基于改进粒子群算法的机器人时间最优轨迹规划

Time optimal trajectory planning of robot based on improved particle swarm optimization

针对标准粒子群算法在进行机器人时间最优轨迹规划时容易陷入局部最优、早熟等缺点,提出一种快速收敛的改进算法。算法采用动态学习因子策略替代传统固定的学习因子,并在此基础上利用“3-5-3”混合多项式插值函数进行规划,最后在MATLAB仿真软件中完成机器人各关节运动轨迹的拟合。研究结果表明:改进粒子群算法的局部收敛速度和全局收敛速度均优于标准粒子群算法,且比单纯利用“3-5-3”混合多项式进行轨迹规划所需的时间缩短26%,各关节运行轨迹平稳连续,证明改进算法的优越性、有效性以及可行性。

For the shortcomings of the standard particle swarm optimization algorithm in the time-optimal trajectory planning of the robot,such as easy to fall into the local optimum and premature,an improved algorithm with fast convergence was proposed.The algorithm uses a dynamic learning factor strategy to replace the traditional fixed learning factor,and on this basis uses the“3-5-3”hybrid polynomial interpolation function for planning,and finally completes the fitting of the motion trajectory of the robot joints in the MATLAB simulation software.The research results show that both the local convergence speed and global convergence speed of the improved particle swarm algorithm are better than those of the standard particle swarm algorithm,and the time required for trajectory planning is reduced by 26%compared with purely using the“3-5-3”hybrid polynomial.The joint trajectory is smooth and continuous,which proves the superiority,effectiveness and feasibility of the improved algorithm.