Abstract
In recent years, robotic sorting is widely used in the industry, which is driven by necessity and opportunity. In this paper, a novel neuromorphic vision-based tactile sensing approach for robotic sorting application is proposed. This approach has low latency and low power consumption when compared to conventional vision-based tactile sensing techniques. Two Machine Learning (ML) methods, namely, Support Vector Machine (SVM) and Dynamic Time Warping-K Nearest Neighbor (DTW-KNN), are developed to classify material hardness, object size, and grasping force. An Event-Based Object Grasping (EBOG) experimental setup is developed to acquire datasets, where 243 experiments are produced to train the proposed classifiers. Based on predictions of the classifiers, objects can be automatically sorted. If the prediction accuracy is below a certain threshold, the gripper re-adjusts and re-grasps until reaching a proper grasp. The proposed ML method achieves good prediction accuracy, which shows the effectiveness and the applicability of the proposed approach. The experimental results show that the developed SVM model outperforms the DTW-KNN model in term of accuracy and efficiency for real time contact-level classification.
Highlights
Robotic sorting is a key part for most industrial production lines
The experimental results show that the developed Support Vector Machine (SVM) model outperforms the Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) model in term of accuracy and efficiency for real time contact-level classification
A novel approach utilizing the developed neuromorphic vision based tactile sensor is developed for contact level classification machine learning approaches utilizing SVM and DTW-KNN are developed to classify material hardness, object size and grasping force
Summary
Robotic sorting is a key part for most industrial production lines. Their applications vary from sorting and organizing products in warehouses, automotive assembling in manufacturing plants, and cleaning up debris in disaster zones. We explore and study how the event based tactile sensor with occluded skin can be effective in contact-level classification, especially in robotic sorting applications. Robust ML approaches are adopted to capture the non-linear relationship over time in this work to obtain prior knowledge of the object characteristics based on DAVIS triggered events. A Machine Learning approach utilizing SVM and DTW-KNN is developed for contact-level classification, in order to acquire the prior knowledge of objects based on EBOG datasets created. A novel approach utilizing the developed neuromorphic vision based tactile sensor is developed for contact level classification machine learning approaches utilizing SVM and DTW-KNN are developed to classify material hardness, object size and grasping force.
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