Tstar-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

In this paper, we develop a new algorithm, called Tstar-Lite, that enables fast time-risk optimal motion planning for variable-speed autonomous vehicles. The Tstar-Lite algorithm is a significantly faster version of the previously developed Tstar algorithm. Tstar-Lite uses the novel time-risk cost function of Tstar; 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 Tstar 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, Tstar-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, Tstar-Lite is developed using the RRTstar motion planner, but adaptation to other motion planners is straightforward and depends on the needs of the planner