This paper describes a genetic algorithm planning method for autonomous robots in unstructured environments. It presents the approach and demonstrates its application to a laboratory planetary exploration problem. The method represents activities of the robot with discrete actions, or action modules. The action modules are assembled into action plan with a Genetic Algorithm (GA). A successful plan allows the robot to complete the task without violating any physical constraints. Plans are developed that explicitly consider constraints such as power, actuator saturation, wheel slip, and vehicle stability. These are verified using analytical models of the robot and environment. The methodology is described in the context of planetary exploration similar to the NASA Mars Pathfinder mission. More aggressive missions are planned where rovers will explore scientifically important areas that are difficult to reach (e.g., ravines, craters, dry riverbeds, steep cliffs). The proposed approach is designed for such areas. 1 1.

“Only a life lived for others is a life worthwhile”, Albert Einstein. Population Variation in canonical GP 3Regard man as a mine rich in gems of inestimable value. Education can, alone, cause it to reveal its treasures, and enable mankind to benefit therefrom. Bahá’u’llah The Genetic Programming paradigm, which applies the Darwinian principle of evolution to hierarchical computer programs, has produced promising breakthroughs in various scientific and engineering applications. However, one of the main drawbacks of Genetic Programming has been the often large amount of computational effort required to solve complex problems. There have been various amounts of research conducted to devise innovative methods to improve the efficiency of Genetic Programming. This thesis has three main contributions. It firstly provides a comprehensive overview of the related work to improve the performance of Genetic Programming and classifies these various proposed approaches into categories. Secondly, a new static population variation scheme (PV) is