A Framework for Dynamic Situational Awareness in Human Robot Teams: An Interview Study

TL;DR

This study develops a framework for understanding the dynamic nature of human situational awareness in human-robot teams, emphasizing its variability and the importance of adaptive interfaces for effective collaboration.

Contribution

It introduces a novel framework based on interview data that explains how situational awareness varies and identifies strategies to maintain it in human-robot teams.

Findings

Situational awareness needs vary with environmental and task conditions.

Gaps in awareness can lead to inefficiencies and team failures.

Strategies exist to help maintain appropriate awareness levels.

Abstract

In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.