Design and Evaluation of Path Planning Decision Support for Planetary Surface Exploration

Abstract

Human intent is an integral part of real-time path planning and re-planning, thus any decision aiding system must support human-automation interaction. The appropriate balance between humans and automation for this task has previously not been adequately studied. In order to better understand task allocation and collaboration between humans and automation for geospatial path problem solving, a prototype path planning aid was developed and tested. The focus was human planetary surface exploration, a high risk, time-critical domain, but the scenario is representative of any domain where humans path plan across uncertain terrain. Three visualizations, including elevation contour maps, a novel visualization called levels of equal costs, and a combination of the two were tested along with two levels of automation. When participants received the lower level of automation assistance, their path costs errors were less than 35% of the optimal, and they integrated manual sensitivity analysis strategies. When participants used the higher level of automation assistance, path costs errors were reduced to a few percentages, and they saved on average 1.5 minutes in the task. However, this increased performance came at the price of decreased situation awareness and automation bias.