Autonomy Balancing in a Manned-Unmanned Teaming (MUT) Swarm Attack

Abstract

In this paper, we describe a framework for developing an interactive feedback model of manned-unmanned teaming (MUT) operational mode selections for a broad spectrum of unmanned vehicle (UV) autonomy levels. Though the highest autonomy levels are within reach as technology continues to advance, lower level autonomy or human manual control will still be needed depending on mission scenarios and dynamic situations. Understanding when and how we change the autonomy level of MUT is critical to ensure system safety and to maximize system performance. Thus, we propose to integrate feedback from various human state variables (i.e., physiological and behavioral signals such as heart rate, skin conductance level, and postures) for estimating human workload and interest level and key task performance measures (accuracy and speed for assigned missions, task interaction) into MUT systems so that the MUT adapts its mode automatically as needed. We developed RESCHU-SA (Research Environment for Supervisory Control of Heterogeneous Unmanned Vehicles Swarm Attacks), a modified version of the RESCHU simulator originally developed at MIT. We designed a human-in-the-loop experiment to collect baseline data for varying levels of autonomy using the RESCHU-SA along with a physiological sensor BioHarness. Different levels of autonomy include 1) high level autonomy using an auction algorithm or nearest-neighbor assignment algorithm, 2) low level autonomy using manual assignment, and 3) interactive autonomy which allows operators to change between high and low autonomy level. The purpose of the research is to investigate the level of autonomy that should be given to unmanned vehicles (UVs) to successfully complete a mission using a MUT in a swarm attack scenario.