| 禾家辉,金晶.面向复杂曲面视觉检测的覆盖路径规划[J].电子测量与仪器学报,2026,40(2):152-163 |
| 面向复杂曲面视觉检测的覆盖路径规划 |
| Coverage path planning for visual inspection of complex curved surfaces |
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| DOI： |
| 中文关键词: 复杂曲面视觉检测 覆盖路径规划 多目标优化 IGWO-WOA 视点规划 |
| 英文关键词:visual inspection of complex surfaces coverage path planning multi-objective optimization IGWO-WOA viewpoint planning |
| 基金项目:国家自然科学基金面上项目（12373107）、黑龙江省“揭榜挂帅”科技攻关项目（2023ZXJ01A01）资助 |
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| 中文摘要: |
| 针对汽车方向盘等具有复杂曲面的工件在自动化视觉检测中的路径规划需求，传统“先视点后路径”的解耦规划方法易陷入局部最优，难以兼顾覆盖率与效率。为此，提出一种视点与路径协同优化的覆盖路径规划方法。首先，基于目标点云模型的轴对齐包围盒进行空间细分，并结合随机偏移生成视点位置；通过球坐标系采样确定视点位姿，构建冗余视点集合。其次，建立融合景深、视场角、可见性及遮挡约束的视锥体覆盖评估模型，量化各视点的有效覆盖能力。最后，提出改进的混合灰狼优化算法-鲸鱼优化算法（IGWO-WOA），融合混沌映射初始化、反向学习策略、动态收敛因子及鲸鱼优化算法中的螺旋狩猎机制，实现视点选择与访问顺序的多目标组合优化。实验结果表明，在两种复杂方向盘模型上，所提方法相较传统的群智能算法，路径长度分别缩短 20.6% 与 11.5%，覆盖率提升至 99.7% 与 99.86%，全程实现零碰撞，路径质量显著优于对比方法。 |
| 英文摘要: |
| To address the path planning requirements for automated visual inspection of workpieces with complex surfaces—such as automotive steering wheels—conventional decoupled approaches that first select viewpoints and subsequently plan paths are prone to local optima and struggle to simultaneously achieve high coverage and computational efficiency. To overcome this limitation, a coverage path planning method featuring joint optimization of viewpoints and trajectories is proposed. First, the axis-aligned bounding box of the target point cloud model is spatially subdivided, and candidate viewpoint positions are generated by applying random offsets to the centroids of the resulting subregions. Viewpoint orientations are then determined through sampling in spherical coordinates, yielding a redundant set of candidate viewpoints. Second, a frustum-based coverage evaluation model is established, incorporating constraints on depth of field, field of view, surface visibility, and self-occlusion to quantitatively assess the effective coverage capability of each candidate viewpoint. Finally, an enhanced hybrid grey wolf optimizer-whale optimization algorithm (IGWO-WOA) is introduced, integrating chaotic map-based initialization, opposition-based learning, a dynamic convergence factor, and the spiral hunting mechanism from the whale optimization algorithm to enable multi-objective co-optimization of viewpoint selection and visiting sequence. Experimental results on two complex steering wheel models demonstrate that, compared with conventional swarm intelligence algorithms, the proposed method reduces path length by 20.6% and 11.5%, achieves coverage rates of 99.7% and 99.86%, respectively, and ensures collision-free execution throughout the inspection process, thereby delivering significantly superior trajectory quality. |
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