Abstract:

Automatic inspection of concrete bridge health based on wall-climbing robot is an effective way to promote intelligent bridge management and maintenance， moreover reasonable path planning is particularly important for the robot to obtain comprehensive detection data. Aiming at the engineering practical problem of weight limitation of the wall-climbing robot power supply and the difficulty of energy supplement during inspection， the inspection scenarios of bridge components such as main beams and high piers were fully considered， the energy consumption index was taken as the objective function of performance evaluation optimization and corresponding constraint conditions were established， and a full coverage path planning evaluation model was proposed. An Improved Grey Wolf Optimization （IGWO） algorithm was proposed to solve the problem that traditional Grey Wolf Optimization （GWO） algorithm is prone to fall into local optimum. The IGWO algorithm improved the characteristics of initial gray wolf population which was difficult to maintain relatively uniform distribution in the search space by K-Means clustering. The nonlinear convergence factor was used to improve the local development ability and global search performance of the algorithm. Combining with the idea of individual superiority of particle swarm optimization， the position updating formula was improved to enhance the model solving ability of the algorithm. Algorithm simulation and comparison experiment results show that IGWO has better stability compared with GWO， Different Evolution （DE） and Genetic Algorithm （GA）， IGWO reduces energy consumption by 10.2% - 16.7%， decreases iterations by 19.3% - 36.9% and solving time by 12.8% - 32.3%， reduces path repetition rate by 0.23 - 1.91 percentage points， and reduces path length by 1.6% - 11.0%.

Key words: Grey Wolf Optimization (GWO), bridge inspection, wall-climbing robot, full coverage path planning, energy consumption model

摘要：

基于爬壁机器人对混凝土桥梁健康进行自动巡检是推动桥梁管养智能化的有效途径，而合理的路径规划对机器人全面获取检测数据尤为重要。针对爬壁机器人电源重量限制与巡检时能源补充困难这一工程实际问题，充分考虑主梁、高墩等桥梁构件巡检场景，将能量消耗指标作为性能评价优化目标函数并建立相应约束条件，进而提出全覆盖路径规划评价模型。针对传统灰狼优化（GWO）算法易陷入局部最优的不足，提出一种改进的灰狼优化（IGWO）算法。IGWO算法通过K-means聚类改善了灰狼初始种群在搜索空间难以保持相对均匀分布的特性；以非线性收敛因子提高算法局部开发能力和全局搜索性能；结合粒子群算法个体优越性的思想对位置更新公式进行改进，提升算法的模型求解能力。仿真对比实验结果表明，IGWO算法相较于GWO、差分进化（DE）与遗传算法（GA）等全局优化算法，稳定性更好，能耗降低了10.2%~16.7%，迭代次数与求解时间分别减少了19.3%~36.9%和12.8%~32.3%，路径重复率降低了0.23~1.91个百分点，同时路径长度缩短1.6%~11.0%。

关键词: 灰狼优化, 桥梁检测, 爬壁机器人, 全覆盖路径规划, 能耗模型