Multi-Robot On-site Shared Analytics Information and Computing
Joshua Vander Hook, Federico Rossi, Tiago Vaquero, Martina, Troesch, Marc Sanchez Net, Joshua Schoolcraft, Jean-Pierre de la, Croix, Steve Chien

TL;DR
This paper presents a communication-aware scheduling method for multi-robot systems that optimizes computational load sharing, significantly increasing task completion efficiency in environments with limited connectivity.
Contribution
It introduces an ILP-based scheduling framework for heterogeneous robots that accounts for communication constraints and dependency graphs, enabling efficient load sharing.
Findings
Achieves a threefold increase in task completion compared to systems without load sharing.
Validates the ILP and distributed implementation in simulated lunar/planetary scenarios.
Demonstrates improved efficiency in extreme, communication-limited environments.
Abstract
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 communication-aware, 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 time-varying) 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…
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Taxonomy
TopicsDistributed systems and fault tolerance · IoT and Edge/Fog Computing · Distributed and Parallel Computing Systems
