Feasible Trajectory Generation for Autonomous Vehicles Based on Quartic Bézier Curve
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摘要: 对于实际的无人车系统来说,轨迹规划需要保证其规划出来的轨迹满足运动学约束、 侧滑约束以及执行机构约束.为了生成满足无人车初始状态约束、目标状态约束的局部可行轨迹,本文提出了一种基于四阶贝塞尔曲线的轨迹规划方法.在该方法中, 轨迹规划问题首先被分解为轨形规划及速度规划两个子问题.为了满足运动学约束、 初始状态约束、目标状态约束以及曲率连续约束,本文采用由3个参数确定的四阶贝塞尔曲线来规划轨迹形状.为了保证转向机构可行,本文进一步采用优化方法求解一组最优参数从而规划出曲率变化最小的轨线.对于轨线执行速度规划,为了满足速度连续约束、加速度连续约束、加速度有界约束以及目标状态侧滑约束,本文首先求解了可行的轨迹执行耗时区间,再进一步在该区间中求解能够保证任意轨迹点满足侧滑约束的耗时,最后再由该耗时对任意点速度进行规划.本文结合实际无人车的应用对轨迹搜索空间生成、道路行车模拟以及路径跟踪进行了仿真实验,并基于实际的环境数据进行了轨迹规划实验.Abstract: For practical autonomous vehicles, the generated trajectories should ensure the feasibility imposed by kinematic, dynamic and actuation. To generate a locally feasible trajectory from the initial state to the target state, a trajectory generation algorithm based on quartic Bzier curve is proposed. Firstly, the original problem is decomposed into shaping the trajectory and executing the shape. To satisfy the kinematic constraints, initial state and target state constraints and continuous curvature constraint, a quartic Bzier curve defined by 3 parameters is adopted to shape the trajectory. To further ensure the feasibility of steering, optimization is utilized to resolve a set of parameters to generate a trajectory that has a minimum curvature change. For velocity generation, an interval of executing time is firstly generated to ensure a continuous velocity, continuous acceleration, bounded acceleration and side-slip avoidance at the target state. Then, the executing time that could avoid side-slip at every point is resolved by adjusting the time. Finally, the executing velocity of the trajectory at each point is generated based on the executing time. To verify the algorithm, trajectory generation based on real environment data and simulations on search space generation, driving on road and path tracking are conducted.
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Key words:
- Trajectory generation /
- feasibility /
- autonomous vehicles /
- quartic Bé
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[1] Howard T M, Kelly A. Trajectory and spline generation for all-wheel steering mobile robots. In: Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China: IEEE, 2006. 4827-4832 [2] Yan Fei, Zhuang Yan, Bai Ming, Wang Wei. 3D outdoor environment modeling and path planning based on topology-elevation model. Acta Automatica Sinica, 2010, 36(11): 1493-1501(闫飞, 庄严, 白明, 王伟. 基于拓扑高程模型的室外三维环境建模与路径规划. 自动化学报, 2010, 36 (11): 1493-1501) [3] Chen Yang, Zhang Dao-Hui, Zhao Xin-Gang, Han Jian-Da. UAV 3D path planning based on IHDR autonomous-learning-framework. Robot, 2012, 34(5): 513-518(陈洋, 张道辉, 赵新刚, 韩建达. 基于IHDR自主学习框架的无人机3维路径规划. 机器人, 2012, 34 (5): 513-518) [4] [4] Kelly A, Nagy B. Reactive nonholonomic trajectory generation via parametric optimal control. The International Journal of Robotics Research, 2003, 22(7-8): 583-601 [5] [5] Howard T M, Kelly A. Optimal rough terrain trajectory generation for wheeled mobile robots. The International Journal of Robotics Research, 2007, 26(2): 141-166 [6] [6] Ferguson D, Howard T M, Likhachev M. Motion planning in urban environments: Part II. Intelligent robots and systems. In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice: IEEE, 2008. 1070-1076 [7] [7] Howard T M, Green C J, Kelly A. Receding horizon model-predictive control for mobile robot navigation of intricate paths. Field and Service Robotics. Berlin, Heidelberg: Springer, 2010, 62: 69-78 [8] [8] Howard T M, Pivtoraiko M, Knepper R A, Kelly A. Model-predictive motion planning: several key developments for autonomous mobile robots. IEEE Robotics and Automation Magazine, 2014, 21(1): 64-73 [9] [9] Ferguson D, Howard T M, Likhachev M. Motion planning in urban environments. Journal of Field Robotics, 2008, 25(11-12): 939-960 [10] Laumond J P, Jacobs P E, Taix M, Murray R M. A motion planner for nonholonomic mobile robots. IEEE Transactions on Robotics and Automation, 1994, 10(5): 577-593 [11] Scheuer A, Fraichard T. Continuous-curvature path planning for car-like vehicles. In: Proceedings of the 1997 IEEE/ RSJ International Conference on Intelligent Robots and Systems. Grenoble: IEEE, 1997. 2: 997-1003 [12] Gmez-Bravo F, Cuesta F, Ollero A, Viguria A. Continuous curvature path generation based on -spline curves for parking manoeuvres. Robotics and Autonomous Systems, 2008, 56(4): 360-372 [13] Li Y B, Xiao J. On-line planning of nonholonomic trajectories in crowded and geometrically unknown environments. In: Proceedings of the 2009 IEEE International Conference on Robotics and Automation. Kobe: IEEE, 2009. 3230- 3236 [14] Jolly K G, Sreerama K R, Vijayakumar R. A bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robotics and Autonomous Systems, 2009, 57(1): 23-33 [15] Choi J, Curry R E, Elkaim G H. Curvature-continuous trajectory generation with corridor constraint for autonomous ground vehicles. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC). Atlanta, GA: IEEE, 2010. 7166-7171 [16] Morten K, Nils A, Ole R. Generic trajectory representation and trajectory following for wheeled robots. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Hong Kong, China: IEEE, 2014. 4073-4080 [17] Kelly A, Stentz A. Rough terrain autonomous mobility Part 1: a theoretical analysis of requirements. Autonomous Robots, 1998, 5(2): 129-161 [18] Duncan M. Applied Geometry for Computer Graphics and CAD. Springer, 2005.
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