Distributed computation and data scheduling for networked visual servo control systems

The stability and performance of visual servo control systems strongly depend on the delays caused by image processing. In order to accelerate the visual feedback, the distributed computational power across networks and appropriate data transmission mechanism are of particular interest. In this paper, a novel distributed computation with data scheduling is proposed for networked visual servo control systems (NVSCSs) aiming at improving the control performance. A realtime transport protocol is developed for image data transmission. For a NVSCS which is modeled as a continuous-time system with computation, transmission and holding delays, a switching control law is applied. A probabilistic sampling scheduler is derived such that the control performance and the network load caused by image data transmission are balanced. Experiments on two 1-DoF linear modules equipped with a camera are conducted to validate the proposed approach. A visual servo system without data scheduling is implemented for comparison. The experimental results demonstrate a comparable control performance of the proposed approach with an advantage of reduced network load.

Authors

• Haiyan Wu
• Lei Lou
• Chih-Chung Chen
• Kolja Kühnlenz
• Sandra Hirche

Keywords

• Delays
• Visualization
• Feature extraction
• Control systems
• Image processing
• Silicon
• Switches
• Transport protocols
• Pose estimation
• Graphics processing units
• Visual System
• Distributed Computing
• Visual Servoing
• Visual Servo Control
• Optimal Control
• Control Performance
• Visual Feedback
• Linear Mode
• Visual Control
• Switching Control
• Continuous-time Systems
• Network Load
• Performance Of Control System
• Transport Protocol
• Sampling Rate
• Image Features
• Cost Function
• Communication Network
• Control Network
• Delay Interval
• Random Delay
• Processing Nodes
• Tracking Error
• Delay Estimation
• Transmission Images
• Transmission Delay
• Proportional-integral-derivative
• Random Sample Consensus