Research on Trajectory Learning and Obstacle Avoidance Algorithms for Mobile Robots Based on Probabilistic Motion Primitives
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.
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维普
