基于蚁群算法的Tripod机器人路径优化研究

Research on Path Optimization for Tripod Robot Based on Ant Colony Algorithm

为提高基于视觉的抓取机器人在多目标、多约束情况下控制效率,以Tripod机器人为模型,提出带有约束矩阵的蚁群算法,使其在满足控制要求的情况下求解出机器人抓取的最优路径。首先,通过图像矫正、阈值分割和特征提取等图像处理方法,得到物体的位置、颜色等信息;接着,按照有约束旅行商问题建立模型,构造运动和颜色约束矩阵;最后,在LabVIEW中采用蚁群算法求解最优路径,并将控制方案传输到机器人控制系统中,使其按照最优路径自动完成抓取过程。实验结果表明,该方法在完成抓取任务的同时,缩短了机器人运动路径,对实际生产中工业机器人智能抓取具有指导意义。

In order to improve the control efficiency of the grab robot based on machine vision in the case of multi - targets and multi - constraints, an ant colony algorithm with constraint matrix, taking Tripod robot as the model, was put forward to find an optimized path for the robot to grab when the control requirements satisfied. Firstly, the image process methods such as image correction, threshold segmentation and feature extraction were used to obtain the information of the position and color of the object. Then, the model was constructed according to the problem of constrained traveling salesman problem, and the motion and color constraint matrix was con- structed. Finally, the optimization path was calculated by adopting the ant colony algorithm via LabVIEW, and the control program was transmitted to the robot which finished the grab automatically following the optimized path. The experimental results show that the method can shorten the motion path of the robot, which has guiding significance in applying the robot into actual industrial production for intelligent grabbing.