摘要
针对当前智能机器人避障效果不佳的问题,提出基于多维数据挖掘的智能机器人避障路径自适应选择方法。通过固连坐标系描述机器人具体位置和作业姿态,获得机器人运动参数信息。采用可视图方式简化障碍物,建立作业环境模型,获取全局视图。利用视觉、超声波和红外传感器实现障碍物形状数据多维挖掘,结合数据挖掘结果检测机器人与障碍物间距。综合时间、路程和转弯次数最少三个约束条件,建立路径选择目标函数。将环境多维数据作为蚁群算法参数输入,设置状态转移规则,计算蚂蚁到达下个目标点的概率,更新全局和局部信息素,当满足最大迭代次数要求时输出最佳避障路径。仿真证明,该算法在静态和动态环境下都能选择出最佳路径,避免碰撞现象,实现快速收敛。
Aiming at the problem of poor obstacle avoidance effect of intelligent robot,an adaptive selection method of obstacle avoidance path for intelligent robot of intelligent robot based on multidimensional data mining is proposed.The specific position and working attitude of the robot are described by the fixed coordinate system,and the motion parameter information of the robot is obtained.It adopts the viewable method to simplify obstacles,establishes the working environment model and obtains the global view.The multi-dimensional data mining of obstacle shape is realized by using vision,ultrasonic and infrared sensors,and the distance between robot and obstacle is detected combined with the data mining results.The objective function of route selection is established by integrating the three constraints of time,distance and turning times.The environment multidimensional data are taken as the parameter input of ant colony algorithm,the state transition rules are set,the probability of ants reaching the next target point is calculated,the global and local pheromones are updated,and the best obstacle avoidance path is output when the maximum number of iterations is met.Simulation results show that the algorithm can select the best path in both static and dynamic environments,avoid collision and achieve rapid convergence.
作者
王苗
李明倩
刘芳
WANG Miao;LI Mingqian;LIU Fang(Experimental Training Centre,Wuhan City College,Wuhan 430083,China;Computer Science Institute,Wuhan University,Wuhan 430072,China)
出处
《微型电脑应用》
2023年第10期192-196,共5页
Microcomputer Applications
关键词
多维数据挖掘
智能机器人
避障路径选择
蚁群算法
目标函数
multidimensional data mining
intelligent robot
obstacle avoidance path selection
ant colony algorithm
objective function
