A conceptual approach to material detection based on damping vibration-force signals via robot
IntroductionObject perception, particularly material detection, is predominantly performed through texture recognition, which presents significant limitations. These methods are insufficient to distinguish between different materials with similar surface roughness, and noise caused by tactile moveme...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1503398/full |
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| author | Ahmad Saleh Asheghabadi Mohammad Keymanesh Saeed Bahrami Moqadam Saeed Bahrami Moqadam Saeed Bahrami Moqadam Jing Xu |
| author_facet | Ahmad Saleh Asheghabadi Mohammad Keymanesh Saeed Bahrami Moqadam Saeed Bahrami Moqadam Saeed Bahrami Moqadam Jing Xu |
| author_sort | Ahmad Saleh Asheghabadi |
| collection | DOAJ |
| description | IntroductionObject perception, particularly material detection, is predominantly performed through texture recognition, which presents significant limitations. These methods are insufficient to distinguish between different materials with similar surface roughness, and noise caused by tactile movements affects the system performance.MethodsThis paper presents a straightforward, impact-based approach to identifying materials, utilizing the cantilever beam mechanism in the UR5e robot's artificial finger. To detect object material, an elastic metal sheet was fixed to a load cell with an accelerometer and a metal appendage positioned above and below its free end, respectively. After recording the damping force signal and vibration data from the load cell and accelerometer caused by the metal appendage's impact, features such as vibration amplitude, damping time, wavelength, and force amplitude were retrieved. Three machine-learning techniques were then used to classify the objects' materials according to their damping rates. Data clustering was performed using the deflection of the cantilever beam to boost classification accuracy.Results and discussionOnline object materials detection shows an accuracy of 95.46% in a study of ten objects [metals (steel, cast iron), plastics (foam, compressed plastic), wood, silicon, rubber, leather, brick and cartoon]. This method overcomes the limitations of the tactile approach and has the potential to be used in industrial robots. |
| format | Article |
| id | doaj-art-74139e8adf1348f8b31c1147b87ae9d7 |
| institution | Kabale University |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-74139e8adf1348f8b31c1147b87ae9d72025-02-11T06:59:58ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-02-011910.3389/fnbot.2025.15033981503398A conceptual approach to material detection based on damping vibration-force signals via robotAhmad Saleh Asheghabadi0Mohammad Keymanesh1Saeed Bahrami Moqadam2Saeed Bahrami Moqadam3Saeed Bahrami Moqadam4Jing Xu5State Key Laboratory of Tribology, The Beijing Key Laboratory of Precision/Ultra-Precision Manufacturing Equipment Control, The Department of Mechanical Engineering, Tsinghua University, Beijing, ChinaState Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, ChinaDepartment of Control Science and Engineering, Tongji University, Shanghai, ChinaNational Key Laboratory of Autonomous Intelligent Unmanned Systems, Shanghai, ChinaFrontiers Science Center for Intelligent Autonomous Systems, Shanghai, ChinaState Key Laboratory of Tribology, The Beijing Key Laboratory of Precision/Ultra-Precision Manufacturing Equipment Control, The Department of Mechanical Engineering, Tsinghua University, Beijing, ChinaIntroductionObject perception, particularly material detection, is predominantly performed through texture recognition, which presents significant limitations. These methods are insufficient to distinguish between different materials with similar surface roughness, and noise caused by tactile movements affects the system performance.MethodsThis paper presents a straightforward, impact-based approach to identifying materials, utilizing the cantilever beam mechanism in the UR5e robot's artificial finger. To detect object material, an elastic metal sheet was fixed to a load cell with an accelerometer and a metal appendage positioned above and below its free end, respectively. After recording the damping force signal and vibration data from the load cell and accelerometer caused by the metal appendage's impact, features such as vibration amplitude, damping time, wavelength, and force amplitude were retrieved. Three machine-learning techniques were then used to classify the objects' materials according to their damping rates. Data clustering was performed using the deflection of the cantilever beam to boost classification accuracy.Results and discussionOnline object materials detection shows an accuracy of 95.46% in a study of ten objects [metals (steel, cast iron), plastics (foam, compressed plastic), wood, silicon, rubber, leather, brick and cartoon]. This method overcomes the limitations of the tactile approach and has the potential to be used in industrial robots.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1503398/fullcantilever beam mechanismdamping force signal and damping vibrationmaterial detectionvibration amplitudedamping timewavelength |
| spellingShingle | Ahmad Saleh Asheghabadi Mohammad Keymanesh Saeed Bahrami Moqadam Saeed Bahrami Moqadam Saeed Bahrami Moqadam Jing Xu A conceptual approach to material detection based on damping vibration-force signals via robot Frontiers in Neurorobotics cantilever beam mechanism damping force signal and damping vibration material detection vibration amplitude damping time wavelength |
| title | A conceptual approach to material detection based on damping vibration-force signals via robot |
| title_full | A conceptual approach to material detection based on damping vibration-force signals via robot |
| title_fullStr | A conceptual approach to material detection based on damping vibration-force signals via robot |
| title_full_unstemmed | A conceptual approach to material detection based on damping vibration-force signals via robot |
| title_short | A conceptual approach to material detection based on damping vibration-force signals via robot |
| title_sort | conceptual approach to material detection based on damping vibration force signals via robot |
| topic | cantilever beam mechanism damping force signal and damping vibration material detection vibration amplitude damping time wavelength |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1503398/full |
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