary Centre for Security, Reliability and Trust (SnT, uni.lu), Richard Coudenhove-Kalergi, 6, 1359, Luxembourg, Luxembourg

    Miguel A. Olivares-Méndez

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  1. Juan Jesús Roldán
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Correspondence to Juan Jesús Roldán.

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Roldán, J.J., Olivares-Méndez, M.A., del Cerro, J. et al. Analyzing and improving multi-robot missions by using process mining. Auton Robot 42, 1187–1205 (2018). https://doi.org/10.1007/s10514-017-9686-1

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