Semi-autonomous robot teams are currently being proposed for dangerous missions including weapons employment, minefield operations, reconnaissance/surveillance, force protection, ordnance detection/clearance, and urban warfare. Experimental ground robots such as the Dragon require one operator per robot while more sophisticated UAVs require multiple operators due to the concurrent demands of control, monitoring and decision-making. Kansas State University, together with Vanderbilt University, have been working on key technologies to overcoming this problem. We currently have one funded projects in this area.
Most deployed robots are currently operated by humans using teleoperation. However, future DoD concepts envision collaborative human-robot teams (HRTs) where humans and robots are deployed side-by-side as partners on missions that require tightly integrated and choreographed activities. These HRTs will require the team members to adapt to each other, the environment, and the state of the team problem solving process.
The key to HRT adaptation is providing teams with the knowledge of how team memberís performance capabilities change over time. Robot capabilities are complex, but are not as difficult to capture and quantify as those of humans. Thus, knowledge human performance factors and how they affect performance is vital to HRT adaptation. This proposal seeks to capture, model, and use human performance information to help HRTs operate at peak efficiency and effectiveness.
This research will employ existing human performance modeling tools to identify the applicability of existing HPMFs to HRTs. However, it is anticipated that existing HPMFs will need modification and new HPMFs may be required. The resulting HPMFs will be used to guide runtime HRT control software in assigning team members to mission roles. The research begins with single human-single robot teams, advances to single human-multiple robot teams, and culminates with an HRT demonstration with humans and real robots that assigns members to roles using HPMFs.
The goal of this research is to develop a theory of human-robot team organization that integrates humans and robots into a single team based on member capabilities and human performance factors.
This research will provide a common framework for incorporating humans and robots into a single team. The impact will be a clearer understanding of the applicability of HPMFs for informing the teaming of humans and robots for tightly coupled missions.
Single Platform Multi-Sensor (Robot) Control