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8th Annual Genetic and evolutionary Computation Conference

作者： Garder, Lena Mariann Hovin, Mats Erling Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781595931863

In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental approach combining a genetic algorithm (GA) with hill climbing is proposed. This combination interacts in an efficient way to generate precise walking patterns in less than 15 generations. Our proposal is compared to various versions of GA and stochastic search, and finally tested on a pneumatic biped walking robot.

关键词： evolutionary robotics genetic algorithms machine learning

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The advantages of evolving perceptual cues

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ADAPTIVE BEHAVIOR 2006年第2期14卷 147-156页

作者： Macinnes, Ian Di Paolo, Ezequiel Univ Sussex Ctr Computat Neurosci & Robot Brighton BN1 9QG E Sussex England

This paper introduces the evolvable functional circle hypothesis. This hypothesis states that if it is assumed that von Uexkull's concept of functional circles exists in robots and that the models used in evolutionary robotics are altered accordingly, then practitioners of evolutionary robotics will benefit in two ways. The first way is that by promoting the evolution of functional circles rather than sensorimotor loops, it allows evolved robots to select their own stimuli and therefore find their own meaning from environmental information. The second way is that it makes it easier for evolved robots to derive multiple meanings from the signal produced by a sensor. The paper goes on to demonstrate a method to alter our models in evolutionary robotics to promote the evolution of functional circles.

关键词： evolutionary robotics functional circles perceptual cues

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Using Communication for the Evolution of Scalable Role Allocation in Collective robotics 16th

Using Communication for the Evolution of Scalable Role Alloc...

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16th Ibero-American Conference on Artificial Intelligence (IBERAMIA)

作者： Martins, Gustavo Urbano, Paulo Christensen, Anders Lyhne Univ Lisbon BioISI Fac Ciencias Lisbon Portugal IUL ISCTE Lisbon Portugal Inst Telecomuncacoes Lisbon Portugal

ISBN: (纸本)9783030039288;9783030039271

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals that represent roles. This idea was successfully applied to evolve communication-based role allocation for a two-role task. However, it was necessary to reward signal differentiation in the fitness function, which is a serious limitation as it does not generalize to tasks where the number of roles is unknown a priori. In this paper, we show that rewarding signal differentiation is not necessary to evolve communication-based role allocation strategies for the given task, and we improve reported scalability, while requiring less a priori knowledge. Our approach puts fewer constrains on the evolutionary process and enhances the potential of evolving communication-based role allocation for more complex tasks.

关键词： Role allocation Collective robotics Swarm NEAT Fitness function Artificial evolution evolutionary robotics

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Autonomous agents with control systems based on genetic algorithms

Autonomous agents with control systems based on genetic algo...

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12th IASTED International Conference on robotics and Applications (ICRA)

作者： Goschin, Sergiu Franti, Eduard Dascalu, Monica Pietraroiu, Marius Univ Politehn Bucuresti Spl Independentei 33Sec 6 Bucharest Romania IMT Bucharest Bucharest Romania

ISBN: (纸本)9780889865952

The purpose of this paper is to present a method of combining neural networks and genetic algorithms to create an efficient control system for a simulated autonomous robot in a 3D environment. The experiments concern the emergence of behaviours like obstacle avoidance, tar-et hitting and shortest path finding using a simple yet robust architecture. The evolutionary process takes place inside a dedicated, flexible framework that allows further development and testing.

关键词： evolutionary robotics simulation neural networks and genetic algorithms

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RESPONSE TO CHANGES IN KEY STIMULI THROUGH THE CO-EVOLUTION OF SENSOR MORPHOLOGY AND CONTROL

RESPONSE TO CHANGES IN KEY STIMULI THROUGH THE CO-EVOLUTION ...

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World Automation Congress 2008

作者： Parker, Gary Nathan, Pramod J. Connecticut Coll New London CT 06320 USA Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781889335384

Co-evolving a robot's sensor morphology and control program increases the potential that it can effectively complete its tasks and provides a means for adapting to changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. Although three sensor stimuli were detectable by the system, it used only two due to the relative importance of these two stimuli in completing the task. In the research presented in this paper, we used the same system, but reduced the availability of a key stimuli;the most effective solution now required the use of all three stimuli. The learning system still performed well by pacing sensors appropriate for the third stimuli and creating a program that utilized these sensors to successfully solve the problem.

关键词： Co-evolution Sensor Morphology Control Legged Robot Learning Hexapod Adaptation Stimuli evolutionary robotics evolutionary Computation Genetic Algorithm

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Generalizations of Evolved Decision-Making Mechanisms in Swarm Collective Perception 17th

Generalizations of Evolved Decision-Making Mechanisms in Swa...

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17th Italian Workshop on Artificial Life and evolutionary Computation (WIVACE)

作者： Trendafilov, Dari Almansoori, Ahmed Carletti, Timoteo Tuci, Elio Univ Namur Namur Inst Complex Syst Namur Belgium

ISBN: (纸本)9783031574290;9783031574306

This paper presents a study investigating the generalization characteristics of two neuro-controllers underpinning decision-making mechanisms in a swarm of robots engaged in a collective perception task. The neuro-controllers are both designed-using evolutionary computation-to operate in a randomly distributed cues environment, but under different conditions for what concerns the length of the communication range characterising the robots interactions. For one neuro-controller, the communication range during the design phase is 30 cm, while for the other is 50 cm. The aim of the study is to explore the robustness and the limitations of the two distinct neuro-controllers across a range of different conditions and to establish the optimal bounds on the swarm communication range for this collective perception task. To examine the performance of the two neuro-controllers we conduct a series of post-evaluations in 45 distinct environments, given by nine different distributions of the perceptual cues, and five different communication ranges (i.e., 10, 20, 30, 40, and 50 cm). The results demonstrate that the neuro-controller evolved with a swarm communication range of 30 cm generalizes better and exceeds the performance of the other neuro-controller evolved with 50 cm communication range in a vast majority of the post-evaluation conditions. However, the swarm performance degrades in conditions with patchily distributed perceptual cues and/or very short communication range.

关键词： Swarm robotics Collective perception evolutionary robotics

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A Robotic Ecosystem with Evolvable Minds and Bodies

A Robotic Ecosystem with Evolvable Minds and Bodies

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IEEE International Conference on Evolvable Systems (ICES)

作者： Weel, Berend Crosato, Emanuele Heinerman, Jacqueline Haasdijk, Evert Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781479944798

This paper presents a proof of concept demonstration of a novel evolutionary robotic system where robots can self-reproduce. We construct and investigate a strongly embodied evolutionary system, where not only the controllers, but also the morphologies undergo evolution in an on-line fashion. Forced by the lack of available hardware we build this system in simulation. However, we use a high quality simulator (Webots) and an existing hardware platform (Roombots) which makes the system, in principle, constructible. Our system can be perceived as an Artificial Life habitat, where robots with evolvable bodies and minds live in an arena and actively induce an evolutionary process 'from within', without a central evolutionary agency or a user-defined synthetic fitness function.

关键词： Embodied Evolution Robotic Organisms evolutionary robotics robotics Robot Morphology

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Exploring Proprioceptive Feedback in the Evolution of Modular Robots 18th

Exploring Proprioceptive Feedback in the Evolution of Modula...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Kargar, Babak Hosseinkhani Miras, Karine Eiben, A. E. Vrije Univ Amsterdam De Boelelaan 1105 NL-1081 HV Amsterdam Netherlands

ISBN: (纸本)9783031700705;9783031700712

We investigate an evolvable robot system where the body provides proprioceptive sensory signals to the controller (brain) about the positions of the joints. The key aspect we consider is whether all joints should be sensed or if sensing fewer joints would be better. We research this matter based on a test suite of twenty-two robots with various shapes and sizes and implement a system where the controller and the sensory signal system evolve together. Experiments with this system show that the evolved solutions use signals only from a fraction of the joints (25-51%) and perform better than the baseline, where all signals are used. This effect was observed across the majority of the test suite.

关键词： evolutionary robotics Proprioception Efficiency

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Cooperative Co-evolution and Adaptive Team Composition for a Multi-rover Resource Allocation Problem 1

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25th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference

作者： Fontbonne, Nicolas Maudet, Nicolas Bredeche, Nicolas Sorbonne Univ ISIR CNRS F-75005 Paris France Sorbonne Univ LIP6 F-75005 Paris France

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

In this paper, we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we propose to limit the number of agents which are updated in-between team evaluations. However, this raises two important problems with respect to (1) the cost of accurately estimating the marginal contribution of agents with respect to the team learning speed and (2) completing tasks where improving team performance requires multiple agents to update their policies in a synchronized manner. We introduce a CCEA algorithm that is capable of learning how to update just the right amount of agents' policies for the task at hand. We use a variation of the El Farol Bar problem, formulated as a multi-robot resource selection problem, to provide an experimental validation of the algorithms proposed.

关键词： ad hoc autonomous agent teams multi-agent systems marginal contribution team composition multi-robots cooperative co-evolutionary algorithms (CCEA) evolutionary computation evolutionary robotics

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Localization Uncertainty-driven Adaptive Framework for Controlling Ground Vehicle Robots

Localization Uncertainty-driven Adaptive Framework for Contr...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kent, Daniel McKinley, Philip K. Radha, Hayder Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI USA

ISBN: (纸本)9781728162126

Modern localization techniques allow ground vehicle robots to determine their position with centimeter-level accuracy under nominal conditions, enabling them to utilize fixed maps to navigate their environments. However, when localization measurements become unavailable, the position accuracy will drop and uncertainty will increase. While research and development on localization estimation seeks to reduce the severity of these outages, the question of what actions a robot should take under high localization uncertainty is still unresolved, and can vary on a platform-by-platform and mission-by-mission basis. In this paper, we exploit localization uncertainty measures to adapt system control parameters in real time. Offline, we optimize non-linear activation functions whose control parameters and relevant weights are trained and learned using evolutionary Algorithm (EA). Subsequently, in real time, we apply the optimized adaptation functions to the controller look-ahead distance and intermediate linear and angular velocity commands, which we identify as the most sensitive to localization error. evolutionary runs are conducted in which a simulated target vehicle is tasked with following a randomly generated path while minimizing cross-track error, with time varying localization uncertainty added. These runs produce situation-dependent weights for parameters to the adaptation functions, which are transferred to the physical platform, a 1:5-scale autonomous vehicle. In simulation, our system was able to reduce cross-track error, which in certain cases exceeds 250 centimeters on non-adapted systems, to below 15 centimeters on average using EA-derived weights and parameters applied to our proposed adaptation system. Evaluation on the physical platform demonstrates that without the adaptation module in place, the platform is unable to successfully follow the path;with the adaptation module, the platform automatically adjusts its velocity and look-ahead distance to compensat

关键词： autonomous vehicle localization path planning uncertainty evolutionary robotics Robot Operating System

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