Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning
Ming Hu, Shuhai Jiang, Kangqian Zhou, Xunan Cao, Cun Li
TL;DR
This paper introduces a new hybrid algorithm for mobile robot path planning that improves efficiency and smoothness compared to existing methods.
Contribution
The novel hybrid algorithm combines an improved A* with Dynamic Window Approach, enhancing path smoothness and search efficiency.
Findings
The hybrid algorithm improves search efficiency by 10.93% and reduces search node count by 32.26%.
It decreases turning points by 36.36% and shortens total path length by 22.05%.
The algorithm is more stable and reduces collision probability in real-world environments.
Abstract
The A* algorithm is widely used in mobile robot path planning; however, it faces challenges such as unsmooth planned paths, redundant nodes, and extensive search areas. This paper proposes a hybrid algorithm combining an improved A* algorithm with the Dynamic Window Approach. By quantifying grid obstacle data to extract environmental information and employing a grid-based environmental modeling method, the proposed approach enhances path smoothness at turns using second-order Bezier curve smoothing. It improves the heuristic function and child node selection process, applying these advancements in experimental path planning scenarios. A simulated 2D map was constructed using point cloud scanning in RViz to validate the hybrid algorithm through simulations and real-world outdoor tests. Experimental results demonstrate that, compared to the A* and DWA algorithms, the improved hybrid…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Robotics and Sensor-Based Localization · Computational Geometry and Mesh Generation