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.
Highlights
The majority of industrial robot still rely on manual teaching in practical application, adding vision to robot is becoming a trend of intelligent robot, and the robotic vision with monocular or binocular camera has widely used in intelligent robot system, whereas one or two cameras can not deal with complex vision task such as workpiece recognition and positioning, visual servoing, robot tracking, multi-robot cooperating
A multi-agent system(MAS) based on computer network is adopted for the immense image data processing of multi-vision task, in which each agent is a computer connected to a camera with independent image processing capability, the MAS forms a smart space surrounding the robot and provides the parallel processing of multi-vision information, there are some models for the communication and cooperation of multiagents in MAS[4-5], a blackboard system of artificial intelligence is used for this purpose, and we put emphasis on the learning and fusing stratergy of multi-vision in industrial robot application
(a) top-view images of five mechanical parts (b) side-view images of five mechanical parts (c) front-view images of five mechanical parts Fig.4 Binary images of mechanical parts obtained from different directions Using these typical samples which include basic shapes, the three BPNNs are trained according to the above BPNN training procedures, Fig.5 displays the training results for top-view, side-view and front-view images of mechanical parts using region feature vector with the goal of performance Et=0.001
Summary
The majority of industrial robot still rely on manual teaching in practical application, adding vision to robot is becoming a trend of intelligent robot, and the robotic vision with monocular or binocular camera has widely used in intelligent robot system, whereas one or two cameras can not deal with complex vision task such as workpiece recognition and positioning, visual servoing, robot tracking, multi-robot cooperating. Using muticameras is an effective method to deal with the complex vision task of industrial robot[1,2,3], and the subsequent problem of multi-vision is immense image data processing that may be not undertaken by one computer. A multi-agent system(MAS) based on computer network is adopted for the immense image data processing of multi-vision task, in which each agent is a computer connected to a camera with independent image processing capability, the MAS forms a smart space surrounding the robot and provides the parallel processing of multi-vision information, there are some models for the communication and cooperation of multiagents in MAS[4-5], a blackboard system of artificial intelligence is used for this purpose, and we put emphasis on the learning and fusing stratergy of multi-vision in industrial robot application
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