Multi-Robot On-site Shared Analytics Information and Computing

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 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.