摘要
飞机蒙皮、船舶舱体、高铁车身等大型复杂部件高效高品质制造是航空航天、海洋舰船、轨道交通等领域重大装备发展的根基.大型复杂部件具有尺寸超大、型面复杂等特点,传统的人工、单机制造面临着效率低、一致性差、空间有限等问题,多机器人具有高鲁棒性、高效性等优点,为大型复杂部件制造提供了良好的制造基础.任务分配与运动规划是多机器人制造系统的决策中枢,其性能影响整个系统的运行效率.考虑到重大装备部件制造任务分配与运动规划过程中任务工序多、冲突干涉多、精度需求高等挑战,本文首先对复杂环境下多机器人任务分配与运动规划的重要性进行了说明;然后阐述了目前主要的任务分配与运动规划方法,包括其在智能制造领域下的应用;在此基础上,对现阶段复杂场景下任务分配和运动规划存在的问题进行了分析,并使用强化学习与混合优化算法等方法提出了解决思路;最后对重大装备大型复杂部件制造过程多机器人任务分配和动态规划技术及应用的发展进行了总结与展望.
Abstract
Large-scale complex components such as aircraft skin,ship cabin body,and high-speed rail body are manufactured with high-efficiency and high-quality,which is the foundation for developing major equipment such as aerospace,marine vessels,and rail transportation.Large-scale complex components have the characteristics of large size,complex shapes,traditional manual and single-robot manufacturing are faced with the problems of low effi-ciency,poor consistency,limited space,etc.,multi-robot has the advantages of high robustness and efficiency and is widely used in large-scale complex components.Task allocation and motion planning constitute the decision center of multi-robot systems,whose performance affects the whole system's efficiency.Firstly,considering the challenges in task allocation and motion planning of major equipment components manufacturing,such as multiple task pro-cesses,multiple conflict interference,and high precision requirements,the importance of task allocation and motion planning of multiple robots in complex environments is explained.Then the existing main methods task allocation and motion planning methods are described,including their applications in intelligent manufacturing.On this basis,the existing problems affecting task allocation and motion planning in complex scenarios are analyzed.The solution is proposed using reinforcement learning and a hybrid optimization algorithm.Finally,the development of task al-location and dynamic planning technology and the application of multi-robot in the large-scale complex compon-ents manufacturing processes prospect.
维普
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