为了提高切削加工机器人的加工效率以及加工稳定性,对机器人加工轨迹进行了优化.在机器人任务空间中用双NURBS曲线描述刀位轨迹及刀轴矢量轨迹,并将其转化到关节空间.将刀轴矢量通过旋转坐标变换转换为机器人旋转矩阵,并结合基于旋量的机器人正运动学方程求取关节角,此角度值作为求运动学逆解的约束条件,以保证机器人加工时刀轴矢量相对于工件表面不变.基于骨干粒子群优化算法提出了一种自适应罚函数的约束多目标骨干粒子群优化算法,对机器人加工过程中的时间、加速度、加加速度等指标进行多目标优化,该算法采用自适应指数罚函数对约束进行处理,为了避免算法早熟,引入时变变异因子,增强了算法全局搜索能力和局部探索能力.最后,通过实验验证所提出算法的正确性和有效性.%In order to improve the machining efficiency and stability of the machining robot, the machining trajectory of the robot is optimized. In the robot task space, the double NURBS curve is used to describe the tool path and the tool axis, the algorithm of the inverse kinematics of the robot is used to transform the tool path into the joint space, the tool axis vector is transformed into the rotation matrix of the robot by the rota-tion coordinate transformation, and the joint angle is obtained by the kinematic equation of the robot based on the Screw theory, this angle value is used as the constraint of the inverse kinematics solution to ensure that the tool axis is unchanged relative to the workpiece surface. Based on the key particle swarm optimiza-tion algorithm, a constrained Backbones Multi-objective Particle Swarm Optimization Algorithm with Adap-tive Penalty Function ( APF-CBBMOPSO) is proposed to optimize the time, velocity, acceleration and ac-celeration of the machining robot. The algorithm uses the adaptive exponential penalty function to deal with the constraint, In order to avoid the early maturing of the algorithm; the time-varying variance factor is in-troduced to enhance the global searching ability and local exploration ability. Finally, the correctness and va-lidity of the proposed algorithm are verified by experiment.
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