Probabilistic approach enhances robot navigation in complex environments, indicating improved path planning efficiency.
In this paper, a probabilistic approach is proposed for autonomous robots. The proposed method analyzes various environmental factors to generate a probability density distribution. These environmental factors are represented within Euclidean space and modeled as multidimensional structures. A multidimensional environmental model was employed to support robot navigation. The proposed approach comprises two main modules: a collision and path planning module. The collision module estimates the risk level in local environments, whereas the path planning module generates efficient movement paths. In simulations, the proposed method enabled autonomous robots to navigate safely, even in highly complex environments.
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Yoon et al. (2025) studied this question.
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