The invention relates to the technical field of
industrial robot intelligent
programming and man-
machine interaction, in particular to a
robot teaching
programming method and
system based on global input. The
system comprises a task intention interaction module, a semantic constraint matching module, an equipment modeling module, a
trajectory optimization module, an interaction
verification module and a cross-platform deployment module. According to the method, through global task modeling and semantic abstraction, a user does not need point-by-point teaching or
manual segmentation, and the high-quality track can be generated through one key only by defining the
key frame at a time and adding the behavior semantic
label. According to the method, normal form upgrading from traditional'segmented input, local interpolation and post-
processing smoothing 'to'global input, unified optimization and essential
smoothing' is achieved, and high-level task
semantics are automatically converted into a bottom-layer smooth track through a three-layer decoupling framework of a task intention layer, a semantic planning layer and a physical execution layer. A semantic planning layer is deeply fused with a multi-
modal constraint template and a high-performance optimization
algorithm, on the premise of strictly meeting dynamic constraints, the total time of generated tracks can be shortened by 15%-25%, the acceleration
peak value is reduced by 40%-50%, ultra-smooth control is achieved from a motion instruction source, and
mechanical vibration and
impact are effectively restrained. According to the method, the complexity of teaching
programming is remarkably reduced, the track smoothness and the
system transportability are improved, and the method is particularly suitable for rapid deployment and efficient programming of middle-end and low-end industrial robots in typical process scenes such as
assembly, carrying and detection.