| dc.description.abstract | Robots are increasingly essential in various fields, excelling in tasks from routine operations
to hazardous situations. Enhancing robots with human-like capabilities, such as tactile
sensing, broadens their potential applications. Tactile sensors enable robots to perceive and
interact with their environment similarly to humans. This research focuses on leveraging
tactile sensors to classify textures on uneven surfaces, an area previously unexplored in the
literature. By collecting data points along predefined paths on object surfaces, we minimized
assumptions about the object’s geometry, making the system more flexible and adaptable.
These data points guided the robot’s trajectory, during which tactile data were systematically
gathered on the surface of uneven objects, marking a pioneering effort in this area.
To improve texture classification and reduce processing time, we employed a sliding
window approach, segmenting the dataset into smaller overlapping windows for multi-scale
analysis. In addition to data from uneven surfaces, we supplemented our dataset with tactile
data from even surfaces from another study. We applied advanced deep learning models,
including convolutional neural networks (1D CNN), recurrent neural networks (bidirectional
LSTM), and hybrid architectures, to classify tactile textures using time-series data. The
models achieved average accuracy, precision, and recall rates of 92.3%, 92.4%, and 92.3% for
uneven surfaces, and 96.9%, 97.0%, and 97.0% for even surfaces.
This study demonstrates the importance of tactile sensing in robotic systems, particularly
for texture classification on uneven surfaces. By incorporating MARG and barometer sensors
into the Open Manipulator X, this research advances tactile perception in robotics, equipping
robots to interact more effectively with diverse environments. The findings set the stage for
future applications where precise tactile perception is essential. | en_US |
