摘要
两栖机器人的水下路径最优规划是目前机器人运动控制研究领域的热点和难点。本文针对两种基于视觉伺服的广义约束优化(GCOP)和序列二次规划(SQP)的机器人运动控制算法进行对比分析,结合视觉伺服传感器,实现了两栖机器人最佳路径的规划、监测动态目标标定、移动目标监测、水下障碍物识别和目标跟踪。利用球形机器人的结构对称特性及阿基米德浮力原理,并结合模糊控制算法对水舱水位进行实时控制,使球形两栖机器人在水下能实现水下多自由度运动。最后,进行了算法的仿真和水下运动实验。实验结果表明,GCOP算法和SQP算法在相对障碍物的有限距离内,SQP算法规划的路径更加合理;而在达到目标坐标位置上,两种算法的误差为167.5 mm,SQP算法在水下路径规划上更加有效。
The underwater path planning of amphibious spherical robots is currently a research challenge in the field of amphibious robot motion control.In this study,two types of robot motion control algorithms,namely Generalized Constraint Optimization(GCOP)and Sequential Quadratic Programming(SQP)algorithms based on visual servo,were compared and analyzed.The optimal path planning of the amphibious spherical robot was realized,combinated with visual servo sensors.Dynamic target calibration,moving target monitoring,underwater obstacle recognition,and target trackingfunctions were also developed.Furthermore,this study considered the symmetrical structure of spherical robots(using Archimedes′buoyancy principle)and combined fuzzy control algorithms to control the water level of the water tank so that spherical amphibious robots can achieve multi-DOF underwater motion.Finally,algorithm simulations and underwater motion experiments were performed to verify the feasibility of the proposed method.The results show that path planning by the SQP algorithm is more reasonable considering the distance between the GCOP and SQP algorithms,relative to the obstacle.In reaching the target coordinate position,the error between the two algorithms reaches to 167.5 mm,showing that the SQP algorithm is superior in underwater path planning than the GCOP algorithm.
作者
马宇科
郑亮
胡高凯
吉晓雯
司兆怡
刘晏彤
MA Yu-ke;ZHENG Liang;HU Gao-kai;JI Xiao-wen;SI Zhao-yi;LIU Yan-tong(Jilin Agricultural Science and Technology University, Jilin 132101, China;Changchun University of Science and Technology, Changchun 130022, China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2020年第8期1733-1742,共10页
Optics and Precision Engineering
基金
吉林农业科技学院大学生科技创新创业训练计划资助项目(No.201911439027)
吉林农业科技学院青年基金资助项目(No.20190505)。
关键词
两栖机器人
球形机器人
路径规划
目标识别
amphibious robot
spherical robot
path planning
target recognition
