Demonstration learning has shown potential in path planning for mobile robots,but when it is directly applied to three-dimensional space,it often faces challenges such as low efficiency and obstacle collisions.A three-dimensional path planning method for mobile robots based on probabilistic motion element modeling is proposed.By simplifying speed information and modeling the time-domain coordinates of teaching path points,efficient online planning has been achieved,and path accuracy has been improved through conditional Gaussian calculation.An obstacle avoid-ance algorithm that utilizes obstacle information to assign path bias values,combined with a first-order system attraction point model is designed to ensure smooth obstacle avoidance along the path.Experimental verification shows that the model has good planning effect in three-dimensional space,low time cost,and effective obstacle avoidance algorithm,providing a new idea for autonomous navigation of mobile robots in complex environments.
维普
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