Modeling human-automation task allocations in lunar landing

Designing a complex vehicle or system for use in human space exploration involves analyzing the dynamic interactions between the crew and the vehicle automation over the course of a mission. Modeling and simulating the varying task allocations between the human and the automation, as well as their individual capabilities, provides the ability to analyze their effects on mission and system performance. Piloted lunar landing was used as the case scenario. A task analysis was performed on the landing phases of Apollo and candidate Autonomous Landing and Hazard Avoidance Technology (ALHAT) trajectories. A closed loop pilot-vehicle simulation was developed which includes models of automation performance and initial abstract representations of human perception, attention, decision making, and action. Simulation results yielded predictions for overall task allocations that optimize system performance along metrics such as fuel usage and landing accuracy, as well as for the sensitivity of such metrics to the allocations of individual tasks.