Machine Learning Versus Deep Learning for Contact Detection in Human-Robot Collaboration

 Due to the rapid progression of Human-Robot Collaboration (HRC), ensuring safe interactions between humans and robots, contact detecting systems must be dependable and efficient. In this research, various models are tested using a contact detection dataset that includes non-contact motions, intentional interactions, and accidental collisions among others. K-Nearest Neighbors (KNN), Bagging, and Long Short-Term Memory (LSTM) networks are evaluated on their ability to classify different types of contacts. According to the findings of the experiment, it is clear that KNN and Bagging are reasonably accurate, but LSTM has surpassed both by achieving higher accuracy levels besides being better at handling temporal dependencies which are inherent in sensor data collected from dynamic human-robot interactions. The results have shown that when it comes to such kind of contact detection datasets, long short-term memory (LSTM) and other deep learning models are superior to other methods. These results show that HRC systems can be made safer and more effective by using more sophisticated neural networks. This research helps connect theory with practice by providing a foundation for the creation of collaborative robots that are not only intelligent but also safe.