Efficient optimization-based trajectory planning
CoRR(2023)
摘要
This study proposes a unified optimization-based planning framework that
addresses the precise and efficient navigation of a controlled object within a
constrained region, while contending with obstacles. We focus on handling two
collision avoidance problems, i.e., the object not colliding with obstacles and
not colliding with boundaries of the constrained region. The object or obstacle
is denoted as a union of convex polytopes and ellipsoids, and the constrained
region is denoted as an intersection of such convex sets. Using these
representations, collision avoidance can be approached by formulating explicit
constraints that separate two convex sets, or ensure that a convex set is
contained in another convex set, referred to as separating constraints and
containing constraints, respectively. We propose to use the hyperplane
separation theorem to formulate differentiable separating constraints, and
utilize the S-procedure and geometrical methods to formulate smooth containing
constraints. We state that compared to the state of the art, the proposed
formulations allow a considerable reduction in nonlinear program size and
geometry-based initialization in auxiliary variables used to formulate
collision avoidance constraints. Finally, the efficacy of the proposed unified
planning framework is evaluated in two contexts, autonomous parking in
tractor-trailer vehicles and overtaking on curved lanes. The results in both
cases exhibit an improved computational performance compared to existing
methods.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要