Abstract
Vibration is an indicator of performance degradation or operational safety issues of mobile cleaning robots. Therefore, predicting the source of vibration at an early stage will help to avoid functional losses and hazardous operational environments. This work presents an artificial intelligence (AI)-enabled predictive maintenance framework for mobile cleaning robots to identify performance degradation and operational safety issues through vibration signals. A four-layer 1D CNN framework was developed and trained with a vibration signals dataset generated from the in-house developed autonomous steam mopping robot ‘Snail’ with different health conditions and hazardous operational environments. The vibration signals were collected using an IMU sensor and categorized into five classes: normal operational vibration, hazardous terrain induced vibration, collision-induced vibration, loose assembly induced vibration, and structure imbalanced vibration signals. The performance of the trained predictive maintenance framework was evaluated with various real-time field trials with statistical measurement metrics. The experiment results indicate that our proposed predictive maintenance framework has accurately predicted the performance degradation and operational safety issues by analyzing the vibration signal patterns raised from the cleaning robot on different test scenarios. Finally, a predictive maintenance map was generated by fusing the vibration signal class on the cartographer SLAM algorithm-generated 2D environment map.
Highlights
Mobile cleaning robots with various capacities are ubiquitous today, for instance in food courts, hypermarkets, hospitals, industries, airports, and homes, and are used for vacuuming, mopping, and sanitizing the environment
An artificial intelligence (AI)-enabled predictive maintenance framework was proposed for mobile cleaning robots to monitor performance degradation and identify operational safety issues
A four-layer 1D Convolutional Neural Network (1D CNN) model was developed using TensorFlow API and trained with five vibration signal datasets generated from the Snail robot with different health conditions
Summary
Mobile cleaning robots with various capacities are ubiquitous today, for instance in food courts, hypermarkets, hospitals, industries, airports, and homes, and are used for vacuuming, mopping, and sanitizing the environment. Manual supervision is widely used to monitor professional cleaning robots’ performance degradation and safety-related issues It is time-consuming, labor and skill-setdependent, and challenging to deploy due to the lack of historical failure data, especially for the newly developed advanced cleaning robots. Automated predictive maintenance strategies overcome these pitfalls They are widely used in industrial robots and autonomous vehicles for continuous health monitoring, performance degradation prediction, hazardous operational environment identification, and safety system failure indication.
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