An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Um algoritmo evolucionário para geração de planos de rotas /

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico

A reactive/deliberative planner using genetic algorithms on tactical primitives

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 99-102).Unmanned aerial systems are increasingly assisting and replacing humans on so-called dull, dirty, and dangerous missions. In the future such systems will require higher levels of autonomy to effectively use their agile maneuvering capabilities and high-performance weapons and sensors in rapidly evolving, limited-communication combat situations. Most existing vehicle planning methods perform poorly on such realistic scenarios because they do not consider both continuous nonlinear system dynamics and discrete actions and choices. This thesis proposes a flexible framework for forming dynamically realistic, hybrid system plans composed of parametrized tactical primitives using genetic algorithms, which implicitly accommodate hybrid dynamics through a nonlinear fitness function. The framework combines deliberative planning with specially chosen tactical primitives to react to fast changes in the environment, such as pop-up threats. Tactical primitives encapsulate continuous and discrete elements together, using discrete switchings to define the primitive type and both discrete and continuous parameters to capture stylistic variations. This thesis demonstrates the combined reactive/deliberative framework on a problem involving two-dimensional navigation through a field of threats while firing weapons and deploying countermeasures. It also explores the planner's performance with respect to computational resources, problem dimensionality, primitive design, and planner initialization. These explorations can guide further algorithm design and future autonomous tactics research.by Stephen William Thrasher, Jr.S.M