A Deterministic Path Planning Algorithm with Greedy Heuristics for Mobile Robots

This paper proposes a novel global path planning algorithm for mobile robots. The path extends with samples taken under a greedy heuristic strategy so that samples close to the goal are prioritized. Inspired by graph-searching algorithms, it employs a vertex evaluation scheme to navigate around the obstacles. To remove redundant paths, a rewiring mechanism is proposed to fine-tune the planned path. Numerical simulation is conducted in
$\mathbb{R}^{2}$
with different obstacle distributions. The proposed algorithm finds better paths with less computation cost than reference sampling-based planners. Compared with the optimality-guaranteed graph-search methods, the proposed algorithm is more robust against obstacle density while ensuring a solution quality close to the global optimum.