Hierarchical path planning for industrial inspection vehicles based on lightweight topological graphs
Peng Yuan, Jian Wang, Hai Tao Xu, Hong Bo Du, Lu Chen Wang, Hua Wei LiangPurpose
This paper aims to address the dependence on high-definition maps, complex road networks and temporary obstacles encountered in autonomous inspection tasks in industrial parks.
Design/methodology/approach
A hierarchical path planning framework based on lightweight topological graphs is proposed. At the global planning layer, a directed topological road network is constructed from pre-collected road trajectories, and a global reference loop is generated using an improved Johnson-cycle method (I-JCycle) under a start-node strong-connectivity constraint. At the local planning layer, candidate trajectories are generated using Hermite curves and evaluated through a cost-based selection mechanism for obstacle avoidance and safe stopping.
Findings
Real-vehicle experiments conducted in an industrial park show that the proposed method can stably generate high-coverage reference loops without relying on high-definition maps. Compared with representative baseline methods, the proposed framework achieves a better balance between route coverage, loop compactness and planning efficiency at the global planning layer, while providing smoother and safer obstacle avoidance with better reference-loop consistency at the local planning layer.
Research limitations/implications
The method has been validated only in a limited number of representative industrial-park scenarios and assumes that the lightweight topological road network remains broadly valid during operation. Larger-scale validation under road closures and long-term environmental changes is still needed.
Practical implications
The proposed method can reduce reliance on high-definition maps and provide a feasible planning solution for low-speed inspection vehicles in semi-structured environments such as industrial parks and factories, while supporting both repeated loop inspection and local obstacle avoidance.
Social implications
The proposed method may support safer and more consistent autonomous inspection in industrial environments, thereby reducing reliance on frequent manual patrols in repetitive tasks and contributing to more efficient intelligent operation and maintenance.
Originality/value
This paper presents a task-oriented global-local hierarchical path planning framework for low-speed industrial inspection vehicles. The framework combines loop-oriented global planning on a lightweight directed topological graph with reference-consistent local obstacle avoidance. It reduces dependence on high-definition maps and is validated through real-vehicle experiments in an industrial park.