Material Classification Using Active Temperature Controllable Robotic Gripper
International audience Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One of the distinguishing features of each object's mat...
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ftccsdartic:oai:HAL:hal-03453954v1 2023-05-15T17:14:08+02:00 Material Classification Using Active Temperature Controllable Robotic Gripper Osawa, Yukiko Kase, Kei Domae, Yukiyasu Furukawa, Yoshiyuki Kheddar, Abderrahmane National Institute of Advanced Industrial Science and Technology Tokyo (AIST) Interactive Digital Humans (IDH) Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) National Institute of Advanced Industrial Science and Technology (AIST) Narvik, Norway 2022-01-09 https://hal.archives-ouvertes.fr/hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954/document https://hal.archives-ouvertes.fr/hal-03453954/file/SII22_osawa.pdf en eng HAL CCSD info:eu-repo/semantics/altIdentifier/arxiv/2111.15344 hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954/document https://hal.archives-ouvertes.fr/hal-03453954/file/SII22_osawa.pdf ARXIV: 2111.15344 info:eu-repo/semantics/OpenAccess Proceedings of the 14th IEEE/SICE International Symposium on System Integration (SII 2022) https://hal.archives-ouvertes.fr/hal-03453954 Proceedings of the 14th IEEE/SICE International Symposium on System Integration (SII 2022), Jan 2022, Narvik, Norway [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] info:eu-repo/semantics/conferenceObject Conference papers 2022 ftccsdartic 2022-01-08T23:48:37Z International audience Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One of the distinguishing features of each object's material is its heat properties, and classification can exploit heat transfer, similarly to human thermal sensation. Thermal-based recognition has the advantage of obtaining contact surface information in realtime by simply capturing temperature change using a tiny and cheap sensor. However, heat transfer between a robot surface and a contact object is strongly affected by the initial temperature and environmental conditions. A given object's material cannot be recognized when its temperature is the same as the robotic grippertip. We present a material classification system using active temperature controllable robotic gripper to induce heat flow. Subsequently, our system can recognize materials independently from their ambient temperature. The robotic gripper surface can be regulated to any temperature that differentiates it from the touched object's surface. We conducted some experiments by integrating the temperature control system with the Academic SCARA Robot, classifying them based on a long short-term memory (LSTM) using temperature data obtained from grasping target objects. Conference Object Narvik Narvik Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Narvik ENVELOPE(17.427,17.427,68.438,68.438) Norway |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] |
spellingShingle |
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] Osawa, Yukiko Kase, Kei Domae, Yukiyasu Furukawa, Yoshiyuki Kheddar, Abderrahmane Material Classification Using Active Temperature Controllable Robotic Gripper |
topic_facet |
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-SY]Computer Science [cs]/Systems and Control [cs.SY] |
description |
International audience Recognition techniques allow robots to make proper planning and control strategies to manipulate various objects. Object recognition is more reliable when made by combining several percepts, e.g., vision and haptics. One of the distinguishing features of each object's material is its heat properties, and classification can exploit heat transfer, similarly to human thermal sensation. Thermal-based recognition has the advantage of obtaining contact surface information in realtime by simply capturing temperature change using a tiny and cheap sensor. However, heat transfer between a robot surface and a contact object is strongly affected by the initial temperature and environmental conditions. A given object's material cannot be recognized when its temperature is the same as the robotic grippertip. We present a material classification system using active temperature controllable robotic gripper to induce heat flow. Subsequently, our system can recognize materials independently from their ambient temperature. The robotic gripper surface can be regulated to any temperature that differentiates it from the touched object's surface. We conducted some experiments by integrating the temperature control system with the Academic SCARA Robot, classifying them based on a long short-term memory (LSTM) using temperature data obtained from grasping target objects. |
author2 |
National Institute of Advanced Industrial Science and Technology Tokyo (AIST) Interactive Digital Humans (IDH) Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM) National Institute of Advanced Industrial Science and Technology (AIST) |
format |
Conference Object |
author |
Osawa, Yukiko Kase, Kei Domae, Yukiyasu Furukawa, Yoshiyuki Kheddar, Abderrahmane |
author_facet |
Osawa, Yukiko Kase, Kei Domae, Yukiyasu Furukawa, Yoshiyuki Kheddar, Abderrahmane |
author_sort |
Osawa, Yukiko |
title |
Material Classification Using Active Temperature Controllable Robotic Gripper |
title_short |
Material Classification Using Active Temperature Controllable Robotic Gripper |
title_full |
Material Classification Using Active Temperature Controllable Robotic Gripper |
title_fullStr |
Material Classification Using Active Temperature Controllable Robotic Gripper |
title_full_unstemmed |
Material Classification Using Active Temperature Controllable Robotic Gripper |
title_sort |
material classification using active temperature controllable robotic gripper |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.archives-ouvertes.fr/hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954/document https://hal.archives-ouvertes.fr/hal-03453954/file/SII22_osawa.pdf |
op_coverage |
Narvik, Norway |
long_lat |
ENVELOPE(17.427,17.427,68.438,68.438) |
geographic |
Narvik Norway |
geographic_facet |
Narvik Norway |
genre |
Narvik Narvik |
genre_facet |
Narvik Narvik |
op_source |
Proceedings of the 14th IEEE/SICE International Symposium on System Integration (SII 2022) https://hal.archives-ouvertes.fr/hal-03453954 Proceedings of the 14th IEEE/SICE International Symposium on System Integration (SII 2022), Jan 2022, Narvik, Norway |
op_relation |
info:eu-repo/semantics/altIdentifier/arxiv/2111.15344 hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954 https://hal.archives-ouvertes.fr/hal-03453954/document https://hal.archives-ouvertes.fr/hal-03453954/file/SII22_osawa.pdf ARXIV: 2111.15344 |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1766071419093909504 |