{"title":"Hamiltonian coordination primitives for decentralized multiagent navigation","authors":"Christoforos Mavrogiannis, Ross A. Knepper","doi":"10.1177/02783649211037731","DOIUrl":null,"url":null,"abstract":"We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robotics Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/02783649211037731","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 9
Abstract
We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.
期刊介绍:
The International Journal of Robotics Research (IJRR) has been a leading peer-reviewed publication in the field for over two decades. It holds the distinction of being the first scholarly journal dedicated to robotics research.
IJRR presents cutting-edge and thought-provoking original research papers, articles, and reviews that delve into groundbreaking trends, technical advancements, and theoretical developments in robotics. Renowned scholars and practitioners contribute to its content, offering their expertise and insights. This journal covers a wide range of topics, going beyond narrow technical advancements to encompass various aspects of robotics.
The primary aim of IJRR is to publish work that has lasting value for the scientific and technological advancement of the field. Only original, robust, and practical research that can serve as a foundation for further progress is considered for publication. The focus is on producing content that will remain valuable and relevant over time.
In summary, IJRR stands as a prestigious publication that drives innovation and knowledge in robotics research.