{"type":"ScholarlyArticle","title":"Summary: Multi-Agent Path Finding with Kinematic Constraints","authors":[{"name":"Wolfgang Hönig","slug":"wolfgang-honig","given":"Wolfgang","family":"Hönig","affiliation":"Department of Computer Science University of Southern California"},{"name":"T. K. Satish Kumar","slug":"t-k-satish-kumar","given":"T. K. Satish","family":"Kumar","affiliation":"Department of Computer Science University of Southern California"},{"name":"Liron Cohen","slug":"liron-cohen","given":"Liron","family":"Cohen","affiliation":"Department of Computer Science University of Southern California"},{"name":"Hang Ma","slug":"hang-ma","given":"Hang","family":"Ma","affiliation":"Department of Computer Science University of Southern California"},{"name":"Hong Xu","slug":"hong-xu","given":"Hong","family":"Xu","affiliation":"Department of Computer Science University of Southern California"},{"name":"Nora Ayanian","slug":"nora-ayanian","given":"Nora","family":"Ayanian","affiliation":"Department of Computer Science University of Southern California"},{"name":"Sven Koenig","slug":"sven-koenig","given":"Sven","family":"Koenig","affiliation":"Department of Computer Science University of Southern California"}],"abstract":"Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, considers kinematic constraints, provides a guaranteed safety distance between robots, and exploits slack to avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.","doi":"10.24963/ijcai.2017/684","url":"https://ijcai.org/proceedings/2017/684","pdf":"https://ijcai.org/proceedings/2017/0684.pdf","markdown":"https://ijcai.org/proceedings/2017/684.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/684","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":684,"pages":{"first":4869,"last":4873},"keywords":["Artificial Intelligence: robotics","Artificial Intelligence: multi-agent systems","Artificial Intelligence: artificial intelligence"],"videos":[],"references":[{"position":1,"raw":"[Barer et al., 2014] M. 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