引用本文:张俊,刘平,朴昌浩.障碍物无规则运动下启发式Gauss配点参数化连续避障规划[J].控制理论与应用,2024,41(10):1873~1881.[点击复制]
ZHANG Jun,LIU Ping,PIAO Chang-hao.Heuristic Gauss allocation points parameterization consecutive obstacle avoidance planning under obstacle irregular motion[J].Control Theory and Technology,2024,41(10):1873~1881.[点击复制]
障碍物无规则运动下启发式Gauss配点参数化连续避障规划
Heuristic Gauss allocation points parameterization consecutive obstacle avoidance planning under obstacle irregular motion
摘要点击 3249  全文点击 37  投稿时间：2022-10-16  修订日期：2024-06-05
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DOI编号  10.7641/CTA.2023.20901
  2024,41(10):1873-1881
中文关键词  自动驾驶  轨迹规划  无规则运动  启发式策略  Gauss配点
英文关键词  automatic driving  trajectory planning  irregular motion  heuristic strategy  Gauss allocation points
基金项目  国家重点研发计划项目(2022YFE0101000), 重庆市自然科学基金面上项目(CSTB2022NSCQ–MSX0355)资助.
作者单位E-mail
张俊 重庆邮电大学自动化学院 s180302001@stu.cqupt.edu.cn 
刘平 重庆邮电大学自动化学院  
朴昌浩* 重庆邮电大学自动化学院 piaoch@cqupt.edu.cn 
中文摘要
      针对障碍物无规则运动下自动驾驶车辆(SDV)的避障轨迹规划, 本文提出一种结合模型约束简化和启发式非均匀Gauss配点参数化的轨迹规划算法. 首先, 结合SDV运动学方程和约束条件建立了SDV连续避障轨迹规划的问题; 然后, 在高斯伪谱法离散化算法框架下, 采用Gauss配点对状态变量进行离散化; 进一步, 提出了启发式Gauss配点初始化策略, 以此提升SDV求解速度进而实现避障实时规划; 同时, 对经过约束处理后的轨迹进行验证, 以此保证规划轨迹的安全性; 最后, 在车辆模型上针对不同障碍物运动场景进行仿真测试, 验证提出方法的有效性和实时性. 结果显示, 本文方法可以有效进行避障轨迹规划, 并确保轨迹的安全性, 轨迹规划平均求解耗时维持在25 ms左右, 显示出本文方法在实时避障规划的效能和实际应用价值.
英文摘要
      Aiming at the obstacle avoidance trajectory planning of self-driving vehicle (SDV) under the irregular movement of obstacles, this article proposes a trajectory planning algorithm combining model constraint simplification and heuristic non-uniform Gauss allocation parameterization. First, the SDV continuous trajectory planning for problem of obstacle avoidance is established by combining the SDV kinematics equations and constraints; Then, the Gauss collocation is employed to discretize the state variables under the framework of the Gaussian pseudospectral discretization algorithm; Accordingly, a heuristic Gauss configuration initialization strategy is proposed to improve the SDV solution efficiency and realize real-time planning for obstacle avoidance; Meanwhile, verification is carried out after the obtained trajectory through constraint processing to ensure the safety of the planned trajectory; Finally, simulation tests are conducted on the vehicle model in different obstacle motion scenes to verify the effectiveness and real-time performance of the proposed method. The results show that this method can effectively generate trajectory planning for obstacle avoidance and ensure the trajectory security. Meanwhile, the average solving time of the trajectory planning method in this paper is about 25 ms, revealing the effectiveness and practical application value of the real-time obstacle avoidance planning method.