A Concept for Using Superformula and Information Theory to Identify and Prioritize Interesting Objects in Autonomous Exploration

In order to refine autonomous exploratory movement planning schemes, an approach must be developed that accounts for valuable information other than that gained from map filling. To this end, the goal of this thesis is divided into two parts. The first is to develop a technique for categorizing objects detected by an autonomous exploratory robot and assigning them a score based on their interest value. The second is an attempt to develop a method of integrating this technique into a navigation algorithm in order to refine the movements of a robot or robots to maximize the efficiency of information gain. The intention of both of these components is to provide a method of refining the navigation scheme applied to autonomous exploring robots and maximize the amount of information they can gather in deployments where they face significant resource or functionality constraints. To this end this project is divided into two main sections: a shape-matching technique and a simulation in in which to implement this technique. The first section was accomplished by combining concepts from information theory, principal component analysis, and the eigenfaces algorithm to create an effective matching technique. The second was created with inspiration from existing navigation algorithms. Once these components were determined to be functional, a testing regime was applied to determine their capabilities. The testing regime was also divided into two parts. The tests applied to the matching technique were first to demonstrate that it functions under ideal conditions. After testing was conducted under ideal conditions, the technique was tested under non-ideal conditions. Additional tests were run to determine how the system responded to changes in the coefficients and equations that govern its operation. Similarly, the simulation component was initially tested under normal conditions to determine the base effectiveness of the approach. After these tests were conducted, alternative conditions were tested to evaluate the effects of modifying the implementation technique. The results of these tests indicated a few things. The first series of tests confirmed that the matching technique functions as expected under ideal conditions. The second series of tests determined that the matching element is effective for a reasonable range of variations and non-ideal conditions. The third series of tests showed that changing the functional coefficients of the matching technique can help tune the technique to different conditions. The fourth series of tests demonstrated that the basic concept of the implementation technique makes sense. The final series of tests demonstrated that modifying the implementation method is at least somewhat effective and that modifications to it can be used to specifically tailor the implementation to a method. Overall the results indicate that the stated goals of the project were accomplished successfully.