We study a novel problem that tackles learning based sensor scanning in 3D and uncertain environments with heterogeneous multi-robot systems. Our motivation is two-fold: first, 3D environments are complex, the use of heterogeneous multi-robot systems intuitively can facilitate sensor scanning by fully taking advantage of sensors with different capabilities. Second, in uncertain environments (e.g. rescue), time is of great significance. Since the learning process normally takes time to train and adapt to a new environment, we need to find an effective way to explore and adapt quickly. To this end, in this paper, we present a meta-learning approach to improve the exploration and adaptation capabilities. The experimental results demonstrate our method can outperform other methods by approximately 15%-27% on success rate and 70%-75% on adaptation speed.
翻译:我们研究一个新问题,在3D和不确定的环境中用多种机器人系统进行基于学习的传感器扫描。 我们的动机有两个方面:第一, 3D环境是复杂的, 使用多种机器人系统直观地能够充分利用不同能力的传感器来方便传感器的扫描。 第二, 在不确定的环境中(例如救援),时间非常重要。 由于学习过程通常需要时间来训练和适应新的环境, 我们需要找到一个有效的方法来迅速探索和适应。 为此,我们在本文件中介绍了一种元学习方法来改进探索和适应能力。 实验结果表明,我们的方法在成功率上比其他方法高出大约15%-27%,在适应速度上比其他方法高出70%-75%。