As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, there is no efficient trajectory planning solution capable of spatial-temporal joint optimization due to nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a versatile and real-time trajectory optimization method that can generate a high-quality feasible trajectory using a full vehicle model under arbitrary constraints. By leveraging the differential flatness property of car-like robots, we use flat outputs to analytically formulate all feasibility constraints to simplify the trajectory planning problem. Moreover, obstacle avoidance is achieved with full dimensional polygons to generate less conservative trajectories with safety guarantees, especially in tightly constrained spaces. We present comprehensive benchmarks with cutting-edge methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes as open-source packages with the purpose for the reference of the research community.
翻译:作为自主驾驶系统的核心部分,运动规划得到了学术界和工业界的广泛关注,然而,没有有效的轨迹规划解决方案,由于非光学动态,特别是在存在无结构环境和动态障碍的情况下,能够实现空间-时际联合优化。为了缩小差距,我们建议采用多功能和实时轨迹优化方法,在任意限制下,利用完整的车辆模型,产生高质量的可行轨迹。通过利用汽车类机器人的差分平板特性,我们使用平板产出来分析制定所有可行性限制,以简化轨迹规划问题。此外,通过全方位多边形来避免障碍,以产生有安全保障的保守轨迹,特别是在严格受限制的空间。我们以尖端方法提出全面基准,表明拟议方法在效率和轨迹质量方面的重要性。现实世界实验将核查我们的算法的实用性。我们将发布我们的代码,作为开放源包,用于参考研究界。