Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses.Red de Universidades con Carreras en Informática (RedUNCI

Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses

The Mini-Robot Khepera as a Foraging Animate: Synthesis and Analysis of Behaviour

Löffler A, Klahold J, Rückert U. The Mini-Robot Khepera as a Foraging Animate: Synthesis and Analysis of Behaviour. In: Rückert U, Sitte J, Witkowski U, eds. Proceedings of the 5th International Heinz Nixdorf Symposium: Autonomous Minirobots for Research and Edutainment (AMiRE01). Vol 97. Paderborn, Germany: Heinz Nixdorf Institut, Universität Paderborn; 2001: 93-130.The work presented in this paper deals with the development of a methodology for resource-efficient behaviour synthesis on autonomous systems. In this context, a definition of a maximal problem with respect to the resources of a given system is introduced. It is elucidated by means of an exemplary implementation of the solution to such a problem using the mini-robot Khepera as the experimental platform. The described task consists of exploring an unknown and dynamically changing environment, collecting and transporting objects, which are associated with light-sources, and navigating to a home-base. The critical point is represented by the accumulated positioning errors in odometrical path-integration due to slippage. Therefore, adaptive sensor calibration using a specific variant of Kohonen’s algorithm is applied in two cases to extract symbolic, e.g. geometric, information from the sub-symbolic sensor data, which is used to enhance position control by landmark mapping and orientation. In order to successfully handle the arising complex interactions, a heterogeneous control-architecture based on a parallel implementation of basic behaviours coupled by a rule-based central unit is proposed

Evolving robots: from simple behaviours to complete systems

Building robots is generally considered difficult, because the designer not only has to predict the interaction between the robot and the environment, but also has to deal with the ensuing problems. This thesis examines the use of the evolutionary approach in designing robots; the explorations range from evolving simple behaviours for real robots, to complex behaviours (also for real robots), and finally to complete robot systems — including controllers and body plans. A framework is presented for evolving robot control systems. It includes two components: a task independent Genetic Programming sub-system and a task dependent controller evaluation sub-system. The performance evaluation of each robot controller is done in a simulator to reduce the evaluation time, and then the evolved controllers are downloaded to a real robot for performance verification. In addition, a special rep¬ resentation is designed for the reactive robot controller. It is succinct and can capture the important characteristics of a reactive control system, so that the evolutionary system can efficiently evolve the controllers of the desired behaviours for the robots. The framework has been successfully used to evolve controllers for real robots to achieve a variety of simple tasks, such as obstacle avoidance, safe exploration and box-pushing. A methodology is then proposed to scale up the system to evolve controllers for more complicated tasks. It involves adopting the architecture of a behaviour-based system, and evolving separate behaviour controllers and arbitrators for coordination. This allows robot controllers for more complex skills to be constructed in an incremental manner. Therefore the whole control system becomes easy to evolve; moreover, the resulting control system can be explicitly distributed, understandable to the system designer, and easy to maintain. The methodology has been used to evolve control systems for more complex tasks with good results. Finally, the evolutionary mechanism of the framework described above is extended to include a Genetic Algorithm sub-system for the co-evolution of robot body plans — structuralparametersofphysicalrobotsencodedaslinearstringsofrealnumbers. An individual in the extended system thus consists of a brain(controller) and a body. Whenever the individual is evaluated, the controller is executed on the corresponding body for a period of time to measure the performance. In such a system the Genetic Programming part evolves the controller; and the Genetic Algorithm part, the robot body. The results show that the complete robot system can be evolved in this manner. i

Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses.Red de Universidades con Carreras en Informática (RedUNCI

Incremental Robot Shaping

We propose a modular architecture for autonomous robots which allows for the implementation of basic behavioral modules by both programming and training, and accommodates for an evolutionary development of the interconnections among modules. This architecture can implement highly complex controllers and allows for incremental shaping of the robot behavior. Our proposal is exemplified and evaluated experimentally through a number of mobile robotic tasks involving exploration, battery recharging and object manipulation