Motion Planning by Search in Derivative Space and Convex Optimization with Enlarged Solution Space - Robotics Institute Carnegie Mellon University

Motion Planning by Search in Derivative Space and Convex Optimization with Enlarged Solution Space

Jialun Li, Xiaojia Xie, Qin Lin, Jianping He, and John M. Dolan
Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 13500 - 13507, October, 2022

Abstract

To efficiently generate safe trajectories for an autonomous vehicle in dynamic environments, a layered motion planning method with decoupled path and speed planning is widely used. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given a planned path. The main challenges lie in the optimization in a non-convex space and the trade-off between safety, comfort, and efficiency. First, this work proposes to conduct a search in second-order derivative space for generating a comfort-optimal reference trajectory. Second, by combining abstraction and refinement, an algorithm is proposed to construct a convex feasible space for optimization. Finally, a piecewise Bézier polynomial optimization approach with trapezoidal corridors is presented, which theoretically guarantees safety and significantly enlarges the solution space compared with the existing rectangular corridors-based approach. We validate the efficiency and effectiveness of the proposed approach in simulations.

BibTeX

@conference{Li-2022-134812,
author = {Jialun Li and Xiaojia Xie and Qin Lin and Jianping He and John M. Dolan},
title = {Motion Planning by Search in Derivative Space and Convex Optimization with Enlarged Solution Space},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2022},
month = {October},
pages = {13500 - 13507},
keywords = {autonomous driving, motion planning, convex optimization, Bézier polynomials},
}