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Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

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ARTIFICIAL LIFE AND robotics 2016年第3期21卷 317-323页

作者： Drozd, Aleksandr Witkowski, Olaf Matsuoka, Satoshi Ikegami, Takashi Tokyo Inst Technol Global Sci Informat & Comp Ctr Tokyo Japan Tokyo Inst Technol Earth Life Sci Inst Tokyo Japan Univ Tokyo Dept Multidisciplinary Sci Tokyo Japan

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.

关键词： Artificial life Artificial neural networks Bioinspired computation evolutionary robotics Foraging Swarming Treecode

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Autonomous task allocation by artificial evolution for robotic swarms in complex tasks

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ARTIFICIAL LIFE AND robotics 2019年第1期24卷 127-134页

作者： Wei, Yufei Hiraga, Motoaki Ohkura, Kazuhiro Car, Zlatan Hiroshima Univ Grad Sch Engn 1-4-1 Kagamiyama Hiroshima 7398527 Japan Univ Rijeka Fac Engn Vukovarska 58 Rijeka 51000 Croatia

Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issuesthe bootstrapping problem and deceptionespecially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach.

关键词： Robotic swarm evolutionary robotics Autonomous task allocation Task partitioning

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A dynamic navigation for autonomous mobiles robots

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2016年第1期10卷 81-91页

作者： Benmachiche, Abdelmadjid Tahar, Bouhadada Tayeb, Laskri Mohamed Asma, Zendi Univ Badji Mokhtar LRI GReLearn Dept Comp Sci Annaba Algeria

In this paper, we discuss cooperative approach to solve the problem of navigation for autonomous mobile robots in fully dynamic environments, it is about building an environment of robotics specific to a construction site where such robots must cooperate to deal with the functions related to the site. For that, we used the power of the biological inspiration;the genetic algorithms (GA's) because theirs capacity to solve hard problems in short time in any environment with increasing complexity which are an interesting alternative to conventional methods of path planning. Our work is to determine the optimal path of several mobile robots using genetic algorithms in a dynamic environment. The evaluation of paths is a "fitness" function based on the path length. This method is implemented and tested on several scenarios. The results demonstrate the robustness and performance of our approach.

关键词： Construction site evolutionary robotics genetic algorithm mobile robots multi-robot system

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Linear reactive control for efficient 2D and 3D bipedal walking over rough terrain

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ADAPTIVE BEHAVIOR 2013年第1期21卷 29-46页

作者： Solomon, Joseph H. Locascio, Mark A. Hartmann, Mitra J. Z. Northwestern Univ Dept Biomed Engn Evanston IL 60208 USA Northwestern Univ Dept Mech Engn Evanston IL 60208 USA

The kinematics of human walking are largely driven by passive dynamics, but adaptation to varying terrain conditions and responses to perturbations require some form of active control. The basis for this control is often thought to take the form of entrainment between a neural oscillator (i.e., a central pattern generator and/or distributed counterparts) and the mechanical system. Here we use techniques in evolutionary robotics to explore the potential of a purely reactive, linear controller to control bipedal locomotion over rough terrain. In these simulation studies, joint torques are computed as weighted linear sums of sensor states, and the weights are optimized using an evolutionary algorithm. We show that linear reactive control can enable a seven-link 2D biped and a nine-link 3D biped to walk over rough terrain (steps of similar to 5% leg length or more in the 2D case). In other words, the simulated walker gradually learns the appropriate weights to achieve stable locomotion. The results indicate that oscillatory neural structures are not necessarily a requirement for robust bipedal walking. The study of purely reactive control through linear feedback may help to reveal some basic control principles of stable walking.

关键词： Bipedal walking dynamic walking biped reactive control rough terrain evolutionary robotics

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Behavioural plasticity in evolving robots

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THEORY IN BIOSCIENCES 2016年第4期135卷 201-216页

作者： Carvalho, Jonata Tyska Nolfi, Stefano CNR Inst Cognit Sci & Technol Via S Martino Battaglia 44 I-00185 Rome Italy Fed Univ Rio Grande Ctr Computat Sci C3 Av ItaliaKm 8 BR-96203900 Rio Grande Brazil

In this paper, we show how the development of plastic behaviours, i.e., behaviour displaying a modular organisation characterised by behavioural subunits that are alternated in a context-dependent manner, can enable evolving robots to solve their adaptive task more efficiently also when it does not require the accomplishment of multiple conflicting functions. The comparison of the results obtained in different experimental conditions indicates that the most important prerequisites for the evolution of behavioural plasticity are: the possibility to generate and perceive affordances (i.e., opportunities for behaviour execution), the possibility to rely on flexible regulatory processes that exploit both external and internal cues, and the possibility to realise smooth and effective transitions between behaviours.

关键词： Behavioural plasticity evolutionary robotics Multiple behaviours Autonomous robotics Modularity Action switching

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Premature convergence in morphology and control co-evolution: a study

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ADAPTIVE BEHAVIOR 2024年第2期32卷 137-165页

作者： Eguiarte-Morett, Luis Aguilar, Wendy Univ Nacl Autonoma Mexico Posgrad Ciencia & Ingn Comp Mexico City Mexico Univ Nacl Autonoma Mexico Dept Comp Sci Inst Invest Matemat Aplicadas & Sistemas Mexico City Mexico Univ Nacl Autonoma Mexico Dept Comp Sci Inst Invest Matemat Aplicadas & Sistemas Mexico City 04510 Mexico

This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.

关键词： evolutionary robotics morphology and control co-evolution premature convergence morphological diversity comparative analysis

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Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers

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ADAPTIVE BEHAVIOR 2011年第3期19卷 187-207页

作者： Hoinville, Thierry Siles, Cecilia Tapia Henaff, Patrick Italian Inst Technol I-16163 Genoa Italy Univ Cergy Pontoise ETIS ETIS Lab CNRSUMR 8051 Cergy Pontoise France

In evolutionary robotics, plastic neural network models proved to be promising for evolving adaptive behaviors. In particular, neurocontrollers incorporating hebbian synapses have been shown to be useful for implementing conflicting sub-behaviors. Numerous interesting complex tasks assume such flexibility. However, those evolved controllers often exhibit behavioral instability, as simulation time is extended beyond the short limit used during evolution. In this paper, we propose constrained plastic models inspired by neural homeostasis phenomena, in order to evolve flexible and stable pattern generators for single-legged locomotion. Comparative results show that constrained controllers perform better than unconstrained ones in both terms of evolvability and behavioral stability. Functional analyses of the best evolved controller unveil the adaptivity, robustness and homeostasis arising from the statically constrained plasticity. Interestingly, homeostasis evolved implicitly without relying on any active homeostatic mechanisms and is implemented through hebbian plasticity, usually considered destabilizing.

关键词： Homeostasis plastic neural networks adaptive synapses legged locomotion evolutionary robotics

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Evolved Transistor Array Robot Controllers

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evolutionary COMPUTATION 2020年第4期28卷 677-708页

作者： Garvie, Michael Flascher, Ittai Philippides, Andrew Thompson, Adrian Husbands, Phil Univ Sussex Dept Informat Brighton BN1 9QJ E Sussex England Binderr Haifa Israel

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.

关键词： Evolvable hardware evolutionary robotics evolved analog circuits incremental evolution visually guided behaviour word recognition field programmable transistor array feature selection evolved visual features dimensionality reduction

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Exploring coevolutionary relations by alterations in fitness function: Experiments with simulated robots

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2006年第3期47卷 257-284页

作者： Mermigkis, Ioannis Petrou, Loukas Aristotle Univ Thessaloniki Fac Engn Dept Elect & Comp Engn Div Elect & Comp Engn GR-54124 Thessaloniki Greece

This paper focuses on various coevolutionary robotic experiments where all parameters except for the fitness function remain the same. Initially an attempt to categorize coevolutionary experiments is made and subsequently three experiments of competitive coevolution (hunt, battle and mating) are presented. The experiment concerning implicit competition of two species (mating) is given special attention as it shows emergence of compromise and collaboration through a competitive environment. The co-evolution progress monitoring is evaluated through fitness graphs, CIAO and Hamming maps and the results are interpreted for each experimental setup. The paper concludes that despite the alteration of fitness functions, several evasion-pursuit elements emerge. Furthermore, conciliatory strategies can emerge in implicit competitional cases.

关键词： coevolutionary relations competitive coevolution evolution of collaboration evolutionary robotics

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Evolution of neural controllers for locomotion and obstacle avoidance in a six-legged robot

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CONNECTION SCIENCE 1999年第3-4期11卷 225-242页

作者： Filliat, D Kodjabachian, J Meyer, JA OASIS LIP 6 Animat Lab F-75252 Paris 05 France

This article describes how the SGOCE paradigm has been used within the context of a 'minimal simulation' strategy to evolve neural networks controlling locomotion and obstacle avoidance in a six-legged robot. A standard genetic algorithm has been used to evolve developmental programs according to which recurrent networks of leaky-integrator neurons were grown in a user-provided developmental substrate and were connected to the robot:sensors and actuators. Specific grammars have been used to limit the complexity of the developmental programs and of the corresponding neural controllers. Such controllers were first evolved through simulation and then successfully downloaded on the real robot.

关键词： evolutionary robotics neural controllers locomotion obstacle avoidance walking robot

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