{"type":"ScholarlyArticle","title":"Multi-robot Task Allocation in the Environment with Functional Tasks","authors":[{"name":"Fuhan Yan","slug":"fuhan-yan","given":"Fuhan","family":"Yan","affiliation":"College of Computer Science and Technology , Chongqing University of Posts and Telecommunications , Chongqing , China."},{"name":"Kai Di","slug":"kai-di","given":"Kai","family":"Di","affiliation":"School of computer science and engineering , Southeast University , Nanjing , China."}],"abstract":"Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the problem, neither allocating all functional tasks nor allocating no functional task is always optimal. Previous algorithms usually allocate all tasks and cannot suitably select the functional tasks. Because of the interaction and sequential influence, the total effects of the functional tasks are too complex to exactly calculate. We fully analyze this problem and then design a heuristic algorithm. The heuristic algorithm scores the functional tasks referring to linear threshold model (used to analyze the sequential influence of a functional task). The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms.","doi":"10.24963/ijcai.2022/653","url":"https://ijcai.org/proceedings/2022/653","pdf":"https://ijcai.org/proceedings/2022/0653.pdf","markdown":"https://ijcai.org/proceedings/2022/653.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/653","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Robotics","track":"Main Track","number":653,"pages":{"first":4710,"last":4716},"keywords":["Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence","Agent-based and Multi-agent Systems: Coordination and Cooperation","Robotics: Multi-Robot Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986700","length_sec":77,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984843","length_sec":339,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Behnck et al., 2015] Lucas P Behnck, Dionisio Doering, Carlos Eduardo Pereira, and Achim Rettberg. 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