Interaction-Based Active Perception Method and Vibration-Audio-Visual Information Fusion for Asteroid Surface Material Identification

Asteroid exploration is challenging due to its unknown surface material and microgravity. This article proposes an interaction-based active perception (IBAP) framework and a vibration-audio-visual information fusion (VAVIF) method for asteroid surface material identification. First, a robotic sensor...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2024, Vol.73, p.1-14
Main Authors: Zhang, Jun, Xiao, Yi, Ding, Yizhuang, Chen, Liuchen, Song, Aiguo
Format: Article
Language:English
Subjects:
Asteroid missions
Audio data
Audio signals
Data integration
Image classification
Machine learning
Microgravity
Recognition
Robot sensors
Space exploration
Vibration
Vibration perception
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Asteroid exploration is challenging due to its unknown surface material and microgravity. This article proposes an interaction-based active perception (IBAP) framework and a vibration-audio-visual information fusion (VAVIF) method for asteroid surface material identification. First, a robotic sensor node was designed to acquire vibration and audio signals when impacting the surface. With the help of the “interaction” between the node and surface material, a camera records visual images of the material splashing. Then, we proposed a SimpleCNN (SCNN) model to explore the rich features of the fusion images converted from the vibration signals for material identification. We also proposed a bidirectional RNN with attention mechanism (AB-RNN) model to fuse the high- and low-frequency vibration signals to improve the recognition performance. Results showed that SCNN and AB-RNN have better performance than the state-of-the-art models. In addition, state-of-the-art machine learning models are used to classify the visual images and audio signals for identification. Moreover, we utilized the output of multiple classifiers to construct a new dataset and employed ensemble learning (EL) for training and testing. The EL-based VAVIF obtained a recognition accuracy of 99.7%, higher than any individual learner. The results indicate that IBAP and VAVIF make material identification more accurate and robust, which can improve the success rate of an asteroid exploration mission.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3351245