基于DBN-GA算法的水处理罐搬运机器人时间最优轨迹规划  

Time Optimal Trajectory Planning of Water Treatment Tank Handling Robot Using DBN-GA Algorithm

作　　者：王聪 田会方[1] 吴迎峰[1] WANG Cong;TIAN Huifang;WU Yingfeng(School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学机电工程学院,湖北武汉430070

出　　处：《数字制造科学》2024年第4期290-295,共6页

摘　　要：以六轴机械臂关节空间的轨迹作为拟合目标,提出一种工业机器人时间最优轨迹规划方法。引入3-5-3多项式插值,根据关节轨迹角度、角速度、角加速度的约束建立时间最优轨迹规划的数学模型,结合深度置信网络(DBN)预测模型和遗传算法(GA)优化关节空间;应用Matlab工具箱建立机器人运动学模型,生成轨迹策略数据,对DBN预测模型进行训练;之后模型作为遗传算法的适应度函数得到机器人时间最优轨迹。对水处理罐搬运机器人仿真实验结果表明,与传统的智能算法相比,DBN-GA优化的轨迹总时间明显降低。This paper proposes a time-optimal trajectory planning method for industrial robots.The trajectory of the six-axis manipulator’s joint space is treated as the fitting objective,and a mathematical model for time-optimal trajectory planning is established based on the constraints of joint trajectory angles,angular velocities,and angular accelerations using 3-5-3 polynomial interpolation.The joint space optimization is achieved by integrating a Deep Belief Network(DBN)prediction model with a Genetic Algorithm(GA).The robot kinematics model is built using the Matlab toolbox,which generates trajectory strategy data and trains the DBN prediction model.Subsequently,the DBN model serves as the fitness function for the GA to derive the robot’s time-optimal trajectory.Simulation experiments on a water treatment tank handling robot demonstrate that the DBN-GA-optimized trajectory achieves a significantly reduced total trajectory time compared to traditional intelligent algorithms.

关 键 词：深度置信网络 遗传算法 轨迹规划 六轴机械臂 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]