论文标题
t $^{\ star} $ - lite：多速自动驾驶汽车的快速时风险最佳运动计划算法
T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles
论文作者
论文摘要
在本文中，我们开发了一种新算法，称为T $^{\ Star} $ - Lite，该算法可以为可变速度自动驾驶汽车提供快速的时风险最佳运动计划。 T $^{\ star} $ - Lite算法是先前开发的T $^{\ Star} $算法的更快版本。 t $^{\ star} $ - Lite使用T $^{\ Star} $的新颖时风险成本函数;但是，它不是基于网格的方法，而是使用基于最佳采样的运动计划者。此外，它利用了最近开发的广义多速Dubins运动模型（GMDM）进行样本到样本的动力学运动计划。基于样本的方法和GMDM显着减轻了T $^{\ star} $的计算负担，同时提供合理的解决方案质量。样品点是从由两个位置坐标以及车辆标题和速度组成的四维配置空间中得出的。具体而言，T $^{\ Star} $ - Lite使运动计划者可以根据其靠近障碍物的速度和方向来选择更快，更安全的路径。在本文中，使用rrt $^{\ star} $运动计划者开发t $^{\ star} $ - lite，但对其他运动计划者的改编很简单，取决于计划者的需求
In this paper, we develop a new algorithm, called T$^{\star}$-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The T$^{\star}$-Lite algorithm is a significantly faster version of the previously developed T$^{\star}$ algorithm. T$^{\star}$-Lite uses the novel time-risk cost function of T$^{\star}$; however, instead of a grid-based approach, it uses an asymptotically optimal sampling-based motion planner. Furthermore, it utilizes the recently developed Generalized Multi-speed Dubins Motion-model (GMDM) for sample-to-sample kinodynamic motion planning. The sample-based approach and GMDM significantly reduce the computational burden of T$^{\star}$ while providing reasonable solution quality. The sample points are drawn from a four-dimensional configuration space consisting of two position coordinates plus vehicle heading and speed. Specifically, T$^{\star}$-Lite enables the motion planner to select the vehicle speed and direction based on its proximity to the obstacle to generate faster and safer paths. In this paper, T$^{\star}$-Lite is developed using the RRT$^{\star}$ motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner
