Path Planning of Intelligent Mobile Robots with an Improved RRT Algorithm

The Rapidly Exploring Random Tree algorithm, renowned for its randomness, asymptotic properties, and local planning capabilities, is extensively employed in autonomous driving for path planning. Addressing issues such as pronounced randomness, low search efficiency, inefficient utilization of effective points, suboptimal path smoothness, and potential deviations from the optimal path in the RRT algorithm based on random sampling, we proposed an optimization algorithm that integrates Kalman filtering to eliminate redundant points along the path. Initially, this algorithm addresses the issue of inverse growth in the RRT algorithm's search tree by implementing a variable steering angle strategy, thereby minimizing oscillations and unnecessary pose adjustments. Secondly, by merging collision detection with Kalman filtering, and by comparing the step sizes between newly generated child nodes and random tree nodes towards the root node, we filtered redundant points from the path, thereby reducing the count of effective points and optimizing the path. Lastly, we utilized a second-order Bezier curve to smoothen the path, eliminating sharp corners and discontinuities, ultimately yielding the optimal path. Across diverse map environments and two distinct dimensional scenarios, we conducted multiple sets of simulation experiments to validate the algorithm's feasibility. The experimental outcomes demonstrate notable improvements in parameters like average path length, average planning time, average count of effective points, and average sampling points, highlighting the enhanced accuracy and efficiency of the improved algorithm in path planning.