基于高斯噪声发散的协作机器人路径优化及避障

Path optimization and obstacle avoidance of collaborative robots based on Gaussian noise divergence

为解决协作机器人示教学习领域中回归路径过拟合的问题,提出一种基于高斯噪声发散的路径优化方法,该方法通过在原始示教数据中随机增加符合高斯分布的噪声值来模糊由于人为示教产生的抖动等不利特征,以减少回归路径对冗余弯折特征的保留,从而提高回归路径的质量。在此基础上,针对回归路径自适应性差的问题,提出一种基于点云消隐的自适应避障策略,通过对被障碍物影响的数据点云进行消隐处理来引导回归路径避开障碍区域,实现了无需重新示教自适应生成避障路径。最后,利用UR5机器人进行了有障碍物时的自适应抓取实验,结果表明,所提方法能有效改善路径的平滑度,并能达到自适应避障的效果。

To solve the over-fitting problem of regression path in the learning from demonstration of collaborative robots,a path optimization method based on Gaussian noise divergence was proposed.To blur the unfavorable features caused by the jittery during human demonstration,the proposed method randomly added the noise that obeyed Gaussian distribution to the original demonstration data,which could reduce the retention of redundant bending features of the demonstration data,thereby improving the quality of the regression path.An adaptive obstacle avoidance strategy based on scattered data was proposed,which blanked the data in obstacle area to guide the regression path for avoiding the obstacle,so as to adaptively generate the obstacle avoidance path without re-demonstration.The UR5 robot was used to conduct an adaptive grasping experiment with obstacles.The experimental results showed that the method could effectively improve the smoothness of the path and achieve the result of adaptive obstacle avoidance.