Path Backtracking Planning of Mobile Robot Based on Variable Probability Constraints
In order to solve the problem that the Informed RRT*-Connect algorithm does not have the ability to search the near optimal path efficiently,an optimization algorithm based on variable probability constrained sampling target is proposed.First-ly,the target offset mechanism is introduced into the sampling process.Its purpose is to accelerate the convergence of the first path and generate the ellipse sample set quickly.Secondly,backtracking mechanism is used to optimize the searched path and further shorten the path length.In order to verify the effectiveness of the improved algorithm,a comparative experiment with the classical path planning algorithm is carried out on the Python platform in different scenarios.The results show that the improved algorithm can improve the search efficiency effectively,reduce the search time,and ensure that the search path is a near optimal path.
mobile robotpath planningrapidly-exploring random treessearching speed
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