Optimization-based Safe Trajectory Planning for Autonomous Ground Vehicle in Multi-Floor Scenarios
For researchers in autonomous vehicle navigation, this work addresses the specific problem of multi-floor trajectory planning but offers only incremental improvements with no quantitative results.
This paper proposes a trajectory planning framework for autonomous ground vehicles in multi-floor scenarios, using GVD and multi-objective algorithms for task planning and optimization-based methods for trajectory planning. Simulations verify feasibility and effectiveness, but no concrete performance numbers are provided.
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.