Abstract
Swarm systems consist of large numbers of agents that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from urban search and rescue situations to cyber defence. However, the successful deployment of the swarm in such applications is conditioned by the effective coupling between human and swarm. While adaptive autonomy promises to provide enhanced performance in human-machine interaction, distinct factors must be considered for its implementation within human-swarm interaction. This paper reviews the multidisciplinary literature on different aspects contributing to the facilitation of adaptive autonomy in human-swarm interaction. Specifically, five aspects that are necessary for an adaptive agent to operate properly are considered and discussed, including mission objectives, interaction, mission complexity, automation levels, and human states. We distill the corresponding indicators in each of the five aspects, and propose a framework, named MICAH (i.e., Mission-Interaction-Complexity-Automation-Human), which maps the primitive state indicators needed for adaptive human-swarm teaming.
Highlights
Recent technological advancements have enabled the realization of swarm systems that can include large numbers of robots
The aim of this paper is to identify and fuse together different categories of indicators needed for adaptive autonomy in HSI, and to study how the state of each component can be quantified using synthesized indicators from the literature
These metrics were the number of localized victims, the number of found obstacles, the number of packages supplied to victims, and quality of communication with humans
Summary
Recent technological advancements have enabled the realization of swarm systems that can include large numbers of robots. Humans and the swarm need to coordinate their actions throughout the mission to maintain acceptable levels of workload while ensuring that the tasks are performed effectively. This coordination can be assigned to the human or to a coordinating agent. Adaptive autonomy has demonstrated its ability to enhance the performance of the overall human-machine interaction and mission (Chen and Barnes, 2014) This enhancement is attributed to its ability to reconcile conflicting requirements within the interaction (e.g., to make best use of the automation while ensuring that the human does not lose situational awareness or his/her level of engagement).
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