基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。

Time-optimal trajectory planning of robot based on QPSO

On the premise of considering joint constraints,a new algorithm for the optimal time trajectory planning of industrial robots is proposed to obtain the optimal time trajectory of industrial robots.Five times non-uniform b-spline interpolation method was used to construct the motion trajectory of each joint,and the obtained position of each joint of the robot was accurate,and the velocity,acceleration and acceleration curves of each joint were continuous.Using Quantum-behaved Particle Swarm Optimization(QPSO)for time optimal trajectory planning,the algorithm can search in the whole feasible region,has strong global search ability.Compared with standard particle swarm optimization(PSO) and differential evolution algorithm(DE),the results show that the numerical results obtained by using this algorithm for time-optimal trajectory planning are smaller.