Fast optimal and bounded suboptimal Euclidean pathfinding

Abstract We consider optimal and suboptimal algorithms for the Euclidean Shortest Path Problem (ESPP) in two dimensions. For optimal path planning, Our approach leverages ideas from two recent works: Polyanya, a mesh-based ESPP planner which we use to represent and reason about the environment, and Compressed Path Databases (CPD), a speedup technique for pathfinding on grids and spatial networks, which we exploit to efficiently compute candidate paths, in order to construct a completely novel ESPP algorithm, End Point Search (EPS). In a range of experiments and empirical comparisons we show that: (i) the auxiliary data structures required by EPS are cheap to build and store; (ii) for optimal search, the new algorithm is faster than a range of recent ESPP planners, with speedups ranging from several factors to over one order of magnitude; (iii) for anytime search, where feasible solutions are needed fast, we report even better performance. For suboptimal path planning, we extend the CPD such that it computes and compresses first move data of a larger number of selected candidate nodes covering every point in the Euclidean space. Our approach is search-free, simultaneously fast, and returns a path within a fixed bound of the optimal solution. In a range of empirical results, we show that: (i) our approach outperforms both offline/online optimal and suboptimal ESPP algorithms proposed in the literature; (ii) our approach demonstrates excellent path quality, better than all existing suboptimal ESPP algorithms; and (iii) the approach offers flexibility by allowing a trade-off between the CPD construction cost (space and time) and the suboptimality bound.