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.
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