Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors
Human–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/24/9780 |
| _version_ | 1827573591370104832 |
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| author | Rajmeet Singh Saeed Mozaffari Masoud Akhshik Mohammed Jalal Ahamed Simon Rondeau-Gagné Shahpour Alirezaee |
| author_facet | Rajmeet Singh Saeed Mozaffari Masoud Akhshik Mohammed Jalal Ahamed Simon Rondeau-Gagné Shahpour Alirezaee |
| author_sort | Rajmeet Singh |
| collection | DOAJ |
| description | Human–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the human. To perform complex tasks, such as robotic grasping and manipulation, which require both human intelligence and robotic capabilities, effective interaction modes are required. To address this issue, we use a wearable glove to collect relevant data from a human demonstrator for improved human–robot interaction. Accelerometer, pressure, and flexi sensors were embedded in the wearable glove to measure motion and force information for handling objects of different sizes, materials, and conditions. A machine learning algorithm is proposed to recognize grasp orientation and position, based on the multi-sensor fusion method. |
| first_indexed | 2024-03-08T20:23:26Z |
| format | Article |
| id | doaj.art-cc02dfe954214d01952c5f7d504d7bb9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:23:26Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cc02dfe954214d01952c5f7d504d7bb92023-12-22T14:40:39ZengMDPI AGSensors1424-82202023-12-012324978010.3390/s23249780Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple SensorsRajmeet Singh0Saeed Mozaffari1Masoud Akhshik2Mohammed Jalal Ahamed3Simon Rondeau-Gagné4Shahpour Alirezaee5Mechanical, Automotive, and Material Engineering Department, University of Windsor, Windsor, ON N9B 3P4, CanadaMechanical, Automotive, and Material Engineering Department, University of Windsor, Windsor, ON N9B 3P4, CanadaMechanical, Automotive, and Material Engineering Department, University of Windsor, Windsor, ON N9B 3P4, CanadaMechanical, Automotive, and Material Engineering Department, University of Windsor, Windsor, ON N9B 3P4, CanadaDepartment of Chemistry and Biochemistry, University of Windsor, Windsor, ON N9B 3P4, CanadaMechanical, Automotive, and Material Engineering Department, University of Windsor, Windsor, ON N9B 3P4, CanadaHuman–robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the human. To perform complex tasks, such as robotic grasping and manipulation, which require both human intelligence and robotic capabilities, effective interaction modes are required. To address this issue, we use a wearable glove to collect relevant data from a human demonstrator for improved human–robot interaction. Accelerometer, pressure, and flexi sensors were embedded in the wearable glove to measure motion and force information for handling objects of different sizes, materials, and conditions. A machine learning algorithm is proposed to recognize grasp orientation and position, based on the multi-sensor fusion method.https://www.mdpi.com/1424-8220/23/24/9780robotic graspinghuman–robot interactioninertiapressureflexi sensorswearable devices |
| spellingShingle | Rajmeet Singh Saeed Mozaffari Masoud Akhshik Mohammed Jalal Ahamed Simon Rondeau-Gagné Shahpour Alirezaee Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors Sensors robotic grasping human–robot interaction inertia pressure flexi sensors wearable devices |
| title | Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors |
| title_full | Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors |
| title_fullStr | Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors |
| title_full_unstemmed | Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors |
| title_short | Human–Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors |
| title_sort | human robot interaction using learning from demonstrations and a wearable glove with multiple sensors |
| topic | robotic grasping human–robot interaction inertia pressure flexi sensors wearable devices |
| url | https://www.mdpi.com/1424-8220/23/24/9780 |
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