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2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)

作者： Yu, Tian Yasuda, Toshiyuki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Engn Hiroshima 730 Japan

ISBN: (纸本)9784907764487

Swarm robotics is a new approach to the coordination of large numbers of homogeneous robots that takes inspiration from social insects. One of the major topics of SRS that have been getting attention from the early stage is the design methodology of robot controllers. On the other hand, no general methods for analyzing its collective behavior have been developed so far. In this paper, we propose a method based on the duration of subgroup's behavioral sequence for analyzing task allocation in subgroups of a swarm robotics system. The proposed method shows that SRS takes most of the time searching for food sources and the behavior sequences duration over 100 time steps increased as the result of artificial evolution, which helps us understand the role changing of subgroups in the food foraging problem from the view point of duration time.

关键词： swarm robotics system evolutionary robotics duration time complex networks

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NERD Neurodynamics and evolutionary robotics Development Kit

NERD Neurodynamics and Evolutionary Robotics Development Kit

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2nd International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

作者： Rempis, Christian Thomas, Verena Bachmann, Ferry Pasemann, Frank Univ Osnabruck Inst Cognit Sci D-49069 Osnabruck Germany

ISBN: (纸本)9783642173189

The aim of evolutionary robotics is to develop neural systems for behavior control of autonomous robots. For non-trivial behaviors or non-trivial machines the implementation effort for suitably specialized simulators and evolution environments is often very high. The Neurodynamics and evolutionary robotics Development Kit (NERD), presented in this article, is a free open-source framework to rapidly implement such applications. It includes separate libraries (1) for the simulation of arbitrary robots in dynamic environments, allowing the exchange of underlying physics engines, (2) the simulation, manipulation and analysis of recurrent neural networks for behavior control, and (3) an extensible evolution framework with a number of neuro-evolution algorithms. NERD comes with a set of applications that can be used directly for many evolutionary robotics experiments. Simulation scenarios and specific extensions can be defined via XML, scripts and custom plug-ins. The NERD kit is available at *** under the GPL license.

关键词： Simulation evolutionary robotics Neuro-evolution

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Engineering Online Evolution of Robot Behaviour 15

Engineering Online Evolution of Robot Behaviour

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International Conference on Autonomous Agents and Multiagent Systems

作者： Fernando Silva Anders Lyhne Christensen Luis Correia BioMachines Lab & Instituto de Telecomunicacoes & BioISI

ISBN: (纸本)9781450337717

evolutionary computation techniques have been widely studied to automate the synthesis of behavioural control for robots. In online evolution, an evolutionary algorithm is executed on the robots themselves during task execution so as to continuously optimise the robot controllers. On-line evolution provides numerous potential benefits, including enabling robots to modify their behaviour in response to changes in the task and in the environment. Current approaches to online evolution on physical robots, however, often require a prohibitively long evolution time and a substantial amount of human experimentation, and have not yet scaled to complex real-world tasks. In this research, we study how to accelerate and scale online evolution to more complex tasks while minimising the amount of human intervention. Our ultimate objective is to enable the realisation of real-world multirobot systems that can effectively learn new behaviours and adapt online to take on dynamic tasks in a timely manner.

关键词： Adaptation and learning Artificial neural network evolutionary robotics Online evolution Robot controller

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Multi-Objective vs. Single-Objective evolutionary Algorithms for Hybrid Mobile Robot Optimization

Multi-Objective vs. Single-Objective Evolutionary Algorithms...

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IEEE International Symposium on robotics and Manufacturing Automation (IEEE-ROMA)

作者： Lim, S. H. Teo, J. Univ Malaysia Sabah Fac Comp & Informat Evolutionary Comp Lab Kota Kinabalu Malaysia

ISBN: (纸本)9781479957651

In this paper, the comparison of Multi-Objective evolutionary Algorithm (MOEA) and Single-Objective evolutionary Algorithm (SOEA) in designing and optimizing the morphology of a Six Articulated-Wheeled Robot (SAWR) is presented. Results show that both methods are able to produce optimized SAWR which have smaller size with the capability to perform climbing motion. However, one of the solutions from the Pareto-set of MOEA is outperforming the fittest solution from SOEA. The solution is able to achieve the same performance of the fittest solution from SOEA and yet it is smaller in size. Besides that, another advantage of using MOEA is that MOEA is capable to produce a set of Pareto optimal solutions from the smallest SAWR with poor performance to the largest SAWR with robust performance which provide users a choice of solutions for trade-off between the two objectives.

关键词： multi-objective evolutionary algorithm single-objective evolutionary algorithm evolutionary robotics evolved hybrid mobile robot

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Evolved Machines Shed Light on Robustness and Resilience

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PROCEEDINGS OF THE IEEE 2014年第5期102卷 899-914页

作者： Bongard, Josh Lipson, Hod Univ Vermont Dept Comp Sci Burlington VT 05405 USA Cornell Univ Sibley Sch Mech & Aerosp Engn Ithaca NY 14853 USA

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.

关键词： Embodied cognition evolutionary robotics

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Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第2期18卷 145-166页

作者： Lones, Michael A. Fuente, Luis A. Turner, Alexander P. Caves, Leo S. D. Stepney, Susan Smith, Stephen L. Tyrrell, Andy M. Univ York York YO10 5DD N Yorkshire England

Biological organisms exist within environments in which complex nonlinear dynamics are ubiquitous. They are coupled to these environments via their own complex dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behavior of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organization of biochemical networks. We refer to these as artificial biochemical networks and show how they can evolve to control trajectories within three behaviorally diverse complex dynamical systems: 1) the Lorenz system;2) Chirikov's standard map;and 3) legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks (ABNs). We find both approaches to be advantageous in certain situations, though we note that the relative tradeoffs between different models of ABN strongly depend on the type of dynamical systems being controlled.

关键词： Biochemical networks chaos control dynamical systems evolutionary robotics genetic programming

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An Evolved Climbing SAWR: From Simulation to Realization with 3D Printing

An Evolved Climbing SAWR: From Simulation to Realization wit...

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Asian Control Conference

作者： S. H. LIM J. TEO Evolutionary Computing Laboratory Faculty of Computing and Informatics Universiti Malaysia Sabah

ISBN: (纸本)9781479978632

This paper presents the methodology from evolving a climbing Six Articulated-Wheeled Robot (SAWR) to realizing the simulation result for physical-testing with 3D printing fabrication. The design of the SAWR is obtained from a single-objective evolution process where the morphology of a climbing SAWR is optimized (minimized). The fittest SAWR obtained from the simulation is then fabricated with 3D printing technology and assembled with sensors and motors for real-world testing. Results show that the fabricated SAWR is able to achieve 80% of the performance of the fittest SAWR in the simulation.

关键词： Evolved hybrid robots Simulation to real world robots 3d-printed robots Robots fabrication evolutionary robotics

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On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

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NEURAL COMPUTING & APPLICATIONS 2014年第5期25卷 1063-1076页

作者： Tuci, Elio Trianni, Vito Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3RD Dyfed Wales ISTC CNR I-00185 Rome Italy

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution.

关键词： evolutionary robotics Homogeneous and heterogeneous teams Role allocation

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Reconfigurable swarm robots produce self-assembling and self-repairing organisms

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robotics AND AUTONOMOUS SYSTEMS 2014年第10期62卷 1371-1376页

作者： Levi, Paul Meister, Eugen Schlachter, Florian Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

Reconfigurable robots are set to become a vital factor in the theoretical development and practical utilization of robotics. The core problem in this scientific area is steady information transfer between a swarm and its organisms and vice versa. To this end, we present a basic theoretical framework that stipulates the interoperation between the two modes. We evaluate our proposed framework by constructing 100 mobile microrobots of three different types that initiate the processes of self-reconfigurability and self-repair. The autonomous decision to self-aggregate to an organism mainly derives from the necessity to overcome existing obstructive environmental conditions, e.g. ramps or clefts. The methodological dichotomy that we have chosen to evaluate our concept was to pursue in parallel an approach based on embodied distributed cognition and an evolutionary path mainly based on artificial genomes and reproduction. In this paper, we evaluate these two different approaches in two distinct grand challenges and present the main results. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Swarm robotics Modular and reconfigurable robots Cognitive framework evolutionary robotics

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Vector-valued function estimation by grammatical evolution for autonomous robot control

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INFORMATION SCIENCES 2014年 258卷 182-199页

作者： Burbidge, Robert Wilson, Myra S. Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

An autonomous mobile robot requires a robust onboard controller that makes intelligent responses in dynamic environments. Current solutions tend to lead to unnecessarily complex solutions that only work in niche environments. evolutionary techniques such as genetic programming (GP) can successfully be used to automatically program the controller, minimizing the limitations arising from explicit or implicit human design criteria, based on the robot's experience of the world. Grammatical evolution (GE) is a recent evolutionary algorithm that has been applied to various problems, particularly those for which GP has performed. We formulate robot control as vector-valued function estimation and present a novel generative grammar for vector-valued functions. A consideration of the crossover operator leads us to propose a design criterion for the application of GE to vector-valued function estimation, along with a second novel generative grammar which meets this criterion. The suitability of these grammars for vector-valued function estimation is assessed empirically on a simulated task for the Khepera robot. (C) 2013 Elsevier Inc. All rights reserved.

关键词： evolutionary robotics Grammatical evolution Genetic programming Vector-valued function Ripple crossover Schema

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