Dynamic Path Visualization for Human-Robot Collaboration
Andre Cleaver, Darren Vincent Tang, Victoria Chen, Elaine Schaertl Short, Jivko Sinapov
Abstract
Augmented reality technology can enable robots to visualize their future actions giving users crucial information to avoid collisions and other conflicting actions. Although a robot's entire action plan could be visualized (such as the output of a navigational planner), how far into the future it is appropriate to display the robot's plan is unknown. We developed a dynamic path visualizer that projects the robot's motion intent at varying lengths depending on the complexity of the upcoming path. We tested our approach in a virtual game where participants were tasked to collect and deliver gems to a robot that moves randomly towards a grid of markers in a confined area. Preliminary results on a small sample size indicate no significant effect on task performance; however, open-ended responses reveal participants preference towards visuals that show longer path projections.