Informed-RRT*Algorithm Based on Heuristic Adaptive Step Size Optimization
The Informed-RRT*algorithm is a commonly used algorithm for solving global path planning problems.When dealing with narrow environments,the Informed-RRT*algorithm often gets trapped in local optima,while the cost of path planning in complex environments tends to be excessively high.To address these issues,a sampling strategy based on heuristic adaptive step size is proposed to improve the limitations of the Informed-RRT*algorithm.Firstly,the heuristic value is computed by expanding the sample node set around random points,and then the optimal node is selected and expanded along its growth direction.Secondly,the distance between the optimal node and the nearest node is calculated to determine the step size for the next sampling.This enables the robot to better adapt to narrow areas and complex environments in both 2D and 3D scenarios.Finally,the improved algorithm is validated through simulation in both 2D and 3D scenarios.The experimental results demonstrate the remarkable effectiveness and robustness of the algorithm.
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