NASA Human Research Program Investigators' Workshop (2012)
4139.pdf
MIDAS-FAST: Development and Validation of a Model-Based Tool to Predict Human and System
Performance in Robotic Missions
A. Sebok1, C. D. Wickens1, M. Gacy1, M. Brehon1, N. Sarter2, H. Li2, B. Gore3, B. Hooey3
1Alion Science and Technology, MA&D Operation, 4949 Pearl East Circle, Suite 200, Boulder, CO 80301,
2University of Michigan, Department of Industrial and Operations Engineering, Center for Ergonomics,
1205 Beal Ave., Ann Arbor, MI 48109-2117, and
3 San Jose State University, NASA Ames Research Center, Human Factors Research Division, MS 262-12, Moffett
Field, CA 94035-1000.
ABSTRACT
In exploration missions, NASA relies on human-controlled robotic systems to perform various operations in the
hostile environment of space. Robotic manipulations are typically complex, and require extensive training and
careful planning before operators are sufficiently prepared to perform the tasks with the necessary degree of
accuracy. Mistakes, such as steering the robotic arm into structure, can have disastrous, potentially life threatening,
consequences. One approach to improving the usability of robotic systems is to automate aspects of the task; this
could include automating the trajectory and collision avoidance calculations, so operators specify where the arm is
to fly, and the automation plans the trajectory and moves the arm to the appropriate location. While this appears to
be an obvious improvement, there is still potential for problems. For example, the automation can fail to take into
account an obstruction (e.g., space junk) that was not accounted for in trajectory calculations. As automation is
introduced to a system, it changes the role of the operator from a controller to that of a monitor or supervisor. This
change in role can result in performance degradations, where the operator loses awareness and manual skills.
This paper describes the status of a NASA Human Research Program project, MIDAS-FAST (the Man-machine
Integration Design and Analysis System – Function Allocation Simulation Tool), to develop and empirically validate
a model-based tool to help automation system designers and mission planners predict the effects of different
function allocation strategies on human and system performance in robotic missions. MIDAS-FAST includes
models of operator performance, in particular, operator visual attention, difficulties due to sub-optimal camera views
(e.g., in which robotic arm movement is parallel to the line of sight) and control reversals (e.g., where control
movements are in one dimension and corresponding arm movements are in another), and decision making as a result
of tasks, views, occurrences, and experience. It also includes a robotic simulation, the Basic Operational Robotic
Instruction System (BORIS), to provide the kinematics and visualization of robotic arm movements. MIDAS-FAST
is being designed to allow users to specify automation conditions of interest, including the type of function
allocation strategies being implemented, the layout of the environment in which the robotic is used, and the location
of obstructions. When the conditions of interest are specified, users run the model to generate predictions of
performance, including: 1) time to complete tasks and mission, 2) errors made, 3) workload, 4) operator camera
selections compared with optimal camera recommendations, and 5) focus of visual attention throughout the
scenario.
To date, human performance models have been developed for a set of scenarios (specified automation conditions),
and an experimental study is being prepared to evaluate human performance in those same conditions. The paper
will present the status of the project, including results of the model predictions, empirical findings, and plans for
improving the model.