Character Design and Validation on Aerial Robotic Platforms Using Laban Movement Analysis

Abstract

Exploring the application of aerial robots in human-robot interaction is a currently active area of research. One step toward achieving more natural human-robot interactions is developing a method in which an aerial robot successfully portrays a character or exhibits character traits that a human can recognize. Recognizable character types are conveyed through movement in many performing arts, including ballet. However, past work has not leveraged the movement expertise of ballet dancers to create a method and portray complex characters on low-degree-of-freedom aerial robots. This paper explores the recognition and differentiation of archetypal characters used in classical ballet using Laban Movement Analysis (LMA) and applies the results from tracking the movements to an aerial robotic platform. Movement sequences were created on a Bebop Drone to emulate these character types. This process was subsequently validated by a user study, highlighting the successful application of state recognition through movement on aerial robots. Such work can be used to create robots with recognizable movement signatures for quick identification by human counterparts in social settings.