Multi-robot task allocation clustering based on game theory

doi:10.1016/j.robot.2022.104314

Robotics and Autonomous Systems

Volume 161, March 2023, 104314

https://doi.org/10.1016/j.robot.2022.104314 Get rights and content

Under a Creative Commons license

open access

Highlights

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Cooperative game theory tools are considered to deal with MRTA problems.
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Robots and tasks are defined and ranked in a game according to their Shapley value.
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An algorithm is proposed to group the players into balanced clusters.
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Randomized methods are applied to large problems to relieve the computational load.
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The feasibility is assessed in a large scenario and contrasted with a genetic approach.

Abstract

A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.

Keywords

Multi-robot systems (MRS)

Multi-robot task allocation (MRTA)

Clustering

Task planning

Cooperative game theory

Shapley value

Data availability

All necessary data to reproduce the presented results are included along the manuscript.

Cited by (0)

Javier G. Martin was born in Cadiz in 1990. Since September 2018, he combines his Ph.D. in the field of automation, robotics and telematics at the Department of Systems and Automation Engineering of the University of Seville, with working as a researcher for the ADG-ERC OCONTSOLAR Project. His research interests focus on robotics and predictive control.

Francisco Javier Muros received the Ph.D. on automation, robotics and telematics, summa cum laude and international mention, from the University of Seville in 2017. Since 2005, he works in the medium voltage south control center in Endesa, acquiring a wide experience in the power network real-time operation and management. He received a master’s degree in project, construction and maintenance of high voltage electrical transmission from the Comillas Pontifical University, Madrid in 2014. Currently, he combines his work at Endesa with working as a part-time lecturer at the Loyola University Andalusia; also, he is an active researcher of the Department of Systems and Automation Engineering at the University of Seville. He has participated in the European Union OCONTSOLAR and DYMASOS Projects and in several national and regional projects. He has authored or co-authored more than 30 publications, highlighting 10-JCR journal papers and the book “Cooperative Game Theory Tools in Coalitional Control Networks” (Springer, 2019). His research interests focus on cooperative and noncooperative game theory and coalitional and distributed control.

José María Maestre got his PhD on automation and robotics from the University of Seville, where he works as full professor. He has also worked in other universities as TU Delft, University of Pavia, University of Keio, and Tokyo Institute of Technology. His main research interest is the control of distributed cyber-physical systems. He has (co-)authored around two hundred journal and conference papers and has (co-)edited the books “Service robotics within the Digital Home: Applications and Future Prospects” (Springer, 2011), “Distributed Model Predictive Control Made Easy” (Springer, 2014), and “Domótica para Ingenieros” (Paraninfo, 2015). Also, he has (co-)authored several books, including “A Programar se Aprende Jugando” (Paraninfo, 2017) and “Sistemas de Medida y Regulación” (Paraninfo, 2018).

Eduardo F. Camacho received the Ph.D. degree in electrical engineering from the University of Seville, where he is now a full professor with the Department of System and Automation Engineering. He is author of “Model Predictive Control in the Process Industry” (1995), “Control e Instrumentación de Procesos Químicos” (1997), “Advanced Control of Solar Plants” (1997), “Model Predictive Control” (1999, 2004), “Control of Dead-time Processes” (2007) and “Control of Solar Systems” (2012, translated into Chinese in 2014). He has served on various IFAC technical committees and chaired the IFAC publication Committee from 2002–2005. He was the president of the European Control Association (2005–2007) and chaired the IEEE/CSS International Affairs Committee (2003–2006), Chair of the IFAC Policy Committee, a member of the IEEE/CSS Board of Governors, and a member of the IFAC Council. He has acted as evaluator of projects at national and European level and was appointed Manager of the Advanced Production Technology Program of the Spanish National RD Program (1996–2000). He was one of the Spanish representatives on the Program Committee of the Growth Research program and expert for the Program Committee of the NMP research priority of the European Union. He has carried out reviews and editorial work for various technical journals and many conferences. He has been one of the Editors of the IFAC Journal, Control Engineering Practice, Editor-at-Large of the European Journal of Control and Subject Editor of Optimal Control: Methods and Applications. Dr. Camacho is an IEEE and IFAC Fellow. He was Publication Chair for the IFAC World Congress 2002, General Chair of the joint 44th IEEE CDC-ECC 2005, and co-General Chair of the joint 50th IEEE CDC-ECC 2011. He has been awarded an Advanced Grant by the European Research Council for the OCONTSOLAR Project, consisting of integrating solar radiation sensors mounted in drones to control solar plants.