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Effective human-robot interaction is increasingly vital across various domains, including assistive robotics, emotional communication, entertainment, and industrial automation. Visual feedback, a common feature of current interfaces, may not be suitable for all environments. Audio feedback serves as a critical supplementary communication layer in settings where visibility is low or where robotic operations generate extensive data. Sonification, which transforms a robot's trajectory, motion, and environmental signals into sound, enhances users' comprehension of robot behavior. This improvement in understanding fosters more effective, safe, and reliable Human-Robot Interaction (HRI). Demonstrations of auditory data sonification's benefits are evident in real-world applications such as industrial assembly, robot-assisted rehabilitation, and interactive robotic exhibitions, where it promotes cooperation, boosts performance, and heightens engagement. Beyond conventional HRI environments, auditory data sonification shows substantial potential in managing complex robotic systems and intricate structures, such as hyper-redundant robots and robotic teams. These systems often challenge operators with complex joint monitoring, mathematical kinematic modeling, and visual behavior verification. This dissertation explores the sonification of motion in hyper-redundant robots and teams of industrial robots. It delves into the Wave Space Sonification (WSS) framework developed by Hermann, applying it to the motion datasets of protein molecules modeled as hyper-redundant mechanisms with numerous rigid nano-linkages. This research leverages the WSS framework to develop a sonification methodology for protein molecules' dihedral angle folding trajectories. Furthermore, it introduces a novel approach for the systematic sonification of robotic motion across varying configurations. By employing localized wave fields oriented within the robots' configuration space, this methodology generates auditory outputs with specific timbral qualities as robots move through predefined configurations or along certain trajectories. Additionally, the dissertation examines a team of wheeled industrial/service robots whose motion patterns are sonified using sinusoidal vibratory sounds, demonstrating the practical applications and benefits of this innovative approach.
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Sonification of Motion of Unmanned Aerial Vehicles
The development of Unmanned Aerial Vehicles (UAVs) can be enhanced through the use of sonification, an emerging field within Human-Robot Interaction (HRI). This dissertation introduces UAVSonification, a computational algorithm that maps simulation data to musical notes using the Musical Instrument Digital Interface (MIDI). By integrating UAVSonification with Formation Flight Simulation in Simulink, this study explores the sonification of UAV trajectories under environmental conditions. The function transforms simulation data into auditory signals, allowing users to discern key dynamics through sound. Specifically, the data series for multiple UAVs is mapped to piano notes via MIDI on MATLAB, providing auditory insights into UAV trajectories, environmental conditions, and control errors. A well-controlled flight path and stable heading controller produce harmonious sounds, while disruptions and deviations result in dissonance. UAVSonification offers a unique auditory approach to understanding UAV behavior in relation to control dynamics and environmental conditions. The sonification of UAVs has the potential to aid in the planning and analysis of UAV trajectories and controllers, as well as in creative endeavors. The effectiveness of the proposed method is shown through MATLAB numerical simulations.
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- Award ID(s):
- 2153744
- PAR ID:
- 10616687
- Publisher / Repository:
- University of Michigan
- Date Published:
- Subject(s) / Keyword(s):
- unmanned aerial vehicles uav development sonification human-drone interaction human-robot interaction controller
- Format(s):
- Medium: X
- Institution:
- University of Michigan
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Dissertation:
- https://doi.org/10.7302/25819
