  We are interested in the problem of finding $k$ nearest neighbours in the
plane and in the presence of polygonal obstacles ($\textit{OkNN}$). Widely used
algorithms for OkNN are based on incremental visibility graphs, which means
they require costly and online visibility checking and have worst-case
quadratic running time. Recently $\mathbf{Polyanya}$, a fast point-to-point
pathfinding algorithm was proposed which avoids the disadvantages of visibility
graphs by searching over an alternative data structure known as a navigation
mesh. Previously, we adapted $\mathbf{Polyanya}$ to multi-target scenarios by
developing two specialised heuristic functions: the $\mathbf{Interval
heuristic}$ $h_v$ and the $\mathbf{Target heuristic}$ $h_t$. Though these
methods outperform visibility graph algorithms by orders of magnitude in all
our experiments they are not robust: $h_v$ expands many redundant nodes when
the set of neighbours is small while $h_t$ performs poorly when the set of
neighbours is large. In this paper, we propose new algorithms and heuristics
for OkNN which perform well regardless of neighbour density.
