ALIAS (Aircrew Labor In-cockpit Automation System) is a concept development program to insert new automation into existing multi-crew aircraft to enable operations with reduced onboard crew. The objective of the program is to create a portable and extensible hardware and software toolbox introducing of new levels of automation across a wide variety of military and civilian aircraft that ultimately reduce crew requirements.
Aurora’s ALIAS concept utilizes minimally invasive robotic manipulation and machine vision to actuate aircraft controls and perceive aircraft instruments. A key program element is the ability to rapidly train the system and transition to a new aircraft type in less than one month, which requires acquiring knowledge of not only aircraft flight dynamics but also of aircraft procedures and general airmanship. Additionally, Aurora is developing an intuitive in-cockpit user interface to enable the pilot to coordinate tasks and collaborate with the ALIAS system, which is central to the development of a cooperative automation system.
The ability to reassign cockpit roles, allowing humans to perform tasks best suited to humans and automation to perform tasks best suited to automation, would potentially increase the efficiency of current flight operations. Successful introduction of such a system would help improve pilot performance and reduce individual workload, while also providing significant cost savings in the form of simplified training and lower crew costs.
Capabilities & Benefits
- Platform can operate a variety of manned aircraft (military and civilian) with minimal (one month) transition period between aircraft types
- Minimally-invasive installation supports transition back to standard configuration and airworthiness
- Ability to learn aircraft procedures and visually gather information without requiring access to aircraft avionics
- Monitors procedure completion, aircraft health, and other flight parameters and provides intelligent recommendations to the pilot
- Potentially improves pilot performance while simultaneously reducing individual workload