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.

中文翻译：

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动态环境中机器人碰撞检测的在线主动集成学习

为了提高基于在线学习的碰撞检测方法的准确性和精度，本文提出了一种用于机器人碰撞检测的在线主动集成学习（OELRCD）。OAELRCD 由两个关键部分组成：(1) 一种集成学习方法，将多个基分类器结合起来，以提高碰撞检测的准确性和精度，(2) 一种主动学习算法，以减少训练样本的数量，以实现环境变化时的在线培训和学习。我们在具有移动工作空间障碍物的动态环境中评估了在一个机器人手臂上提出的 OAELRCD，表明所提出的 OAELRCD 优于最先进的基于在线学习的方法和几何碰撞检查器。与最先进的基于在线学习的动态环境中机器人碰撞检测方法相比，所提出的 OAELRCD 在 TPR、AUC、准确性和 TNR 方面提供了显着的改进。与最先进的几何碰撞检查器相比，使用提出的 OAELRCD，碰撞检查速度更快。