PDDL Task Planning for Tunnel Spraying Based on a Multivariate Coating Accumulation Model - Yan Huang; Wenzheng Shi; Xin Sui; Chunyang Liu; Kai Xu (Applied Sciences)
To address the challenges of low automation in tunnel wet-spraying jumbos and the heavy reliance on manual expertise for ensuring the spraying quality, this study proposes a novel task planning method for tunnel spraying operations. First, the tunnel surface to be sprayed is aligned with the designed contour using a vehicle navigation method, enabling the estimation of the overbreak and underbreak volumes. These volumes are then utilized to hierarchically plan the spraying tasks (e.g., patching, filling, and surface smoothing). A concrete coating thickness prediction method is developed, incorporating static and dynamic coating accumulation models with key process parameters—spraying flow rate Q, air pressure P, and spraying distance H—as independent variables. Based on the required thickness for each task layer, operational parameters such as the spraying duration t and nozzle movement speed v are optimized. By analyzing the spray gun action combinations and integrating hierarchical task planning with parameter optimization, a Planning Domain Definition Language (PDDL) domain file and problem file are designed to generate the spray gun action sequences and paths via a planner. The experimental results demonstrate that the overbreak volume on the sprayed tunnel surface is reduced to approximately 3 cm after applying the planned sequences. The proposed method autonomously generates the task hierarchies and the corresponding spray gun actions based on the 3D morphology of the tunnel surface, effectively ensuring the spraying quality while significantly reducing the dependence on manual intervention. This approach provides a practical solution for enhancing automation and precision in tunnel spraying operations.