Formulating layered adjustable autonomy for unmanned aerial vehicles

The purpose of this paper is to propose a layered adjustable autonomy (LAA) as a dynamically adjustable autonomy model for a multi-agent system. It is mainly used to efficiently manage humans’ and agents’ shared control of autonomous systems and maintain humans’ global control over the agents.

The authors apply the LAA model in an agent-based autonomous unmanned aerial vehicle (UAV) system. The UAV system implementation consists of two parts: software and hardware. The software part represents the controller and the cognitive, and the hardware represents the computing machinery and the actuator of the UAV system. The UAV system performs three experimental scenarios of dance, surveillance and search missions. The selected scenarios demonstrate different behaviors in order to create a suitable test plan and ensure significant results.

The results of the UAV system tests prove that segregating the autonomy of a system as multi-dimensional and adjustable layers enables humans and/or agents to perform actions at convenient autonomy levels. Hence, reducing the adjustable autonomy drawbacks of constraining the autonomy of the agents, increasing humans’ workload and exposing the system to disturbances.

The application of the LAA model in a UAV manifests the significance of implementing dynamic adjustable autonomy. Assessing the autonomy within three phases of agents run cycle (task-selection, actions-selection and actions-execution) is an original idea that aims to direct agents’ autonomy toward performance competency. The agents’ abilities are well exploited when an incompetent agent switches with a more competent one.