用分层优化自动规划汽车喷漆机器人路径,省时且效果媲美人工。
Vehicle Painting Robot Path Planning Using Hierarchical Optimization
- 分层优化:上层解车辆路径问题,下层做精细路径规划。
- 三款量产车测试验证,自动路径满足所有约束且质量接近人工设计。
- 灵活适配喷漆特有约束,适合工业自动化场景的工程师使用。
在汽车生产工厂中,多台机器人需同步对传送线上移动的车体进行喷漆。喷漆路径的设计涉及将车身区域分配给各机器人,并确定每台机器人的喷漆顺序,这一过程长期依赖工程师手动完成,耗时费力,亟需自动化以缩短设计周期。由于喷漆工艺具有独特约束,传统机器人路径规划方法(如焊接应用)难以直接适用。本文将喷漆路径设计建模为分层优化问题:上层子问题类似车辆路径问题(VRP),下层子问题则为详细路径规划。该框架允许在不同层级采用不同优化算法,并通过变量表示、约束设计、修复算子及初始化策略灵活处理喷漆工艺特有约束。针对三款商用汽车模型的实验表明,所提方法能自动生成满足全部约束的喷漆路径,其质量与工程师手工设计相当。
原文摘要 · Abstract (English)
In vehicle production factories, the vehicle painting process employs multiple robotic arms to simultaneously apply paint to car bodies advancing along a conveyor line. Designing paint paths for these robotic arms, which involves assigning car body areas to arms and determining paint sequences for each arm, remains a time-consuming manual task for engineers, indicating the demand for automation and design time reduction. The unique constraints of the painting process hinder the direct application of conventional robotic path planning techniques, such as those used in welding. Therefore, this paper formulates the design of paint paths as a hierarchical optimization problem, where the upper-layer subproblem resembles a vehicle routing problem (VRP), and the lower-layer subproblem involves detailed path planning. This approach allows the use of different optimization algorithms at each layer, and permits flexible handling of constraints specific to the vehicle painting process through the design of variable representation, constraints, repair operators, and an initialization process at the upper and lower layers. Experiments with three commercially available vehicle models demonstrated that the proposed method can automatically design paths that satisfy all constraints for vehicle painting with quality comparable to those created manually by engineers.
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