面向薄板件在线检测的机器人轨迹自主规划

Autonomous robot trajectory planning for online inspection of compliant sheet parts

针对工业机器人在非结构化环境中轨迹规划效率低、规划结果适应性差等问题,提出一种基于生成对抗式神经网络的视觉检测机器人在线轨迹规划算法。首先,提出基于机器人操作系统仿真的点云数据集构建方法。其次,通过对检测特征点云数据提取和机器人检测轨迹的自动标注,提出全新的基于编码-解码结构的生成对抗式网络,利用所输入的检测特征的点云数据端到端地生成机器人检测轨迹。同时,通过融合薄板件的点云几何特征的自注意力机制模块,提高了生成轨迹的准确性。然后,结合机器人运动学模型,提出一种多样性损失函数,提高了生成对抗式网络所生成数据的多样性,解决了笛卡尔空间到机器人关节空间映射的不唯一性下的求解难题。最后,通过案例对比分析,验证了算法的有效性。结果表明:机器人检测规划时间降低了52.6%,末端轨迹精度提高了67.4%。

A vision inspection robot online trajectory planning algorithm based on generative adversarial neural networks was proposed for industrial robots to address the problems of low trajectory planning efficiency and poor adaptability of planning results in unstructured environments.Firstly,a point cloud dataset construction method based on robot operating system simulation was proposed.Secondly,a new coding-decoding structure-based generative adversarial networks was proposed to generate robot inspection trajectories end-to-end using the point cloud data of the input inspection features by extracting the point cloud data of the inspection features and automatically labeling the robot inspection trajectories.Meanwhile,the accuracy of the generated trajectories was improved by incorporating a selfattention mechanism module with point cloud geometric features of compliant sheet parts.Then,a diversity loss function was proposed in conjunction with the robot kinematics to improve the diversity of the data generated by the generative adversarial network and solve the solution problem under the uniqueness of the mapping from Cartesian space to robot joint space.Finally,the effectiveness of the algorithm in this paper was verified by case comparison analysis.The results show that the robot inspection planning time is reduced by 52.6%and the end trajectory accuracy is improved by 67.4%.