Faucet milling robot arm path planning based on ant colony algorithm
-
摘要: 为了提高机器人对水龙头铣削的加工质量及效率,提出一种路径最短的机器人铣削路径规划方法。以UR10e机器人为模型,分析了机器人的工作原理,同时,建立机器人的运动仿真模型。然后针对机器人铣削的水龙头,采用激光三维扫描,精准获取其点云图,并采用改进CC截面法获取1.75%机器人铣削路径。在保证铣削轮廓的前提下,按照曲率采样的方法,简化加工路径点。再结合遗传算法不断进化迭代,搜索铣削路径最短的最优解。通过仿真与实际测试,在最优轨迹优化方面,遗传算法在路径有约1%的正向优化效果。对于一些非关键部位优化了轨迹,提高水龙铣削加工的效率和精度,证明该方法具有可行性。Abstract: In order to improve the machining quality and efficiency of the robot for faucet milling, a robot milling path planning method with the shortest path is proposed. Using the UR10e robot as a model, the working principle of the robot is analyzed, and at the same time, the motion simulation model of the robot is established. Then, for the robot milled faucet, laser 3D scanning is used to accurately obtain its point cloud map, and the improved CC section method is used to obtain 1.75% robot milling paths. The processing path points are simplified according to the curvature sampling method under the premise of ensuring the milling profile. Then combined with the genetic algorithm continuous evolutionary iteration, the optimal solution of the shortest milling path is searched. Through simulation and actual test, the genetic algorithm has about 1% positive optimization effect on the path in terms of optimal trajectory optimization. The trajectory is optimized for some non-critical parts to improve the efficiency and accuracy of water dragon milling processing, which proves that the method is feasible.
-
Key words:
- robot /
- faucet /
- path planning /
- genetic algorithm
-
表 1 优化结果
路径 路径1 路径2 路径3 优化前/mm 425.091 8 430.556 5 424.706 5 优化后/mm 423.913 6 429.233 5 423.494 4 首次最优解迭代次数 1 811 3 579 2 426 -
[1] 龙樟, 李显涛, 帅涛, 等. 工业机器人轨迹规划研究现状综述[J]. 机械科学与技术, 2021, 40(6): 853-862. doi: 10.13433/j.cnki.1003-8728.20200132 [2] Elias K X. Time-optimal trajectory planning for hyper-redundant manipulators in 3D workspaces[J]. Robotics and Computer-Integrated Manufacturing, 2018, 50: 286-298. doi: 10.1016/j.rcim.2017.10.005 [3] John G, Alberto O, Ernesto S. Energy-optimal trajectory planning for robot manipulators with holonomic constraints[J]. Systems & Control Letters, 2012, 61(2): 279-291. [4] Menasri R, Nakib A, Daachi B, et al. A trajectory planning of redundant manipulators based on bilevel optimization[J]. Applied Mathematics and Computation, 2015, 250: 934-947. doi: 10.1016/j.amc.2014.10.101 [5] Lin H I. A fast and unified method to find a minimum jerk robot joint trajectory using particle swarm optimization[J]. Journal of Intelligent and Robotic System, 2014, 75(3/4): 379-392. [6] 丁阳, 顾寄南. 基于 QPSO 算法的机器人时间最优轨迹规划[J]. 自动化与仪器仪表, 2020(1): 16-19. [7] 杨锦涛, 姜文刚, 林永才. 工业机器人冲击最优的轨迹规划算法[J]. 科学技术与工程, 2014, 14(28): 64-69. doi: 10.3969/j.issn.1671-1815.2014.28.013 [8] 顾益铭. 基于能量最优六自由度植发机械臂轨迹规划研究[D]. 青岛: 青岛科技大学, 2019. [9] Huai C F, Shi G Y. Research on trajectory planning and simulation of vehicle exterior wall grinding robot[C]. IEEE Chinese Control and Decision Conference, 2018: 852-857. [10] Kharidege A, Yajun Z. A practical approach for automated polishing system of free-form surface path generation based on industrial arm robot[J]. The International Journal of Advanced Manufacturing Technology, 2017, 93(9): 3921-3934. [11] Craig J J. Introduction to robotics: mechanics and control[M]. 3th Edition. China Machine Press, 2006. [12] 张斌, 黄彬彬, 宋亚勤, 等. 基于改进CC路径截面线法的机器人铣削加工刀具轨迹生成方法[J]. 机床与液压, 2016, 44(3): 35-39. doi: 10.3969/j.issn.1001-3881.2016.03.009 [13] 郝晗, 文志强. 基于改进型遗传算法的六自由度机械臂轨迹优化[J]. 组合机床与自动化加工技术, 2020(12): 56-59. doi: 10.13462/j.cnki.mmtamt.2020.12.014 [14] 曾关平, 王直杰. 基于改进遗传算法的机械臂时间最优轨迹规划[J]. 科技创新与应用, 2020(22): 6-9. [15] Liu J , Luo Q . Modeling and simulation of robotic arm in MATLAB for industrial applications[C]. 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2019.