2,176 research outputs found
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.
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Evolution of behaviour trees for collective transport with robot swarms
Swarm robotics, inspired by natural swarms, studies how simple robots with only local sensing capabilities and no centralised control may cooperate to achieve a common goal in a robust, flexible and scalable way. A robotic system with such properties constitutes an interesting alternative to the platforms currently used in warehouses and distribution plants, where workers are at risk of injury and the space and budget available for complex infrastructure is limited. Swarm behaviours are emergent, which makes the task of designing the controllers of the individual robots particularly challenging. In this work, we propose a method for a swarm of industrial robots to collectively transport items that are too heavy for a single agent to carry. We use artificial evolution to evolve behaviour tree controllers for the swarm agents and we conceive a decentralised coordination strategy based on local messaging. The method is developed and tested in a simulated environment, using a combination of freely available open source libraries. The results show that a homogeneous swarm equipped with our solution is able to successfully find the items placed in the environment and transport them back to a nest region. We suggest further tuning of the evolutionary parameters and the introduction of noise in the simulator in order to improve the observed performance of the controllers in simulation and their expected performance the real worldObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i Infraestructur
Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems
Third Conference on Artificial Intelligence for Space Applications, part 2
Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed
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Evolved transistor array robot controllers
For the first time a field programmable transistor array (FPTA) was used to evolve robot control circuits directly in analog hardware. Controllers were successfully incrementally evolved for a physical robot engaged in a series of visually guided behaviours, including finding a target in a complex environment where the goal was hidden from most locations. Circuits for recognising spoken commands were also evolved and these were used in conjunction with the controllers to enable voice control of the robot, triggering behavioural switching. Poor quality visual sensors were deliberately used to test the ability of evolved analog circuits to deal with noisy uncertain data in realtime. Visual features were coevolved with the controllers to automatically achieve dimensionality reduction and feature extraction and selection in an integrated way. An efficient new method was developed for simulating the robot in its visual environment. This allowed controllers to be evaluated in a simulation connected to the FPTA. The controllers then transferred seamlessly to the real world. The circuit replication issue was also addressed in experiments where circuits were evolved to be able to function correctly in multiple areas of the FPTA. A methodology was developed to
analyse the evolved circuits which provided insights into their operation. Comparative experiments demonstrated the superior evolvability of the transistor array medium
Driving Cars by Means of Genetic Algorithms
Proceedings of: 10th International Conference on
Parallel Problem Solving From Nature, PPSN 2008. Dortmund, Germany, September 13-17, 2008The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.Publicad
Pyro: A Python-based Versatile Programming Environment for Teaching Robotics
In this paper we describe a programming framework called Pyro which provides a set of abstractions that allows students to write platformÂindependent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a topÂdown approach. We describe the background of the project, novel abstractions created, its library of objects, and the many learning modules that have been created from which curricula for different types of courses can be drawn. Finally, we explore Pyro from the students\u27 perspective in a case study
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