基于模糊聚类判别的挖掘机器人行为控制  

Behavior Control of Excavator Robot Based on Fuzzy Clustering Discrimination

作　　者：王福斌[1] 刘杰[1] 陈至坤[2] 焦春旺[1] 

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110819 [2]河北联合大学电气工程学院,河北唐山063009

出　　处：《东北大学学报（自然科学版）》2011年第12期1745-1748,1769,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(50775029);中央高校基本科研业务费专项资金资助项目(N090603008)

摘　　要：为实现挖掘机器人的自主挖掘,构建了适合挖掘机器人的行为控制体系结构.以挖掘行为作为基准,用状态流模型实现挖掘目标、挖掘任务、挖掘行为的逐层分解.采集目标图像、机械臂倾角及液压缸压力信号,作为状态流行为状态之间触发转换的事件或条件.针对挖掘中遇到的沙土物料、表面块状物料、块状物料埋于沙中3种挖掘环境,综合视觉定位信息、压力及倾角信息,通过模糊聚类判别后,触发挖掘动作状态流模型,有区别地自主处理挖掘中遇到的不同情况.最后通过控制挖掘动作,实现自主挖掘目标.In order to realize the autonomy excavation of excavator robot,the behavior control system architecture was built.With the excavation behavior as a baseline,the state-flow model was used to implement the excavation object,the excavation task and the excavation behavior stepwise.The object image,rake angle of mechanical arm and pressure signal of hydraulic cylinder were collected,as the event and condition for trigger and transition between behavior states of state-flow.According to the three kinds of mining environment during the excavation,which are sand,surface block material and block material buried in sand,the visual position,pressure and rake angle information were gathered and mining action state-flow model was triggered after the fuzzy clustering discrimination,then the different circumstances encountered in mining were processed distinguishingly and independently.Finally the autonomy excavation object was achieved by controlling excavation action.

关 键 词：挖掘机器人 模糊聚类 特征提取 状态流 行为控制 

分 类 号：TP242.3[自动化与计算机技术—检测技术与自动化装置]