Multi-robot coalition formation in real-time scenarios |
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| Authors: | José Guerrero Gabriel Oliver |
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| Affiliation: | 1. School of Mathematics and Statistics, Shandong University, Weihai 264209, China;2. School of Control Science and Engineering, Shandong University, Jinan 250061, China;3. Department of Mathematics, Tianjin University, Tianjin, China;4. College of Science, North China University of Technology, Beijing 100144, China;1. Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China;2. College of Automation, Chongqing University, Chongqing 400044, China;1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, PR China;3. Institute of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, PR China;4. School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, PR China;5. Department of Mathematics and Computer Science, Tongren College, Tongren 554300, PR China;6. School of Science, Tianjin University, Tianjin 300072, PR China;1. Computer Science Department, University of Verona, Verona CAP 37134, Italy;2. Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom;2. Information Technology and Services Accenture, Turin, Italy;1. Max-Planck-Institut für Informatik and Saarland University, Germany;2. Max-Planck-Institut für Informatik and Saarland University Graduate School of Computer Science, Germany;3. Department of Computer Science, RWTH Aachen University, Germany |
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| Abstract: | Task allocation is one of the main issues to be addressed in multi-robot systems, especially when the robots form coalitions and the tasks have to be fulfilled before a deadline. In general, it is difficult to foresee the time required by a coalition to finish a task because it depends, among other factors, on the physical interference. Interference is a phenomenon produced when two or more robots want to access the same point simultaneously. This paper presents a new model to predict the time to execute a task. Thanks to this model, the robots needed to carry out a task before a deadline can be determined. Within this framework, the robots learn the interference and therefore, the coalition’s utility, from their past experience using an on-line Support Vector Regression method (SVR). Furthermore, the SVR model is used together with a new auction method called ’Double Round auction’ (DR). It will be demonstrated that by combining the interference model and the DR process, the total utility of the system significantly increases compared to classical auction approaches. This is the first auction method that includes the physical interference effects and that can determine the coalition size during the execution time to address tasks with deadlines. Transport like tasks run on a simulator and on real robots have been used to validate the proposed solutions. |
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