Optimization-Based Trajectory Planning for Autonomous Parking With Irregularly Placed Obstacles: A Lightweight Iterative Framework

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Li B., ACARMAN T., Zhang Y., Ouyang Y., Yaman C., Kong Q., ...Daha Fazla

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, cilt.23, sa.8, ss.11970-11981, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 8
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1109/tits.2021.3109011
  • Dergi Adı: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.11970-11981
  • Anahtar Kelimeler: Trajectory, Trajectory planning, Aerospace electronics, Planning, Kinematics, Optimal control, Numerical models, Autonomous parking, trajectory planning, collision avoidance, numerical optimal control, optimization, VEHICLES
  • Galatasaray Üniversitesi Adresli: Evet

Özet

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.