  For heterogeneous unmanned systems composed of unmanned aerial vehicles
(UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist
UGVs in motion planning is a promising research direction due to the UAVs' vast
view scope. However, due to UAVs flight altitude limitations, it may be
impossible to observe the global map, and motion planning in the local map is a
POMDP (Partially Observable Markov Decision Process) problem. This paper
proposes a motion planning algorithm for heterogeneous unmanned system under
partial observation from UAV without reconstruction of global maps, which
consists of two parts designed for perception and decision-making,
respectively. For the perception part, we propose the Grid Map Generation
Network (GMGN), which is used to perceive scenes from UAV's perspective and
classify the pathways and obstacles. For the decision-making part, we propose
the Motion Command Generation Network (MCGN). Due to the addition of memory
mechanism, MCGN has planning and reasoning abilities under partial observation
from UAVs. We evaluate our proposed algorithm by comparing with baseline
algorithms. The results show that our method effectively plans the motion of
heterogeneous unmanned systems and achieves a relatively high success rate.
