T$^{\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles

TL;DR

T$^{\star}$-Lite is a fast, sampling-based motion planning algorithm for autonomous vehicles that optimizes for time and risk, enabling safer and quicker path generation by incorporating vehicle speed and heading in a four-dimensional space.

Contribution

It introduces T$^{\star}$-Lite, a significantly faster, sampling-based version of T$^{\star}$ that integrates GMDM for efficient kinodynamic planning in four-dimensional space.

Findings

Reduces computational time compared to grid-based methods.

Provides a balance between solution quality and planning speed.

Enables dynamic speed and heading adjustments for safety and efficiency.

Abstract

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