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Multi-robot planning with conflicts and synergies

  • Yuqian Jiang
  • Harel YedidsionEmail author
  • Shiqi Zhang
  • Guni Sharon
  • Peter Stone
Article
  • 137 Downloads

Abstract

Multi-robot planning (mrp) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals, while minimizing overall cost. Solving mrp problems requires modeling limited domain resources (e.g., corridors that allow at most one robot at a time), and the possibility of action synergy (e.g., multiple robots going through a door after a single door-opening action). Optimally solving mrp problems is hard as it is a generalization of the single agent planning domain which is known to be NP-hard, and frequently requires considering the states of all the robots, resulting in an exponentially growing joint state and action space. In many mrp domains, robots encounter situations where they have conflicting needs for constrained resources, or where they can take advantage of what each other is doing to form synergies. In this article, we formulate the problem of multi-robot planning with conflicts and synergies (mrpcs), and develop a multi-robot planning framework, called iterative inter-dependent planning (iidp), for representing and solving mrpcs problems. Within the iidp framework, we develop the algorithms of increasing dependency and best alternative which exhibit different trade-offs between plan quality and computational efficiency. Extensive experiments covering the suggested algorithms have been performed using both an abstract-domain simulator, where we can automatically generate a variety of domain configurations, and a practical mrpcs instantiation that focuses on multi-robot navigation. In the navigation domain, we model plan costs with temporal uncertainty, and present a novel shifted-Poisson distribution for accumulating temporal uncertainty over actions. In comparison to baseline approaches, our algorithms produce significant reductions in overall plan cost, while avoiding search in the joint state space. In addition, we present a complete demonstration of the implementation of the model on a team of real robots.

Keywords

Multi-robot planning Planning under temporal uncertainty Intelligent mobile robotics 

Notes

Acknowledgements

This work has taken place in the Learning Agents Research Group (LARG) at the Artificial Intelligence Laboratory, The University of Texas at Austin. LARG research is supported in part by grants from the National Science Foundation (IIS-1637736, IIS-1651089, IIS-1724157), the Office of Naval Research (N00014-18-2243), Future of Life Institute (RFP2-000), DARPA, Intel, Raytheon, and Lockheed Martin. Peter Stone serves on the Board of Directors of Cogitai, Inc. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of TexasAustinUSA
  2. 2.Department of Computer ScienceThe State University of New YorkBinghamtonUSA
  3. 3.Computer Science & EngineeringTexas A&M UniversityTexasUSA

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