基于RRT*的自动驾驶汽车高效路线规划,采用可变概率策略和人工潜力现场方法.
Fazhan Tao1,2, Zhaowei Ding1,3, Zhigao Fu4
1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China.
Scientific reports
|October 21, 2024
概括
本研究介绍了一种改进的A-RRT*算法,用于自动驾驶路径规划. 它通过将目标偏差采样与改进的人工潜力现场方法相结合,提高了融合速度和路径质量.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 自动驾驶技术在很大程度上依赖于路径规划,以提高安全性.
- 快速探索随机树恒星 (RRT*) 算法被广泛使用,但面临着缓慢的融合和不可预测的搜索模式等挑战.
- 解决这些局限性对于推进自主导航至关重要.
研究的目的:
- 提出一个改进的RRT*算法,称为改进的A-RRT*,以实现更高效,更可靠的路径规划.
- 在自动驾驶应用中改进RRT*算法在自动驾驶应用中的融合速度和路径质量.
主要方法:
- 引入了可变概率目标偏差策略,以引导随机树扩张向目标.
- 改进了人工潜力场 (APF) 方法,在路径生成过程中避免局部最佳值.
- 将增强的APF与RRT*集成,使用目标引力场和阻碍力驱逐力来引导树木生长.
主要成果:
- 与标准的RRT*相比,改进的A-RRT*算法在汇速度方面取得了显著的优化.
- 实验结果显示,拟议的算法产生的路径质量优越.
- 改进的算法有效地引导随机树向目标区域,同时避免障碍.
结论:
- 改进的A-RRT*算法有效地解决了标准RRT*算法的局限性.
- 这种增强的方法为自动驾驶中的安全和高效路径规划提供了有希望的解决方案.
- 目标偏差采样和改进的APF的结合导致了在复杂环境中更好的性能.
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