基于改进粒子群算法的凿岩机械臂轨迹规划

Rock drilling arm trajectory planning based on improved particle swarm optimization algorithm

针对凿岩机械臂工作效率低、运行不稳定的问题,提出了一种基于改进粒子群算法优化的轨迹规划算法。该方法采用分段多项式插值拟合轨迹,以时间为适应度函数采用改进粒子群算法对机械臂的轨迹进行优化。本文提出的自适应惯性权重和动态调整学习因子相结合的粒子群算法,可以很好地解决标准粒子群算法容易陷入局部极值和收敛速度慢的问题。在Matlab中对机械臂各关节轨迹规划进行仿真,结果表明:该方法能够保证机械臂工作稳定性,也能较好地实现时间最优轨迹。

Aiming at the problems of low efficiency and unstable operation of rock drilling manipulators,this paper proposed a trajectory planning algorithm based on improved particle swarm optimization.The method uses piecewise polynomial interpolation to fit the trajectory,and then uses the time as the fitness function to optimize the trajectory of the manipulator by using the improved particle swarm algorithm.The particle swarm optimization algorithm proposed in this paper,which combines the adaptive inertia weight and the dynamic adjustment of the learning factor,can solve the problem that the standard particle swarm optimization algorithm is easy to fall into the local extreme value and the convergence speed is slow.The trajectory planning of each joint of the manipulator is simulated in Matlab.The results show that this method can better realize the time optimal trajectory while ensuring the working stability of the manipulator.