Reducing conservatism while ensuring safety poses great difficulties for real-time trajectory planning in uncertain and cluttered environments. If we view trajectory planning as an optimization problem, the non-convex collision avoidance constraints with uncertain obstacles make trajectory planning challenging and time-consuming. Disjunctive chance constraint-based methods have been one of the most popular stochastic tools for this problem, for they can provide a tighter bound and lead to less conservative trajectories compared with other methods. However, previous work on disjunctive chance constraint-based trajectory planning adopts mixed-integer programming which has exponential complexity. Different from existing work, we propose a new optimization-based trajectory planning method with chance constraints, which turns uncertain obstacles into bounding boxes with tight upper bound collision avoidance constraints. Then, with a proposed time-varying convex feasible sets (TVCFS) algorithm, the original non-convex optimization problem is transferred into a series of convex problems, which can meet real-time requirements. Since the planned trajectory may be dynamically infeasible, we consider vehicle kinematics and formulate an optimal control problem to further smooth the planned trajectory and obtain desired control inputs. Simulation tests demonstrate the effectiveness of the proposed method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Real-Time Optimization-Based Trajectory Planning Method in Dynamic and Uncertain Environments


    Beteiligte:
    Guo, Yuqing (Autor:in) / Xu, Huile (Autor:in) / Yao, Danya (Autor:in) / Li, Li (Autor:in)


    Erscheinungsdatum :

    20.09.2020


    Format / Umfang :

    566731 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-Time Spatial Trajectory Planning for Urban Environments Using Dynamic Optimization

    Ruof, Jona / Mertens, Max Bastian / Buchholz, Michael et al. | IEEE | 2023


    Q-Learning-Based Dynamic Drone Trajectory Planning in Uncertain Environments

    Bhamidipati, Subrahmanya Chandra / Maxwell, Adam / Pham, Emily et al. | IEEE | 2025



    Aircraft trajectory planning based on threat sources in uncertain environments

    Ji, Wanfeng / Li, Cheng / Zhang, Yaoqing | IEEE | 2024