| 陈先益,原伟峰,罗兴桓.基于柔绳拉伸机制的A*算法路径改进与AGV自主导航[J].电子测量与仪器学报,2025,39(4):203-212 |
| 基于柔绳拉伸机制的A*算法路径改进与AGV自主导航 |
| Path improvement of the A* algorithm based on the flexible ropestretching mechanism and AGV autonomous navigation |
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| DOI： |
| 中文关键词: A*算法 柔绳拉伸机制 路径规划 AGV自主导航 |
| 英文关键词:A* algorithm flexible rope stretching mechanism path planning AGV navigation |
| 基金项目:福建省自然科学基金（2023J011437）、厦门市自然科学基金（2022FCX012503010090）项目资助 |
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| Author | Institution |
| Chen Xianyi | 1.School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China;
2.Xiamen Key Laboratory of Robotics Systems and Digital Manufacturing, Xiamen 361024, China |
| Yuan Weifeng | 1.School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China;
2.Xiamen Key Laboratory of Robotics Systems and Digital Manufacturing, Xiamen 361024, China |
| Luo Xinghuan | 1.School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China;
2.Xiamen Key Laboratory of Robotics Systems and Digital Manufacturing, Xiamen 361024, China |
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| 摘要点击次数: 37 |
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| 中文摘要: |
| 在多障碍物的复杂环境中,传统A*算法在路径规划中存在冗余转向节点的问题,这既增加了路径的长度和复杂性,也不利于自动引导小车（AGV）的平稳行走。为此提出了一种基于柔绳拉伸机制的改进A*算法以减少路径节点、提升平滑性。首先,分析了柔绳拉伸的机制,并提取A*算法路径中的关键节点;其次,对非障碍受力点进行退化以减少冗余转向节点,依次拉伸受力点间的路径,简化路径、提高平滑度;最后,对改进后的A*算法进行仿真实验并在AGV上进行路径规划自主导航实验。仿真实验结果表明,采用柔绳拉伸机制改进后的A*算法转弯角度减少59.2%,拐点数量减少54.2%,路径长度减少11%,这大幅度的简化并平滑了路径。在AGV行走实验中,优化后的A*算法与传统A*算法相比,平均角速度和行驶转向角分别降低16%和33%,且平均行驶轨迹长度和耗时分别减少2.4%和4%。实验表明AGV在改进A*算法规划的路径上行走位姿变换的节点和姿态调整幅度都较小,行走的更加柔顺和高效。 |
| 英文摘要: |
| In complex environments characterized by multiple obstacles, the traditional A* algorithm in path planning presents the problem of redundant turning nodes. This not only increases path length and complexity but also hinders the smooth navigation of the AGV. To address these challenges, this study introduces an improved A* algorithm predicated on the tensile mechanism of a flexible rope, aimed at diminishing path nodes and augmenting trajectory smoothness. First, the mechanism of flexible rope stretching was analyzed, and critical nodes were extracted from the paths generated by the A* algorithm. Subsequently, the degeneration of non-obstacle force points was executed to minimize redundant steering nodes, followed by the sequential stretching of paths between force points, thereby streamlining the trajectory and enhancing smoothness. Ultimately, the refined A* algorithm underwent simulation experiments and was applied to AGVs for autonomous navigation path planning experiments. The simulation outcomes demonstrated that the A* algorithm, refined with the flexible rope stretching mechanism, achieved a 59.2% reduction in turning angles, a 54.2% decrease in the number of turning points, and an 11% reduction in path length, significantly simplifying and smoothing the trajectory. In the AGV navigation experiments, the optimized A* algorithm, when compared to the traditional A* algorithm, registered a 16% decrease in average angular velocity and a 33% reduction in driving turning angles, with average travel trajectory length and time reduced by 2.4% and 4%, respectively. Additionally, the average travel trajectory length and time spent are reduced by 2.4% and 4%, respectively. The experiments results show that the AGV experiences smaller node transformations and posture adjustments while following the paths planned by the improved A* algorithm, leading to smoother and more efficient movement. |
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