Symbolic processing methods for 3D visual processing

The purpose of this paper is to describe a theory that defines an open method for solving 3D visual data processing and artificial intelligence problems that is independent of hardware or software implementation. The goal of the theory is to generalize and abstract the process of 3D visual processing so that the method can be applied to a wide variety of 3D visual processing problems. Once the theory is described a heuristic derivation is given. Symbolic processing methods can be generalized into an abstract model composed of eight basic components. The symbolic processing model components are: input data; input data interface; symbolic data library; symbolic data environment space; relationship matrix; symbolic logic driver; output data interface and output data. An obstacle detection and avoidance experiment was constructed to demonstrate the symbolic processing method. The results of the robot obstacle avoidance experiment demonstrated that the mobile robot could successfully navigate the obstacle course using symbolic processing methods for the control software. The significance of the symbolic processing approach is that the method arrived at a solution by using a more formal quantifiable process. Some of the practical applications for this theory are: 3D object recognition, obstacle avoidance, and intelligent robot control.