  In Japan, inspection of irrigation water canals has been mostly conducted
manually. However, the huge demand for more regular inspections as
infrastructure ages, coupled with the limited time window available for
inspection, has rendered manual inspection increasingly insufficient. With
shortened inspection time and reduced labor cost, automated inspection using a
combination of unmanned aerial vehicles (UAVs) and ground vehicles (cars) has
emerged as an attractive alternative to manual inspection. In this paper, we
propose a path planning framework that generates optimal plans for UAVs and
cars to inspect water canals in a large agricultural area (tens of square
kilometers). In addition to optimality, the paths need to satisfy several
constraints, in order to guarantee UAV navigation safety and to abide by local
traffic regulations. In the proposed framework, the canal and road networks are
first modeled as two graphs, which are then partitioned into smaller subgraphs
that can be covered by a given fleet of UAVs within one battery charge. The
problem of finding optimal paths for both UAVs and cars on the graphs, subject
to the constraints, is formulated as a mixed-integer quadratic program (MIQP).
The proposed framework can also quickly generate new plans when a current plan
is interrupted. The effectiveness of the proposed framework is validated by
simulation results showing the successful generation of plans covering all
given canal segments, and the ability to quickly revise the plan when
conditions change.
