• Corpus ID: 245123689

Multi-Robot On-site Shared Analytics Information and Computing

@article{Hook2021MultiRobotOS,
  title={Multi-Robot On-site Shared Analytics Information and Computing},
  author={Joshua Vander Hook and Federico Rossi and Tiago Stegun Vaquero and Martina Troesch and Marc Sanchez Net and Joshua Schoolcraft and Jean-Pierre de la Croix and Steve A. Chien},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.06879}
}

• J. V. Hook, F. Rossi, +5 authors S. Chien
• Published 13 December 2021
• Computer Science
• ArXiv

Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or internet may be nonexistent. In this paper we introduce a communicationaware, computation task scheduling problem for multi-robot systems and propose an integer linear program (ILP) that optimizes the allocation of computational tasks across a network of heterogeneous robots, accounting for the networked robots’ computational capabilities and for available (and possibly timevarying) communication links. We consider scheduling of a set of inter-dependent required and optional tasks modeled by a dependency graph. We present a consensus-backed scheduling architecture for shared-world, distributed systems. We validate the ILP formulation and the distributed implementation in different computation platforms and in simulated scenarios with a bias towards lunar or planetary exploration scenarios. Our results show that the proposed implementation can optimize schedules to allow a threefold increase the amount of rewarding tasks performed (e.g., science measurements) compared to an analogous system with no computational load-sharing.