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IEEE Congress on evolutionary Computation (CEC)

作者： Parker, Gary B. Tarimo, William T. Connecticut Coll Dept Comp Sci New London CT 06320 USA

ISBN: (纸本)9781424478354

Various selection schemes have been described for use in genetic algorithms. This paper investigates the effects of adding greediness to the standard roulette-wheel selection. The results of this study are tested on a Cyclic Genetic Algorithm (CGA) used for learning gaits for a hexapod servo-robot. The effectiveness of CGA in learning optimal gaits with selection based on roulette-wheel selection with and without greediness is compared. The results were analyzed based on fitness of the individual gaits, convergence time of the evolution process, and the fitness of the entire population evolved. Results demonstrate that selection with too much greediness tends to prematurely converge with a sub-optimal solution, which results in poorer performance compared to the standard roulette-wheel selection. On the other hand, roulette-wheel selection with very low greediness evolves more diverse and fitter populations with individuals that result in the desired optimal gaits.

关键词： Genetic Algorithm Cyclic Control Hexapod Greedy Selection Gait evolutionary robotics Learning Control Cyclic Genetic Algorithm

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On Using Gene Expression Programming to Evolve Multiple Output Robot Controllers

On Using Gene Expression Programming to Evolve Multiple Outp...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Mwaura, J. Keedwell, Ed Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Univ Exeter CEMPS Dept Comp Sci Exeter Devon England

ISBN: (纸本)9781479944798

Most evolutionary algorithms (EAs) represents a potential solution to a problem as a single-gene chromosome encoding, where the chromosome gives only one output to the problem. However, where more than one output to a problem is required such as in classification and robotic problems, these EAs have to be either modified in order to deal with a multiple output problem or are rendered incapable of dealing with such problems. This paper investigates the parallelisation of genes as independent chromosome entities as described in the Gene Expression Programming (GEP) algorithm. The aim is to investigate the capabilities of a multiple output GEP (moGEP) technique and compare its performance to that of a single-gene GEP chromosome (ugGEP). In the described work, the two GEP approaches are utilised to evolve controllers for a robotic obstacle avoidance and exploration behaviour. The obtained results shows that moGEP is a robust technique for the investigated problem class as well as for utilisation in evolutionary robotics.

关键词： Gene Expression Programming Robot Control evolutionary robotics

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Design of Powered Floor Systems for Mobile Robots with Differential Evolution 1

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22nd International Conference on Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Medvet, Eric Seriani, Stefano Bartoli, Alberto Gallina, Paolo Univ Trieste Dept Engn & Architecture Trieste Italy

ISBN: (数字)9783030166922

ISBN: (纸本)9783030166915;9783030166922

Mobile robots depend on power for performing their task. Powered floor systems, i.e., surfaces with conductive strips alternatively connected to the two poles of a power source, are a practical and effective way for supplying power to robots without interruptions, by means of sliding contacts. Deciding where to place the sliding contacts so as to guarantee that a robot is actually powered irrespective of its position and orientation is a difficult task. We here propose a solution based on Differential Evolution: we formally define problem-specific constraints and objectives and we use them for driving the evolutionary search. We validate experimentally our proposed solution by applying it to three real robots and by studying the impact of the main problem parameters on the effectiveness of the evolved designs for the sliding contacts. The experimental results suggest that our solution may be useful in practice for assisting the design of powered floor systems.

关键词： Multi-objective optimization Automatic design Swarm robotics evolutionary robotics

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Geodesics, Non-linearities and the Archive of Novelty Search 22

Geodesics, Non-linearities and the Archive of Novelty Search

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Genetic and evolutionary Computation Conference (GECCO)

作者： Salehi, Achkan Coninx, Alexandre Doncieux, Stephane Sorbonne Univ CNRS ISIR F-75005 Paris France

ISBN: (纸本)9781450392686

The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short note focuses on the effects of the archive on exploration. Experimental evidence from a few application domains suggests that archive-based NS performs in general better than when Novelty is solely computed with respect to the population. An argument that is often encountered in the literature is that the archive prevents exploration from backtracking or cycling, i.e. from revisiting previously encountered areas in the behavior space. We argue that this is not a complete or accurate explanation as backtracking -beside often being desirable -can actually be enabled by the archive. Through low-dimensional/analytical examples, we show that a key effect of the archive is that it counterbalances the exploration biases that result, among other factors, from the use of inadequate behavior metrics and the non-linearities of the behavior mapping. Our observations seem to hint that attributing a more active role to the archive in sampling can be beneficial.

关键词： evolutionary robotics Complex Systems Theory

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Hybrid Control for a Real Swarm robotics System in an Intruder Detection Task 19th

Hybrid Control for a Real Swarm Robotics System in an Intrud...

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19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Duarte, Miguel Gomes, Jorge Costa, Vasco Oliveira, Sancho Moura Christensen, Anders Lyhne BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Inst Univ Lisboa ISCTE IUL Lisbon Portugal Fac Ciencias BioISI Lisbon Portugal

ISBN: (纸本)9783319311531

Control design is one of the prominent challenges in the field of swarm robotics. evolutionary robotics is a promising approach to the synthesis of self-organized behaviors for robotic swarms but it has, so far, only produced been shown in relatively simple collective behaviors. In this paper, we explore the use of a hybrid control synthesis approach to produce control for a swarm of aquatic surface robots that must perform an intruder detection task. The robots have to go to a predefined area, monitor it, detect and follow intruders, and manage their energy levels by regularly recharging at a base station. The hybrid controllers used in our experiments rely on evolved behavior primitives that are combined through a manually programmed high-level behavior arbitrator. In simulation, we show how simple modifications to the behavior arbitrator can result in different swarm behaviors that use the same underlying behavior primitives, and we show that the composed behaviors are scalable with respect to the swarm size. Finally, we demonstrate the synthesized controller in a real swarm of robots, and show that the behavior successfully transfers from simulation to reality.

关键词： Swarm robotics evolutionary robotics Hybrid control Self-organization Aquatic robots

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Cooperative hole avoidance in a swarm-bot

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robotics AND AUTONOMOUS SYSTEMS 2006年第2期54卷 97-103页

作者： Trianni, V Nolfi, S Dorigo, M Univ Libre Bruxelles IRIDIA Brussels Belgium CNR Inst Cognit Sci & Technol Rome Italy

In this paper, we Study coordinated motion in a swarm robotic system, called a swarm-bot. A swann-bot is a self-assembling and self-organising artifact, composed of a swarm of s-bots, mobile robots with the ability to connect to and disconnect from each other. The swann-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes. In such a scenario, individual s-bots have sensory-motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots' ability to physically connect to each other. In order to synthesise the s-bots' controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task. (c) 2005 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics swarm intelligence swarm robotics swarm-bot

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Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot

Evolving internal reinforcers for an intrinsically motivated...

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6th IEEE International Conference on Development Learning

作者： Schembri, Massimiliano Mirolli, Marco Baldassarre, Gianluca CNR LARAL ISTC I-00185 Rome Italy

ISBN: (纸本)9781424411153

Intrinsically Motivated Reinforcement Learning (IMRL) has been proposed as a framework within which agents exploit "internal reinforcement" to acquire general-purpose building-block behaviors ("skills") which can be later combined for solving several specific tasks. The architectures so far proposed within this framework are limited in that: (1) they use hardwired "salient events" to form and train skills, and this limits agents' autonomy;(2) they are applicable only to problems with abstract states and actions, as grid-world problems. This paper proposes solutions to these problems in the form of a hierarchical reinforcement-learning architecture that: (1) exploits evolutionary robotics techniques so to allow the system to autonomously discover "salient events";(2) uses neural networks so to allow the system to cope with continuous states and noisy environments. The viability of the proposed approach is demonstrated with a simulated robotic scenario.

关键词： intrinsically motivated reinforcement learning evolutionary robotics actor-ctitic surprise neural networks

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Exploring and Evolving Process-oriented Control for Real and Virtual Fire Fighting Robots 12

Exploring and Evolving Process-oriented Control for Real and...

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14th International Conference on Genetic and evolutionary Computation Conference (GECCO)

作者： Hardey, Kathryn Corapcioglu, Eren Mattis, Molly Goadrich, Mark Jadud, Matthew Centenary Coll Louisiana Shreveport LA 71104 USA

ISBN: (纸本)9781450311779

Current research in evolutionary robotics is largely focused on creating controllers by either evolving neural networks or refning genetic programs based on grammar trees. We propose the use of the dataow languages for the construction of effective robotic controllers and the evolution of new controllers using genetic programming techniques. These languages have the advantages of being built on concurrent execution frameworks that lend themselves to formal verification along with being visualized as a dataow graph. In this paper, we compare and contrast the development and subsequent evolution of one such process-oriented control algorithm. Our control software was built from compos-able, communicating processes executing in parallel, and we tested our solution in an annual fire-fighting robotics competition. Subsequently, we evolved new controllers in a virtual simulation of this parallel dataow domain, and in doing so discovered and quantified more efficient solutions. This research demonstrates the effectiveness of using process networks as the basis for evolutionary robotics.

关键词： evolutionary robotics Concurreny CSP occam-pi

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Exploiting Multiple Robots to Accelerate Self-Modeling 07

Exploiting Multiple Robots to Accelerate Self-Modeling

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Bongard, Josh Univ Vermont Dept Comp Sci Burlington VT 05405 USA

ISBN: (纸本)9781595936974

In previous work [8] a computational framework was demonstrated that allows a mobile robot to autonomously evolve models its own body for the purposes of adaptive behavior generation or recovery from damage. Conceivably, robots working in tandem could share their experiences such that one robot, when faced with a situation already encountered by another robot, could draw on that experience and adapt more rapidly. A first demonstration of this is given here: multiple robots with the same or similar body plan, but acting independently, combine self-models such that they accelerate modeling. Two approaches are investigated: the robots feed their experiences back into a common modeling engine, or they maintain their own modeling engine but share their best self-models with each other. It was found that the latter approach achieves a, significant improvement in modeling compared to a single robot and compared to the former approach. This finding has implications for how to design autonomous robots acting in concert to achieve large-scale tasks.

关键词： evolutionary robotics self-modeling artificial intelligence

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Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarms Using Embodied Evolution 8

Achieving Connectivity Between Wide Areas Through Self-Organ...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Hansen, Erik Aaron Nichele, Stefano Yazidi, Anis Haugerud, Harek Mofrad, Asieh Abolpour Alcocer, Alex Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Oslo Metropolitan Univ Dept Mech Elect & Chem Engn Oslo Norway

ISBN: (纸本)9781538692769

Abruptions to the communication infrastructure might occur occasionally, where manual intervention by dedicated personnel is needed to fix the interruptions, restoring communication abilities. However, sometimes this can be dangerous to the personnel carrying out the task, which can be the case in environmental disasters like earthquakes or toxic spills or in the occurrence of tire. Therefore, personal risks can he minimised if autonomous robots are deployed to achieve the same outcome: to establish a communication link between two previously connected but distant sites. In this paper, we investigate the deployment of mobile ad hoc robots which relay traffic between two sites. In order to get the robots to locate themselves appropriately, we take inspiration from self-organisation and emergence in artificial life, where a common overall goal may be achieved if the correct local rules on the agents in the system are invoked. We integrate the aspect of connectivity between two sites into the multirobot simulation platform known as JBotEvolver. The robot swarm is composed of Thymio II robots. In addition, we compare three heuristics, of which one uses neuroevolution (evolution of neural networks) to show how self-organisation and embodied evolution can be used within the integration. Our use of embodiment in robotic controllers shows promising results and provides solid knowledge and guidelines for further investigations.

关键词： Swarm robotics evolutionary robotics Embodied Evolution Internet of Things

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