Target Position Estimation by Genetic Expression Programming for Mobile Robots With Vision Sensors

Target location is an important task in robotics applications. For different application purposes, the positions of targets are usually described by various coordinate systems. Closed-form formulas that describe the relationships between two coordinate systems serve as a means for coordinate transformation. However, the existence of unavoidable measurement errors and uncertainty makes closed-form formulas less reliable. Besides, the closed-form formulas usually involve operations of matrix inversion and transpose that usually consume a considerable amount of computing resources. This paper defines the problem of coordinate transformation on mobile robots as a regression problem and employs the techniques of gene expression programming to discover the regression models. With such regression models, coordinate transformation can be done by simpler formulas with lower processing costs. The proposed techniques have been implemented and integrated with a four-wheeled robot equipped with vision sensors and have been verified in real environments. The experiments demonstrate the effectiveness and performance of the proposed method. To the best of our knowledge, this is the first study on the underlying problem using genetic-based techniques.