Human-inspired Motion Planning for Omni-directional Social Robots

Abstract

Omni-directional robots have gradually been popular for social interactions with people in human environments. The characteristics of omni-directional bases allow the robots to change their body orientation freely while moving straight. However, human spectators show dislike when observing robots behave unnaturally. In this paper, we observed how humans naturally move to goals and then developed a motion planning algorithm for omni-directional robots to resemble human movements in a time-efficient manner. Instead of treating the translation and rotation of a robot separately, the proposed motion planner couples the two motions with constraints inspired from the observation of human behaviors. We implemented the proposed method onto an omni-directional robot and conducted navigation experiments in a shop with shelves and narrow corridors at width of 90cm. Results from a within-participants study of 300 human spectators validated that the proposed human-inspired motion planner provided people with more natural and predictable feelings compared to the common rotate-while-move or rotate-then-move strategies.