Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

You Wang, Ziyi Zou, Ziang Zhang, Xiaoqing Guan, Boyu Lin, Xiang Li, Guang Li
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Abstract

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A* to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots.
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基于贝塞尔曲线的自主轨迹规划(带曲率约束和片断式速度-时间优化
在本文中,我们提出了一种高效的轨迹规划算法,该算法基于带有曲率约束的贝塞尔曲线和片断速度-时间优化的路径平滑。我们使用混合 A* 生成粗糙路径,并通过膨胀路径构建安全走廊。然后,我们将平滑问题表述为具有片断贝塞尔曲线的非线性编程(NLP)。由于贝塞尔曲线的曲率约束比较困难,我们采用了具有特殊形式的四元贝塞尔曲线,并计算了最大曲率的闭式解,以简化最大曲率的表示。通过使用特殊的贝塞尔曲线,我们实现了换挡,轻松保证了路径的安全性、连续性和可行性。同时,我们在 PJSO 的基础上增加了时间变量,在可接受的时间增长范围内提高了轨迹质量,使时间和速度的分配更加合理。在各种环境下对类似汽车的机器人进行的仿真和实际实验证实,我们的算法可以为机器人生成平滑、可行和高质量的轨迹。
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