基于优化的多楼层场景自动驾驶地面车辆安全轨迹规划
Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios
AI总结：
提出一种针对多楼层场景的轨迹规划框架，包括基于广义Voronoi图和多目标算法的任务规划模块，以及采用优化方法和热启动分层规划生成高质量轨迹的轨迹规划模块，并通过仿真验证可行性。
摘要：
自动驾驶地面车辆（AGV）轨迹规划策略的发展是智能交通系统领域的一个普遍研究兴趣。本文介绍了一种针对多楼层场景的轨迹规划框架。该框架由两个主要模块组成：任务规划模块和轨迹规划模块。任务规划模块涉及一个策略选择阶段，其中提出了一种基于广义Voronoi图（GVD）和多目标算法的任务规划策略，用于选择每层的楼层出口。轨迹规划模块利用基于优化的方法生成高质量轨迹，并设计了热启动分层规划框架以确保快速收敛。此外，为了处理复杂的障碍物约束，设计了一种关联约束计算方法，用于减少轨迹规划中的障碍物约束。最后，通过仿真验证了所提框架的可行性和有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
