  In our previous work, we designed a systematic policy to prioritize sampling
locations to lead significant accuracy improvement in spatial interpolation by
using the prediction uncertainty of Gaussian Process Regression (GPR) as
"attraction force" to deployed robots in path planning. Although the
integration with Traveling Salesman Problem (TSP) solvers was also shown to
produce relatively short travel distance, we here hypothesise several factors
that could decrease the overall prediction precision as well because
sub-optimal locations may eventually be included in their paths. To address
this issue, in this paper, we first explore "local planning" approaches
adopting various spatial ranges within which next sampling locations are
prioritized to investigate their effects on the prediction performance as well
as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level
controllers are trained to adaptively produce blended plans from a particular
set of local planners to inherit unique strengths from that selection depending
on latest prediction states. Our experiments on use cases of temperature
monitoring robots demonstrate that the dynamic mixtures of planners can not
only generate sophisticated, informative plans that a single planner could not
create alone but also ensure significantly reduced travel distances at no cost
of prediction reliability without any assist of additional modules for shortest
path calculation.
