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Human Research Program

Space Human Factors and Habitability MIDAS-FAST: Development and Validation of a Tool to Support Function Allocation

Completed Technology Project

Project Introduction

In this project, the research team 1) developed and validated a model- and simulation-based tool to allow researchers to evaluate various function allocation strategies in space robotics missions and 2) conducted empirical research to investigate human-automation interaction (HAI). The purpose of this tool is to allow human performance researchers and system designers to evaluate potential HAI systems early in the design process. The tool leverages the Man-Machine Integration Design and Analysis System (MIDAS, developed for NASA Ames), and the Basic Operational Robotics Instructional System (BORIS, a NASA Johnson Space Center (JSC) training simulation) to provide MIDAS-FAST (Function Allocation Simulation Tool).

The research proceeded along five partially parallel tracks: (1) developing the function allocation tradeoff model, (2) carrying out empirical human in the loop (HITL) research, (3) developing and (4) validating a computational model of the robotics operator, and (5) implementing the model in the context of the MIDAS-FAST tool. These five major components will be described separately:

1. Function allocation model. A key aspect of function allocation between human and automation is the degree of automation: that is, the relative amount of perceptual, cognitive, and motor “work” carried out by the automation versus human in their collaborative effort in completing task goals. A taxonomy of stages and levels of automation developed by Parasuraman, Sheridan, and Wickens (2000, 2008) describes this degree of automation. One of the important components of the degree of automation is the stage of task information processing at which automation operates to support or replace human activity. Earlier stages involve information acquisition and integration to support situation assessment. Later stages involve action selection and implementation to support task completion. The function allocation tradeoff model underlying FAST proposes that later stages of automation better support routine human-system performance and lower human workload. However these later stages become more problematic if automation fails to perform its functions appropriately, a failure caused in part by the loss of situation awareness. Our review of the literature on human-automation interaction, incorporated into a meta-analysis, supported these tradeoff relationships with statistically significant trends; and the guidance from this FAST tradeoff model have been incorporated into the MIDAS FAST tool (see Items 4 and 5 below).

Using this model, we identified several different types of automation to include in the robotic simulation. These required modifications to the existing BORIS software. Trajectory control automation was implemented in one of three degrees: manual, guided, and automated. To help ensure consistency in experimental participant behavior, we developed 3-segment trajectories that crossed a table (an obstruction) in the operating environment. The first and third segments required movement in 1 axis only; the second segment required movement along 2 axes. Manual trajectories were performed without guidance being given to participants. They were informed of the trajectory to follow, but they were required to determine how to implement it, and to move the arm using the hand controllers. In the automation guided condition, participants were shown a trajectory (or “flight path”) to follow. In the autocontrol condition, the trajectory was shown, and the automation executed the trajectory. Hazard alerting and hazard avoidance automation were identified and included. Hazard alerting included color coding to indicate to participants when they had encountered a no-fly zone; hazard avoidance included the color coding as well as stopping the arm to prevent a collision. Camera recommendation logic was also developed. Manual camera control required the operator to make decisions about camera selection, whereas the camera recommendation automation provided a visual alert to suggest a camera switch when needed, and recommended which camera to use. These different types of automation allowed us to research different stages and levels of automation as identified by our framework.

2. Three empirical studies were performed to investigate human performance with different types of robotic arm system automation. The first experiment examined different interface designs, including enhanced (over the current BORIS simulation) graphics for presenting hazards, integrated graphical hazard alerting, and tactile alerting. The second experiment - used for model parameterization and validation - matched the modeling conditions, and examined human performance in conditions with different degrees of automation and with unreliable automation. The third experiment (also used for model validation efforts) investigated adaptive versus adaptable versus fixed automation.

3. The team developed human performance models of scenarios of interest, based on robotic arm task analyses performed in cooperation with subject matter experts (SMEs). The team verified the task analyses by talk-through sessions with SMEs. Human performance model and human-automation interaction predictions were validated in empirical, Human in the loop (HITL) studies identified in Item 2. Results of the validations were used to refine the models. The models included sub models (also referred to as modules) to predict operator visual scanning, operator performance decrements due to poor camera views, and operator decision making. The scanning model is based on SEEV (Salience, Expectancy, Effort and Value) and the performance impacts of camera view quality were predicted using FORT (Frame of Reference Transformation). SEEV and FORT are relatively mature models, having been developed, refined, and validated under previous NASA research efforts. The decision model was developed specifically for tasks associated with the robotic arm, based on the Generic Robotics Training.

4. A primary goal of this research was to verify and validate our model of the robotic arm operator, to be employed in the function allocation tool, and to collect data that would further validate the Function Allocation Support Tool tradeoff model. To accomplish these purposes, data from the Human in the loop (HITL) Experiments 2 and 3 were analyzed, and both models developed and refined.

5. One particular focus of the project was on developing the MIDAS-FAST tool, a prototype model- and simulation-based product that is both usable and useful for researchers, allowing them to easily modify robotic arm scenarios and evaluate different potential automation conditions. This tool offers data entry screens that guide the user through the process of building a scenario. It allows the researchers to specify numerous relevant factors, e.g., operators, tasks, environmental conditions, and function allocation strategy. It offers a visualization capability that provides an animation of the scenario, showing operators interacting with the simulation. The output of the model run includes, in addition to the animation, data files with parameters of interest such as predicted operator situation awareness, workload, visual scanning, camera selection, and trajectory control.

In summary, the MIDAS-FAST project provided a validated model- and simulation based tool for predicting operator performance when working with a robotic arm in different function allocation situations. The function allocation model developed, and the empirical research conducted in this effort were used to identify conditions and provide data development of the tool.

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