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Omnidirectional mobile platform is essential due to its excellent mobility and versatility. With the development of the manufacturing industry, how to transport oversized or overweight goods has become a new problem. Compared with manufacturing omnidirectional mobile platforms with different specifications, it is more cost-effective and flexible to coordinate two non-physically connected omnidirectional platforms to transport overweight and oversized cargo. The roughness of the actual deployment environment and the mechanical deflection between the two vehicles have a significant impact on the normal operation of the system. This paper combines mechanical wheels, image processing algorithms and collaboration algorithms to create a novel and practical split-type omnidirectional mobile platform based on image deviation prediction for transporting oversized or overweighted goods. The proposed system collects raw measurements from a distance sensor and an image sensor, transmits them to a central processing unit through a wireless communication module and calculates and predicts the relative deflection between the two vehicles based on our derived mathematical model. This information is then fed to a Kalman filter and PID control algorithm to coordinate the two vehicles. The effectiveness and performance of our system have been thoroughly tested, which has already been applied in a bullet train production line.

Two-Vehicle Coordination System for Omnidirectional Transportation Based on Image Processing and Deviation Prediction