Flexible Piezoelectric Tactile Sensing System for Intelligent Object Recognition
Overview
This research presents a flexible, distributed piezoelectric tactile sensing system designed for early-stage object recognition during robotic grasping.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
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Reported quantity · recognition latency: 101 other · Basis not reported
“samples), was deployed on an embedded edge platform, achieving an end-to-end recognition latency of approximately 101 ms from contact onset and an energy consumption of 63.8 microjoules per inference. These results demonstrate the potential of distributed piezoelectric tactile sensing for accurate, energy-efficient, and real-time object recognition during robotic grasping.”
Exact source · revision 1Source owner not reported
Reported quantity · energy consumption: 63.8 other · Basis not reported
“samples), was deployed on an embedded edge platform, achieving an end-to-end recognition latency of approximately 101 ms from contact onset and an energy consumption of 63.8 microjoules per inference. These results demonstrate the potential of distributed piezoelectric tactile sensing for accurate, energy-efficient, and real-time object recognition during robotic grasping.”
Exact source · revision 1Source owner not reported
Reported quantity · F1-score: 0.9796 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
Reported quantity · F1-score: 0.9846 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
Reported quantity · F1-score: 0.9903 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
Reported quantity · sensor count: 16 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
Reported quantity · sensor count: 24 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
Reported quantity · sensor count: 32 other · Basis not reported · Differing source assertions
“ion accuracy and real-time feasibility were systematically investigated. Increasing the number of sensing elements improved recognition performance across all approaches. The best-performing method, based on spatiotemporal heatmaps and a 2D-CNN, achieved F1-scores of 0.9796, 0.9846, and 0.9903 using 16, 24, and 32 sensors, respectively. The optimal configuration, comprising 32 sensors and a 100-ms temporal window (50”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalFlexible Piezoelectric Tactile Sensing System for Intelligent Object Recognition
This research presents a flexible, distributed piezoelectric tactile sensing system designed for early-stage object recognition during robotic grasping.
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