Abstract
Aiming to address the shortcomings of conventional algorithms in mobile robot path planning, such as long paths, random sampling and high collision risk, a novel probabilistic roadmap algorithm, or node reduction-based search algorithm, is proposed. A decision-making strategy is developed to identify the suitable node, which depends on the distance error to the nearby obstacles and goal and the Gaussian cost function, which improves path efficiency by eliminating unwanted nodes. Then, an optimal path is selected based on the weight of the edges. The comparative analysis is conducted in five different test cases with differing complexity. Different performance parameters are measured to validate the effectiveness of the proposed algorithm. The outcomes acquired from the different test cases indicate that the proposed algorithm outperforms the state-of-the-art algorithms, with a maximum improvement of 13.51% in path length, 66.82% in execution time, 28.5% in smoothness and 38.05% in collision risk value.
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