The invention discloses a mechanical arm positioning and grabbing method based on
machine vision, and relates to the technical field of
machine vision and mechanical arm control, the method comprises the following steps: synchronously acquiring RGB-D images of a target scene through a multi-
view camera array, and generating three-dimensional
point cloud data through data fusion; an improved LSD
algorithm and a PnP
algorithm are adopted to calculate the initial
pose of the target object, illumination
distortion is eliminated in combination with the
generative adversarial network, and three-dimensional coordinates are output; a mechanical arm
motion error transfer model is constructed based on Monte Carlo
simulation, and a candidate grabbing scheme set is generated through
reinforcement learning; and an optimal grabbing scheme is screened through a preset priority evaluation rule, and a mechanical arm joint movement track and a control
instruction set are generated. Through multi-
modal data fusion and a nonlinear optimization
algorithm, the technical problems of
large target positioning deviation and sensitive illumination interference in a complex environment are solved, and the grabbing precision and robustness of the mechanical arm are improved.