การจดจำวัตถุจากการสัมผัสของหุ่นยนต์เสมือนมนุษย์ด้วยวิธีการประมวลผลภาพจาก การสัมผัสร่วมกับโครงข่ายประสาทเทียม
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ธ.ค. 4, 2018
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เซ็นเซอร์วัดแรงกด ระบบประสาทสัมผัสทางกาย ภาพจากการสัมผัส โครงข่ายประสาทเทียม
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บทคัดย่อ
บทความนี้นำเสนอการพัฒนาระบบสำหรับจดจำวัตถุจากการสัมผัสบริเวณพื้นผิววัตถุโดยใช้ประสาทสัมผัสทางกายเทียม ทั้งนี้งานวิจัยนี้ได้ออกแบบระบบการทดสอบเพื่อเป็นพื้นฐานของมือหุ่นยนต์ที่ติดตั้งระบบเซ็นเซอร์วัดแรงกดแบบอาร์เรย์ ระบบได้รับการออกแบบโดยใช้เซ็นเซอร์สัมผัสขนาด 16x10 พิกเซล เมื่อระบบสัมผัสวัตถุจะทำการจัดเรียงข้อมูลให้เป็นรูปภาพ ภาพที่ได้จะถูกนำมาประมวลผลเพื่ออธิบายลักษณะของวัตถุที่สัมผัสที่แตกต่างกันสำหรับผิวสัมผัสของวัตถุจะสร้างความแตกต่างของแรงกดให้มือหุ่นยนต์ หลังจากนั้นได้จะแยกลักษณะของวัตถุโดยใช้โครงข่ายประสาทเทียม ผลการทดสอบวัตถุ 10 ชนิดแสดงผลการวิเคราะห์ที่ถูกต้องโดยเฉลี่ยสูงสุด 93.2% โดยใช้การหาค่าเฉลี่ยและส่วนเบี่ยงเบนมาตรฐานของแรงกดที่กระทำบนเซ็นเซอร์
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Pohtongkam, S., & Srinonchat, J. (2018). การจดจำวัตถุจากการสัมผัสของหุ่นยนต์เสมือนมนุษย์ด้วยวิธีการประมวลผลภาพจาก การสัมผัสร่วมกับโครงข่ายประสาทเทียม. วิศวกรรมสาร มหาวิทยาลัยนเรศวร, 13(2), 66–79. สืบค้น จาก https://ph01.tci-thaijo.org/index.php/nuej/article/view/100438
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Research Paper
References
[1] Lederman, S. J., & Klatzky, R. L. (2009). Haptic perception: A tutorial. Attention Perception & sychophysics,71,1439-1459. https://dx.doi:10.3758/APP.71.7.1439.
[2] Pressure Mapping: Geotechnical Engineering Retrieved Achieve Design and Process Optimization with Interface Pressure Measurement. (2016). Retrieved from https://www.tekscan.com/news/new-ebook-advancing-geotechnical-engineering-pressure-Mapping.
[3] Micah, K. J., & Edward, H. A. (2009). Retrographic sensing for the measurement of surface texture and shape, Computer Vision and Pattern Recognition 2009 : Proceedings of an IEEE Conference on in Miami, FL, USA, 20-25 June 2009 (pp. 1070–1077). https://dx.doi:10.1109/ CVPR. 2009.5206534.
[4] Casselli, S., Magnanini, C., & Zanichelli, F. (1995). On the robustness of haptic object recognition based on polyhedral shape representations, Intelligent Robots and Systems 95. 'Human Robot Interaction and Cooperative Robots' : Proceedings of an IEEE/RSJ international conference in Pittsburgh, PA, USA, 5-9 Aug. 1995 Vol2 (pp 200–206). https://dx.doi:10.1109/IROS. 1995.526160.
[5] Bajcsy, R. (1984). What can we learn from one finger experiments?. Proceedings of a Symposium on Robotic Research Bretton Woods, NH, 1984, (pp. 509–527).
[6] Ronald, S. F., & Binford, T.O. (1991). Using a Cylindrical Tactile Sensor for Determining Curvature. IEEE Transactions on Robotics and Automation, 7, 806 – 817. https://dx.doi:10.1109/70.105389
[7] Bay, S. J. (1989). Tactile shape sensing via single- and multi fingered hands, Robotics and Automation 1989 : Proceedings of an IEEE international conference in Scottsdale, AZ, USA, 14-19 May 1989 Vol 1 (pp 290-295). https://dx.doi:10.1109/ROBOT.1989.100003
[8] Allen, P. K. & Roberts, K. S. (1989). Haptic object recognition using a multi fingered dexterous hand, Robotics and Automation 1989 : Proceedings of an IEEE international conference in Scottsdale, AZ, USA, 14-19 May 1989 Vol 1 (pp 290-295). https://dx.doi: 10.1109/ROBOT.1989.100011
[9] Alexander, S. Jürgen, S. & Cyrill. S. (2009). Object Identification with Tactile Sensors using Bag-of-Features, Intelligent Robots and Systems 2009 : Proceedings of an IEEE international conference in St. Louis, MO, USA, 10-15 October 2009 (pp 243-248). https://dx.doi:10.1109/IROS. 2009.5354648
[10] Khasnobish, A., Jati, A., Singh, G., Bhattacharyya, S., Konar, A., Tibarewala, D. N., Kim, E. & Nagar, A. K. (2012). Object-Shape Recognition from Tactile Images Using a Feed-forward Neural Network, World Congress on Computational Intelligence : Proceedings of an IEEE international conference in Brisbane, QLD, Australia, 10-15 June 2012. https://dx.doi:10.1109/IJCNN.2012.6252593
[11] Datta, S., Khasnobish, A., Konar, A., Tibarewala, D. N. & Janarthanan, R. (2013). Object Shape and Size Recognition From Tactile images, Control Communication and Computing 2013 : Proceedings of an IEEE international conference in Thiruvananthapuram, India, 13-15 December 2013 (pp 16-21). https://di.doi:10.1109
/ICCC.2013.6731617
[12] Alexander, S., Yusuke, B., Kuniaki, N., Hiroyasu, I., Tetsuya, O. & Shigeki, S. (2014). Object Recognition using Deep Learning and Dropout, Humanoid Robots (Humanoids) 2014 : Proceedings of an IEEE international conference in Madrid, Spain, 18-20 November 2014 (pp 1044-1050). https://di:doi 10.1109/HUMANOIDS.2014. 7041493
[13] Harold, S. & Yiannis, D. (2014). Incrementally Learning Objects by Touch: Online Discriminative and Generative Models for Tactile-Based Recognition. IEEE Transactions On Haptics, 7, 512-525. https://dx.doi:10.1109/TOH.2014.
2326159
[14] Somchai, P. & Jakkree, S. (2016) Object Recognition from Human Tactile Image Using Artificial Neural Network, Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 2016 : Proceedings of an IEEE international conference in Chiang Mai, Thailand, 28 June-1 July 2016. https://dx.doi: 10.1109/ECTICon.2016.7561402
[15] Zachary, P., Erion, P., Caitlin, R. & Gregory, D. H. (2011). Tactile-Object Recognition From Appearance Information. IEEE Transactions On Robotics, 27, 473-487. https://dx.doi:10.1109/TRO.2011.2125350
[16] Alin, D., Gert, K., Arne, B. & Danica, K. (2011). Classification of Rigid and Deformable Objects Using a Novel Tactile Sensor, Advanced Robotics 2011 : Proceedings of an IEEE international conference in Tallinn, Estonia, 20-23 June 2011 (pp 427-434). https://dx.doi:10.1109/ICAR.2011.6088
622
[17] Hiroshi, F., Koichi, I., Tsuyoshi, S., Makoto, T., Takao, S. & Takayasu, S. (2013). Large-Area and Flexible Sensors with Organic Transistors, Advances in Sensors and Interfaces 2013 : Proceedings of an IEEE international conference in Bari, Italy, 13-14 June 2013 (pp 87-90). https://dx.doi:10.
1109/IWASI.2013.6576084
[18] ms9723-pvsr_orig ,Retrieved August 05, 2017, from https://www.kitronyx.com/store/p30/MS9723_FSR_Matrix_Array_Sensor_%2816x10_Rows_and_Columns_%2F_80mm_x_50mm_Active_Sensing_Area%29.html
[2] Pressure Mapping: Geotechnical Engineering Retrieved Achieve Design and Process Optimization with Interface Pressure Measurement. (2016). Retrieved from https://www.tekscan.com/news/new-ebook-advancing-geotechnical-engineering-pressure-Mapping.
[3] Micah, K. J., & Edward, H. A. (2009). Retrographic sensing for the measurement of surface texture and shape, Computer Vision and Pattern Recognition 2009 : Proceedings of an IEEE Conference on in Miami, FL, USA, 20-25 June 2009 (pp. 1070–1077). https://dx.doi:10.1109/ CVPR. 2009.5206534.
[4] Casselli, S., Magnanini, C., & Zanichelli, F. (1995). On the robustness of haptic object recognition based on polyhedral shape representations, Intelligent Robots and Systems 95. 'Human Robot Interaction and Cooperative Robots' : Proceedings of an IEEE/RSJ international conference in Pittsburgh, PA, USA, 5-9 Aug. 1995 Vol2 (pp 200–206). https://dx.doi:10.1109/IROS. 1995.526160.
[5] Bajcsy, R. (1984). What can we learn from one finger experiments?. Proceedings of a Symposium on Robotic Research Bretton Woods, NH, 1984, (pp. 509–527).
[6] Ronald, S. F., & Binford, T.O. (1991). Using a Cylindrical Tactile Sensor for Determining Curvature. IEEE Transactions on Robotics and Automation, 7, 806 – 817. https://dx.doi:10.1109/70.105389
[7] Bay, S. J. (1989). Tactile shape sensing via single- and multi fingered hands, Robotics and Automation 1989 : Proceedings of an IEEE international conference in Scottsdale, AZ, USA, 14-19 May 1989 Vol 1 (pp 290-295). https://dx.doi:10.1109/ROBOT.1989.100003
[8] Allen, P. K. & Roberts, K. S. (1989). Haptic object recognition using a multi fingered dexterous hand, Robotics and Automation 1989 : Proceedings of an IEEE international conference in Scottsdale, AZ, USA, 14-19 May 1989 Vol 1 (pp 290-295). https://dx.doi: 10.1109/ROBOT.1989.100011
[9] Alexander, S. Jürgen, S. & Cyrill. S. (2009). Object Identification with Tactile Sensors using Bag-of-Features, Intelligent Robots and Systems 2009 : Proceedings of an IEEE international conference in St. Louis, MO, USA, 10-15 October 2009 (pp 243-248). https://dx.doi:10.1109/IROS. 2009.5354648
[10] Khasnobish, A., Jati, A., Singh, G., Bhattacharyya, S., Konar, A., Tibarewala, D. N., Kim, E. & Nagar, A. K. (2012). Object-Shape Recognition from Tactile Images Using a Feed-forward Neural Network, World Congress on Computational Intelligence : Proceedings of an IEEE international conference in Brisbane, QLD, Australia, 10-15 June 2012. https://dx.doi:10.1109/IJCNN.2012.6252593
[11] Datta, S., Khasnobish, A., Konar, A., Tibarewala, D. N. & Janarthanan, R. (2013). Object Shape and Size Recognition From Tactile images, Control Communication and Computing 2013 : Proceedings of an IEEE international conference in Thiruvananthapuram, India, 13-15 December 2013 (pp 16-21). https://di.doi:10.1109
/ICCC.2013.6731617
[12] Alexander, S., Yusuke, B., Kuniaki, N., Hiroyasu, I., Tetsuya, O. & Shigeki, S. (2014). Object Recognition using Deep Learning and Dropout, Humanoid Robots (Humanoids) 2014 : Proceedings of an IEEE international conference in Madrid, Spain, 18-20 November 2014 (pp 1044-1050). https://di:doi 10.1109/HUMANOIDS.2014. 7041493
[13] Harold, S. & Yiannis, D. (2014). Incrementally Learning Objects by Touch: Online Discriminative and Generative Models for Tactile-Based Recognition. IEEE Transactions On Haptics, 7, 512-525. https://dx.doi:10.1109/TOH.2014.
2326159
[14] Somchai, P. & Jakkree, S. (2016) Object Recognition from Human Tactile Image Using Artificial Neural Network, Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology 2016 : Proceedings of an IEEE international conference in Chiang Mai, Thailand, 28 June-1 July 2016. https://dx.doi: 10.1109/ECTICon.2016.7561402
[15] Zachary, P., Erion, P., Caitlin, R. & Gregory, D. H. (2011). Tactile-Object Recognition From Appearance Information. IEEE Transactions On Robotics, 27, 473-487. https://dx.doi:10.1109/TRO.2011.2125350
[16] Alin, D., Gert, K., Arne, B. & Danica, K. (2011). Classification of Rigid and Deformable Objects Using a Novel Tactile Sensor, Advanced Robotics 2011 : Proceedings of an IEEE international conference in Tallinn, Estonia, 20-23 June 2011 (pp 427-434). https://dx.doi:10.1109/ICAR.2011.6088
622
[17] Hiroshi, F., Koichi, I., Tsuyoshi, S., Makoto, T., Takao, S. & Takayasu, S. (2013). Large-Area and Flexible Sensors with Organic Transistors, Advances in Sensors and Interfaces 2013 : Proceedings of an IEEE international conference in Bari, Italy, 13-14 June 2013 (pp 87-90). https://dx.doi:10.
1109/IWASI.2013.6576084
[18] ms9723-pvsr_orig ,Retrieved August 05, 2017, from https://www.kitronyx.com/store/p30/MS9723_FSR_Matrix_Array_Sensor_%2816x10_Rows_and_Columns_%2F_80mm_x_50mm_Active_Sensing_Area%29.html