Human-automated path planning optimization and decision support

Path planning is a problem encountered in multiple domains, including unmanned vehicle control, air traffic control, and future exploration missions to the Moon and Mars. Due to the voluminous and complex nature of the data, path planning in such demanding environments requires the use of automated planners. In order to better understand how to support human operators in the task of path planning with computer aids, an experiment was conducted with a prototype path planner under various conditions to assess the effect on operator performance. Participants were asked to create and optimize paths based on increasingly complex path cost functions, using different map visualizations including a novel visualization based on a numerical potential field algorithm. They also planned paths under degraded automation conditions. Participants exhibited two types of analysis strategies, which were global path regeneration and local sensitivity analysis. No main effect due to visualization was detected, but results indicated that the type of optimizing cost function affected performance, as measured by metabolic costs, sun position, path distance, and task time. Unexpectedly, participants were able to better optimize more complex cost functions as compared to a simple time-based cost function.