Fusion of Multi-Vision of Industrial Robot in MAS-Based Smart Space

Abstract

The paper presents a fusion method of muti-vision of industrial robot in a smart space based on multi-agent system(MAS), the robotic multi-vision consists of top-view, side-view, front-view and hand-eye cameras, the moving hand-eye provide vision guidance and give the estimation of robot position, other three cameras are used for target recognition and positioning. Each camera is connected to an agent based on an image-processing computer that aims at analyzing image rapidly and satisfying the real-time requirement of data processing. As a learning strategy of robotic vision, a back-propagation neural network(BPNN) with 3-layer-architecture is first constructed for each agent and is independently trained as a classifier of target recognition using batch gradient descent method based on the region features extracted from the images of target samples(typical mechanical parts), and then the outputs of trained BPNNs in MAS-based smart space are fused with Dempster-Shafer evidence theory to form a final recognition decision, the experimental results of typical mechanical parts show that fusion of multi-vision can improve the robotic vision accuracy and MAS-based smart space will contribute to the parallel processing of immense image data in robotic multi-vision system.