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| 基于优化人工势场法的无人驾驶汽车路径规划研究 |
| Research on Path Planning of Driverless Vehicle Based on Optimized Artificial Potential Field Method |
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| DOI：10.16018/j.cnki.cn32-1650/n.202403010 |
| 中文关键词: 人工势场法 路径规划 无人驾驶汽车 模型预测算法 |
| 英文关键词: artificial potential field method path planning driverless car model prediction algorithm |
| 基金项目: |
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| 摘要点击次数: 45 |
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| 中文摘要: |
| 针对传统人工势场法在车辆路径规划中存在目标不可达、局部最优等问题，通过优化障碍物模型对传统人工势场算法进行了优化，提高所规划路径的准确性和平滑性；然后结合模型预测控制算法对无人驾驶汽车行驶规划路径进行跟踪验证。结果表明: 优化后的模型可以生成一条准确平滑的路径；模型预测算法可以较准确地实现轨迹跟踪，即在换道工况时轨迹跟踪误差小于0. 20 m，在理想误差范围之内，超车工况下的跟踪误差为-0. 4~0. 6 m，既可以实现较为理想地超车，又能保证车辆行驶的稳定性、安全性。 |
| 英文摘要: |
| The traditional artificial potential field method has some problems in vehicle path planning, such as unattainable target and local optimization. The traditional artificial potential field algorithm is optimized by optimizing the obstacle model to improve the accuracy and smoothness of the planned path. Then combined with the model predictive control algorithm, the driving planning path of driverless car is tracked and verified. The results show that the optimized model can generate an accurate and smooth path. The model prediction algorithm can accurately realize trajectory tracking, that is, the trajectory tracking error is less than 0. 20 m under lane changing conditions, and within the ideal error range, the tracking error under overtaking conditions is -0. 4~0. 6 m, which can not only achieve ideal overtaking, but also ensure the stability and safety of vehicle driving. |
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