Lou Peihuang

Agent-Oriented Embedded Control System Design and Development of a Vision-Based Automated Guided Vehicle

This paper presents a control system design and development approach for a vision-based automated guided vehicle (AGV) based on the multi-agent system (MAS) methodology and embedded system resources. A three-phase agent-oriented design methodology Prometheus is used to analyse system functions, construct operation scenarios, define agent types and design the MAS coordination mechanism. The control system is then developed in an embedded implementation containing a digital signal processor (DSP) and an advanced RISC machine (ARM) by using the multitasking processing capacity of multiple microprocessors and system services of a real-time operating system (RTOS). As a paradigm, an onboard embedded controller is designed and developed for the AGV with a camera detecting guiding landmarks, and the entire procedure has a high efficiency and a clear hierarchy. A vision guidance experiment for our AGV is carried out in a space-limited laboratory environment to verify the perception capacity and the onboard intelligence of the agent-oriented embedded control system.

The application of neural networks in the selection of location datum

Selection of locating datum is the basis of the fixture planning, which has a direct influence on the quality of the clamping scheme and the machining quality of workpiece. Selection of locating datum is a complicated process. The designer considers not only the locating feasibility but as well surface type, valid locating area, surface roughness and tolerance relation, etc. In practice the design of a fixture relies heavily on the designers expertise and experience up to now. The paper determines 5 key factors influencing the selection of locating datum of workpiece firstly, the feature information of these influence factors are normalized by defuzzy reasoning. Finally the weights of the influence factors which are obtained according to experience are determined by the training of neural networks. The validity of the method is proved by a case.