Sampling-based path planning algorithms are well-known because they are able to find a path in a very short period of time, even in high-dimensional spaces. However, they are non-smooth, random paths far away from the optimum. In this paper we introduce a novel improving technique based on the Fast Marching Method which improves in a deterministic, non-iterative way the initial path provided by a sampling-based methods. Simulation results show that the computation time of the proposed method is low and that path length and smoothness are improved.
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