Online active ensemble learning for robot collision detection in dynamic environments

In order to improve the accuracy and precision of online learning-based collision detection methods, an online active ensemble learning for robot collision detection (OAELRCD) is proposed in this paper. The OAELRCD consists of two key components: (1) an ensemble learning method to combine several base classifiers in order to improve the accuracy and precision of collision detection, (2) an active learning algorithm to reduce the number of training samples in order to realize online training and learning when the environment changes. We evaluate the proposed OAELRCD on one robot arm in dynamic environments with moving workspace obstacles, showing that the proposed OAELRCD outperforms state-of-The-Art online learning-based method and geometric collision checkers. Compared to the state-of-The-Art online learning-based method for robot collision detection in dynamic environments, the proposed OAELRCD provides noticeable improvements in TPR, AUC, Accuracy and TNR. Compared to state-of-The-Art geometric collision checkers, with the proposed OAELRCD, collision checks are faster.