基于改进蚁群算法的多起重机协作吊装装备避障性能研究  

Research on obstacle avoidance performance of multicrane cooperative lifting equipment based on improved ant colony algorithm

作　　者：钱森[1] 王春航 QIAN Sen;WANG Chunhang(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,Anhui,China)

机构地区：[1]合肥工业大学机械工程学院,安徽合肥230009

出　　处：《中国工程机械学报》2020年第1期89-94,共6页Chinese Journal of Construction Machinery

基　　金：国家自然科学基金青年科学基金资助项目(51605126);中国博士后科学基金面上资助项目(2016M600479)。

摘　　要：多起重机协同作业相对于单起重机作业具有更好的稳定性和适应性,在建筑、能源、物流、救援等领域的应用越来越广泛,尤其是大型、重型负载的移动作业。随着吊装系统和工作环境复杂度的不断提高,对多机协作的安全性提出了更高的要求,势必要对多起重机协作吊装过程中避障方法及性能进行研究。因此,在传统蚁群算法的基础上,设计了一种新的启发函数,将待选栅格点与起始点、目标点的距离纳入到参考因素中。改进的蚁群算法增强了全局性,避免陷入局部最优、死锁,改善了避障路径质量。利用数值仿真得到不同避障路径下各台起重机吊绳的索力变化,表明该改进蚁群算法相较于传统蚁群算法在多起重机吊装过程中有效地优化了避障性能。Multi-crane collaborative work has better stability and adaptability than single crane operation,and is widely used in construction,energy,logistics,rescue and other fields,especially for large heavy loads.Increasing complexity of hoisting system and working environment puts higher demands on the safety of multi-machine collaboration.So it's necessary to study the obstacle avoidance method and hoisting performance in the multi-crane cooperative lifting process.Therefore,based on the traditional ant colony algorithm(TACA),a new heuristic function is designed to incorporate the distance between the candidate grid point and the starting point and the target point into the reference factor.The improved ant colony algorithm(IACA)enhances the global understanding,avoids falling into local optimum and deadlock,and improves the quality of obstacle avoidance path.Numerical simulation of cable force changes of crane slings under different obstacle avoidance paths.It shows that the IACA effectively optimizes the obstacle avoidance performance in the multi-crane lifting process compared with the TACA.

关 键 词：多起重机协作吊装 改进蚁群算法 启发函数 避障路径规划 索力变化 

分 类 号：TH213.6[机械工程—机械制造及自动化]