The invention relates to a mechanical arm scattering and grabbing
planning method based on multi-mode sensing, and belongs to the technical field of mechanical arm control. The problems that in the waste steel
recovery process, sensing is not accurate due to the fact that workpieces are complex in shape and tightly stacked,
structure collapse is caused by direct grabbing, and the generalization ability of a traditional
algorithm is poor are solved. According to the technical scheme, the method comprises the steps that an image and
point cloud data are synchronously obtained through a
structured light sensor, and after a unified
pose is calibrated through hands and eyes, a workpiece is segmented through super
voxel clustering; constructing a grabability index based on the spatial topological relation, the support stability, the
pose and the surface
area ratio, generating a layered scattering cost map, and planning a minimum disturbance path; and a surface normal vector is adopted to generate candidate grabbing point pairs, and the optimal grabbing
pose is evaluated through the grabbing quality convolutional network. And high-precision workpiece segmentation, scientific scattering
decision making and self-adaptive grabbing planning are achieved, and the
operation safety and efficiency are remarkably improved.