AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots
An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/18/3804 |
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| author | Sathian Pookkuttath Raihan Enjikalayil Abdulkader Mohan Rajesh Elara Prabakaran Veerajagadheswar |
| author_facet | Sathian Pookkuttath Raihan Enjikalayil Abdulkader Mohan Rajesh Elara Prabakaran Veerajagadheswar |
| author_sort | Sathian Pookkuttath |
| collection | DOAJ |
| description | An automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice. |
| first_indexed | 2024-03-10T22:29:36Z |
| format | Article |
| id | doaj.art-0bb88fd182ae46b78e40336b49f00cd8 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T22:29:36Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-0bb88fd182ae46b78e40336b49f00cd82023-11-19T11:47:50ZengMDPI AGMathematics2227-73902023-09-011118380410.3390/math11183804AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile RobotsSathian Pookkuttath0Raihan Enjikalayil Abdulkader1Mohan Rajesh Elara2Prabakaran Veerajagadheswar3Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeEngineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, SingaporeAn automated Condition Monitoring (CM) and real-time controlling framework is essential for outdoor mobile robots to ensure the robot’s health and operational safety. This work presents a novel Artificial Intelligence (AI)-enabled CM and vibrotactile haptic-feedback-based real-time control framework suitable for deploying mobile robots in dynamic outdoor environments. It encompasses two sections: developing a 1D Convolutional Neural Network (1D CNN) model for predicting system degradation and terrain flaws threshold classes and a vibrotactile haptic feedback system design enabling a remote operator to control the robot as per predicted class feedback in real-time. As vibration is an indicator of failure, we identified and separated system- and terrain-induced vibration threshold levels suitable for CM of outdoor robots into nine classes, namely Safe, moderately safe system-generated, and moderately safe terrain-induced affected by left, right, and both wheels, as well as severe classes such as unsafe system-generated and unsafe terrain-induced affected by left, right, and both wheels. The vibration-indicated data for each class are modelled based on two sensor data: an Inertial Measurement Unit (IMU) sensor for the change in linear and angular motion and a current sensor for the change in current consumption at each wheel motor. A wearable novel vibrotactile haptic feedback device architecture is presented with left and right vibration modules configured with unique haptic feedback patterns corresponding to each abnormal vibration threshold class. The proposed haptic-feedback-based CM framework and real-time remote controlling are validated with three field case studies using an in-house-developed outdoor robot, resulting in a threshold class prediction accuracy of 91.1% and an effectiveness that, by minimising the traversal through undesired terrain features, is four times better than the usual practice.https://www.mdpi.com/2227-7390/11/18/3804AIcondition monitoringvibrationvibrotactile haptic feedbackwearable deviceIMU |
| spellingShingle | Sathian Pookkuttath Raihan Enjikalayil Abdulkader Mohan Rajesh Elara Prabakaran Veerajagadheswar AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots Mathematics AI condition monitoring vibration vibrotactile haptic feedback wearable device IMU |
| title | AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots |
| title_full | AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots |
| title_fullStr | AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots |
| title_full_unstemmed | AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots |
| title_short | AI-Enabled Vibrotactile Feedback-Based Condition Monitoring Framework for Outdoor Mobile Robots |
| title_sort | ai enabled vibrotactile feedback based condition monitoring framework for outdoor mobile robots |
| topic | AI condition monitoring vibration vibrotactile haptic feedback wearable device IMU |
| url | https://www.mdpi.com/2227-7390/11/18/3804 |
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