Neural network controller against environment: A coevolutive approach to generalize robot navigation behavior

In this paper, a new coevolutive method, called Uniform Coevolution, is introduced to learn weights of a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collisions avoidance. The introduction of coevolutive over evolutionary strategies allows evolving the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method, without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with/without coevolution have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on a mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to examples-based problems.Publicad

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

Reactive with tags classifier system applied to real robot navigation

7th IEEE International Conference on Emerging Technologies and Factory Automation. Barcelona, 18-21 October 1999.A reactive with tags classifier system (RTCS) is a special classifier system. This system combines the execution capabilities of symbolic systems and the learning capabilities of genetic algorithms. A RTCS is able to learn symbolic rules that allow to generate sequence of actions, chaining rules among different time instants, and react to new environmental situations, considering the last environmental situation to take a decision. The capacity of RTCS to learn good rules has been prove in robotics navigation problem. Results show the suitability of this approximation to the navigation problem and the coherence of extracted rules

A reactive approach to classifier systems

IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, 11-14 Oct. 1998The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and/or sequences of actions. Classifier Systems (CS) have proven their ability of continuous learning, however they have some problems in reactive systems. 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. This learning process involves two main tasks: first, discriminating between 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 generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations

Evolving connection weights between sensors and actuators in robots

International Symposium on Industrial Electronics. Guimaraes, 7-11 July 1997.In this paper, an evolution strategy (ES) is introduced, to learn reactive behaviour in autonomous robots. An ES is used to learn high-performance reactive behaviour for navigation and collisions avoidance. The learned behaviour is able to solve the problem in a dynamic environment; so, the learning process has proven the ability to obtain generalised behaviours. The robot starts without information about the right associations between sensors and actuators, and, from this situation, the robot is able to learn, through experience, to reach the highest adaptability grade to the sensors information. No subjective information about “how to accomplish the task” is included in the fitness function. A mini-robot Khepera has been used to test the learned behaviour

RTCS: a reactive with tags classifier system

In this work, a new Classifier System is proposed (CS). The system, a Reactive with Tags Classifier System (RTCS), is able to take into account environmental situations in intermediate decisions. CSs are special production systems, where conditions and actions are codified in order to learn new rules by means of Genetic Algorithms (GA). The RTCS has been designed to generate sequences of actions like the traditional classifier systems, but RTCS also has the capability of chaining rules among different time instants and reacting to new environmental situations, considering the last environmental situation to take a decision. In addition to the capability to react and generate sequences of actions, the design of a new rule codification allows the evolution of groups of specialized rules. This new codification is based on the inclusion of several bits, named tags, in conditions and actions, which evolve by means of GA. RTCS has been tested in robotic navigation. Results show the suitability of this approximation to the navigation problem and the coherence of tag values in rules classification.Publicad

Neural networks robot controller trained with evolution strategies

Congress on Evolutionary Computation. Washington, DC, 6-9 July 1999.Neural networks (NN) can be used as controllers in autonomous robots. The specific features of the navigation problem in robotics make generation of good training sets for the NN difficult. An evolution strategy (ES) is introduced to learn the weights of the NN instead of the learning method of the network. The ES is used to learn high performance reactive behavior for navigation and collision avoidance. No subjective information about “how to accomplish the task” has been included in the fitness function. The learned behaviors are able to solve the problem in different environments; therefore, the learning process has the proven ability to obtain a specialized behavior. All the behaviors obtained have been tested in a set of environments and the capability of generalization is shown for each learned behavior. A simulator based on the mini-robot, Khepera, has been used to learn each behavior

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments