Path planning for unmanned aerial vehicles based on genetic programming

Abstract

Path planning system is one of the key component for the unmanned aerial vehicles (UAVs) and mobile robots in modern operational systems used in all sorts of circumstances. Generally, genetic algorithm (GA) plays a big role in dealing with optimization problems. However, compared to GA, genetic programming (GP) displays better modeling and optimizing ability in path planning problem. GP is capable of dealing with UAV and mobile robot path planning problems. GP improves performance by utilizing generalized hierarchical computer programs and optimizing evolutionarily. This paper presents an optimized GP method which applies to path planning problem. Several special designed function and symbol operators are proposed and appended to the binary tree structure, as well as the redesigned decoding system. With the combination of selection and reproduction operation, the optimized GP accomplishes the design of path planning. By using the optimized GP method, experiment results display better fitness paths against GA method.