3 research outputs found
Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception
Industrial
robots are the main piece of equipment of
intelligent
manufacturing, and array-type tactile sensors are considered to be
the core devices for their active sensing and understanding of the
production environment. A great challenge for existing array-type
tactile sensors is the wiring of sensing units in a limited area,
the contradiction between a small number of sensing units and high
resolution, and the deviation of the overall output pattern due to
the difference in the performance of each sensing unit itself. Inspired
by the human somatosensory processing hierarchy, we combine tactile
sensors with artificial intelligence algorithms to simplify the sensor
architecture while achieving tactile resolution capabilities far greater
than the number of signal channels. The prepared 8-electrode carbon-based
conductive network achieves high-precision identification of 32 regions
with 97% classification accuracy assisted by a quadratic discriminant
analysis algorithm. Notably, the output of the sensor remains unchanged
after 13,000 cycles at 60 kPa, indicating its excellent durability
performance. Moreover, the large-area skin-like continuous conductive
network is simple to fabricate, cost-effective, and can be easily
scaled up/down depending on the application. This work may address
the increasing need for simple fabrication, rapid integration, and
adaptable geometry tactile sensors for use in industrial robots
Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception
Industrial
robots are the main piece of equipment of
intelligent
manufacturing, and array-type tactile sensors are considered to be
the core devices for their active sensing and understanding of the
production environment. A great challenge for existing array-type
tactile sensors is the wiring of sensing units in a limited area,
the contradiction between a small number of sensing units and high
resolution, and the deviation of the overall output pattern due to
the difference in the performance of each sensing unit itself. Inspired
by the human somatosensory processing hierarchy, we combine tactile
sensors with artificial intelligence algorithms to simplify the sensor
architecture while achieving tactile resolution capabilities far greater
than the number of signal channels. The prepared 8-electrode carbon-based
conductive network achieves high-precision identification of 32 regions
with 97% classification accuracy assisted by a quadratic discriminant
analysis algorithm. Notably, the output of the sensor remains unchanged
after 13,000 cycles at 60 kPa, indicating its excellent durability
performance. Moreover, the large-area skin-like continuous conductive
network is simple to fabricate, cost-effective, and can be easily
scaled up/down depending on the application. This work may address
the increasing need for simple fabrication, rapid integration, and
adaptable geometry tactile sensors for use in industrial robots
Improving the Resolution of Flexible Large-Area Tactile Sensors through Machine-Learning Perception
Industrial
robots are the main piece of equipment of
intelligent
manufacturing, and array-type tactile sensors are considered to be
the core devices for their active sensing and understanding of the
production environment. A great challenge for existing array-type
tactile sensors is the wiring of sensing units in a limited area,
the contradiction between a small number of sensing units and high
resolution, and the deviation of the overall output pattern due to
the difference in the performance of each sensing unit itself. Inspired
by the human somatosensory processing hierarchy, we combine tactile
sensors with artificial intelligence algorithms to simplify the sensor
architecture while achieving tactile resolution capabilities far greater
than the number of signal channels. The prepared 8-electrode carbon-based
conductive network achieves high-precision identification of 32 regions
with 97% classification accuracy assisted by a quadratic discriminant
analysis algorithm. Notably, the output of the sensor remains unchanged
after 13,000 cycles at 60 kPa, indicating its excellent durability
performance. Moreover, the large-area skin-like continuous conductive
network is simple to fabricate, cost-effective, and can be easily
scaled up/down depending on the application. This work may address
the increasing need for simple fabrication, rapid integration, and
adaptable geometry tactile sensors for use in industrial robots