As part of the Crew-Automated and integration Testbed (CAT) Advanced Technology Objective (ATO), the US Army
Tank-automotive and Armaments Research, Development, and Engineering Center (TARDEC) developed crew stations
that provided soldiers the ability to control both manned and unmanned vehicles. The crew stations were designed to
optimize soldier workload and provide the ability to conduct mission planning, route planning, reconnaissance,
surveillance, and target acquisition (RSTA), and fire control capabilities. The crew station software is fully
configurable, portable (between crew stations), and interoperable with one another. However, the software architecture
was optimized for the specific computing platform utilized by each crew station and user interfaces were hard coded.
Current CAT crew station capabilities are required to execute on other crew station configurations as well as handheld
devices to meet the needs of expanded soldier roles, including dismounted infantry. TARDEC is currently exploring
ways to develop a scalable software architecture that is able to adapt to the physical characteristics of differing
computing platforms and devices. In addition, based upon a soldier's role, the software must be able to adapt and
optimize the displays based upon individual soldier needs. And finally, the software must be capable of applying a
unique style to the presentation of information to the soldier. Future programs require more robust software
architectures that take these requirements into account. This paper will describe how scalable software architectures can
be designed to address each of these unique requirements.
This presentation will provide program information, goals and objectives of the Technology for Human-Robot Interactions in Soldier-Robot Teaming (HRI) Army Technology Objective (ATO). The intent of this program is to develop and demonstrate an intelligent scaleable interface for mounted and dismounted control of ground and air unmanned systems. Currently in the Army there are unique interfaces developed by engineers for each unmanned system fielded. This saddles the soldier with a training burden to learn specific interface operations prior to controlling the robot. By providing a consistent look and feel across various sized controlling devices, the training burden is reduced as well as the soldier's cognitive workload. Additionally, task analysis will be performed to identify workload barriers and bottlenecks, and intelligent agents will be developed and applied to reduce and/or automate the higher workload tasks. Lastly, this program will develop adaptive automation techniques to intelligently shed or introduce tasks at the appropriate time to the soldier to maintain optimal situational awareness and maximize the performance of the soldier-robot team.