This paper deals with design and implementation of an evolutionary system for control of an autonomous mobile robot. This system should make possible an adaptation to a group of tasks, that can be similarly defined for a living being. We use results of real experiments with laboratory rats and tasks, which these rats are able to learn. The robot control system is a combination of several methods of mobile robotics and artificial intelligence. The adaptable part of the control system is based on genetic algorithms and neural networks. This work covers a wide range of problems related with this subject - various elements of the control system, the robot control and implementation, and also components of the test environment, which can be used...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
In this thesis, the problems of generating and optimizing motor behaviors for both simulated and rea...
This paper deals with design and implementation of an evolutionary system for control of an autonomo...
This paper deals with design and implementation of an evolutionary system for control of an autonomo...
Abstract. This paper deals with the design of an evolutionary system for control of an autonomous mo...
International audienceThis is a study of an application of neuraltechnics to the learning of control...
Inspired by animals’ ability to learn and adapt to changes in their environment during life, hybrid ...
From perception to action and from action to perception, all elements of an autonomous agent are int...
Complex robots inspired by biological systems usually consist of many dependent actuators and are di...
Ability to adapt to a continuously changing environment is inherent both to natural and artificial "...
We discuss the methodological foundations for our work on the development of cognitive architectures...
This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial...
Through series of experiments this work compares effects of different types of genetic algorithms on...
Neural networks, reinforcement learning systems and evolutionary algorithms are widely used to solve...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
In this thesis, the problems of generating and optimizing motor behaviors for both simulated and rea...
This paper deals with design and implementation of an evolutionary system for control of an autonomo...
This paper deals with design and implementation of an evolutionary system for control of an autonomo...
Abstract. This paper deals with the design of an evolutionary system for control of an autonomous mo...
International audienceThis is a study of an application of neuraltechnics to the learning of control...
Inspired by animals’ ability to learn and adapt to changes in their environment during life, hybrid ...
From perception to action and from action to perception, all elements of an autonomous agent are int...
Complex robots inspired by biological systems usually consist of many dependent actuators and are di...
Ability to adapt to a continuously changing environment is inherent both to natural and artificial "...
We discuss the methodological foundations for our work on the development of cognitive architectures...
This thesis describes the use of a Real-Time Evolutionary Algorithm (RTEA) to optimise an Artificial...
Through series of experiments this work compares effects of different types of genetic algorithms on...
Neural networks, reinforcement learning systems and evolutionary algorithms are widely used to solve...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
Mobile robot's navigation and obstacle avoidance in an unknown and static environment is analyzed in...
In this thesis, the problems of generating and optimizing motor behaviors for both simulated and rea...