Scale-Invariant Specifications for Human-Swarm Systems

TL;DR

This paper introduces a scale-invariant, spectral decomposition-based control method for human-swarm systems, enabling real-time, persistent, and adaptive management of large and varying agent populations using decentralized ergodic control.

Contribution

It presents a novel spectral/ergodic control approach that guarantees scale invariance and persistence in human-swarm interaction, validated through field tests and simulations.

Findings

Control method is scale-invariant with respect to agent number

Operator specifications remain active until changed

System demonstrates graceful degradation as agents are disabled or added

Abstract

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.