  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.
