为多层场景车辆规划安全轨迹，提升复杂环境下的路径效率与收敛速度。
Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios
- 基于广义维诺图与多目标算法选择楼层出口，优化任务分配。
- 采用优化方法生成高质量轨迹，支持快速收敛的分层规划框架。
- 设计关联约束计算减少障碍物限制，适合高密度多层场景应用。
自主地面车辆（AGV）的轨迹规划是智能交通系统中的研究热点。本文提出一种面向多层场景的轨迹规划框架，包含任务规划与轨迹规划两个模块。任务规划模块基于广义维诺图（GVD）与多目标算法，实现各楼层出口的策略性选择。轨迹规划模块采用基于优化的方法生成高质量轨迹，并设计了预热启动的分层规划框架以保证快速收敛。针对复杂障碍物约束，提出一种关联约束计算方法，有效降低轨迹规划中的约束复杂度。通过仿真验证了所提框架的可行性与有效性。
原文摘要 · Abstract (English)
The development of trajectory planning strategies for autonomous ground vehicles (AGVs) represents a prevailing research interest within the domain of intelligent transportation systems. This paper introduces a trajectory planning framework tailored for multi-floor scenarios. The framework consists of two main modules: the task planning module and the trajectory planning module. The task planning module involves a strategic selection phase, where a task planning strategy based on generalized voronoi diagrams (GVD) and multi-objective algorithms is proposed to select the floor exits for each floor. The trajectory planning module utilizes optimization-based methods to generate high-quality trajectories, and a warm-started hierarchical planning framework is designed to ensure rapid convergence. Additionally, for handling complex obstacle constraints, a correlation constraint calculation method is designed for reducing obstacle constraints in trajectory planning. Finally, the feasibility and effectiveness of the proposed framework are verified through simulations.
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