基于凸优化平滑动态窗口法的运动规划方法

针对煤矿井下路面崎岖,传统动态窗口法生成的角速度与线速度序列波动频繁无法控制机器人正常运动的问题,提出 了一种基于凸优化平滑动态窗口法生成的角速度与线速度的运动规划方法。 首先使用动态窗口法生成一组角速度线速度序 列,然后基于此序列构建目标函数利用凸优化求得最优解,达到去除原序列中的噪声的目的,实现对机器人的运动控制,求得的 最优解即为平滑后的角速度线速度序列;最后使用 MATLAB 进行仿真,验证不同地图下凸优化的平滑效果,并且与均值滤波和 VMD 重构信号进行对比。 结果表明,与对比算法相比,凸优化处理后的动态窗口法的角速度和线速度序列平滑效果最好,平均 绝对误差最小为 0. 005 6、0. 001 8,平滑了信号同时又保留了其特征。

Motion planning method based on convex optimally smoothed dynamic window approach

A motion planning method based on the angular velocity and linear velocity generated by the convex optimized smoothing dynamic window approach is proposed for the problem that the angular velocity and linear velocity sequences generated by the traditional dynamic window approach fluctuate frequently and cannot control the normal motion of the robot due to the rugged road surface under the coal mine. Firstly, the dynamic window method is used to generate a set of angular velocity linear velocity sequences, then the objective function is constructed based on this sequence to obtain the optimal solution using convex optimization to remove the noise in the original sequence and achieve the motion control of the robot, and the obtained optimal solution is the smoothed angular velocity linear velocity sequence. Finally, simulations are performed using matlab to verify the smoothing effect of convex optimization under different maps and to compare with mean filtering and VMD reconstruction of the signal. The results show that, compared with the comparison algorithms, the convex optimized dynamic window method has the best smoothing effect on the angular and linear velocity sequences with the smallest average absolute errors of 0. 005 6 and 0. 001 8, smoothing the signal while preserving its characteristics.