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On the evolution of adaptable and scalable mechanisms for collective decision-making in a swarm of robots

SWARM INTELLIGENCE 2024年第1期18卷 79-99页

作者： Almansoori, Ahmed Alkilabi, Muhanad Tuci, Elio Univ Namur Fac Comp Sci Rue Bruxelles 61 B-5000 Namur Belgium Univ Kerbala Fac Engn Karbala Iraq

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots.

关键词： evolutionary robotics Swarm robotics Collective decision-making Collective perception

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Information-theoretic decomposition of embodied and situated systems

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NEURAL NETWORKS 2018年 103卷 94-107页

作者： Da Rold, Federico Plymouth Univ Sch Comp Elect & Math Plymouth PL4 8AA Devon England

The embodied and situated view of cognition stresses the importance of real-time and nonlinear bodily interaction with the environment for developing concepts and structuring knowledge. In this article, populations of robots controlled by an artificial neural network learn a wall-following task through artificial evolution. At the end of the evolutionary process, time series are recorded from perceptual and motor neurons of selected robots. Information-theoretic measures are estimated on pairings of variables to unveil nonlinear interactions that structure the agent-environment system. Specifically, the mutual information is utilized to quantify the degree of dependence and the transfer entropy to detect the direction of the information flow. Furthermore, the system is analyzed with the local form of such measures, thus capturing the underlying dynamics of information. Results show that different measures are interdependent and complementary in uncovering aspects of the robots' interaction with the environment, as well as characteristics of the functional neural structure. Therefore, the set of information-theoretic measures provides a decomposition of the system, capturing the intricacy of nonlinear relationships that characterize robots' behavior and neural dynamics. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Embodied cognition Situated cognition evolutionary robotics Information theory Antireductionism

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Integrating high-level cognitive systems with sensorimotor control

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ADVANCED ENGINEERING INFORMATICS 2010年第1期24卷 76-83页

作者： Spranger, Michael Thiele, Christian Hild, Manfred Humboldt Univ Dept Comp Sci Neurorobot Res Lab Artificial Intelligence Workgrp D-10099 Berlin Germany Sony Comp Sci Lab F-75005 Paris France

This paper presents a software system that integrates different computational paradigms to solve cognitive tasks of different levels. The system has been employed to empower research on very different platforms ranging from simple two-wheeled structures with only a few cheap sensors, to complex two-legged humanoid robots, with many actuators, degrees of freedom and sensors. It is flexible and adjustable enough to be used in part or as a whole, to target different research domains projects and questions, including evolutionary robotics, RoboCup and Artificial Language Evolution on Autonomous Robots (ALEAR, an EU funded cognitive systems project). In contrast to many other frameworks, the system is such that researchers can quickly adjust the system to different problems and platforms, while allowing maximum reuse of components and abstractions, separation of concerns and extensibility. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： HIGH-level programming languages SOFTWARE architecture DETECTORS ACTUATORS evolutionary robotics ARTIFICIAL languages CYBERNETICS

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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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Bio-inspired grasp control in a robotic hand with massive sensorial input

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BIOLOGICAL CYBERNETICS 2009年第2期100卷 109-128页

作者： Ascari, Luca Bertocchi, Ulisse Corradi, Paolo Laschi, Cecilia Dario, Paolo Scuola Super Sant Anna Ctr Excellence Informat Commun Engn CEIIC Pisa Italy HENESIS SRL Parma Italy Scuola Super Sant Anna Ctr Res Micro & Nanoengn CRIM Pisa Italy Scuola Super Sant Anna ARTS Lab Pisa Italy

The capability of grasping and lifting an object in a suitable, stable and controlled way is an outstanding feature for a robot, and thus far, one of the major problems to be solved in robotics. No robotic tools able to perform an advanced control of the grasp as, for instance, the human hand does, have been demonstrated to date. Due to its capital importance in science and in many applications, namely from biomedics to manufacturing, the issue has been matter of deep scientific investigations in both the field of neurophysiology and robotics. While the former is contributing with a profound understanding of the dynamics of real-time control of the slippage and grasp force in the human hand, the latter tries more and more to reproduce, or take inspiration by, the nature's approach, by means of hardware and software technology. On this regard, one of the major constraints robotics has to overcome is the real-time processing of a large amounts of data generated by the tactile sensors while grasping, which poses serious problems to the available computational power. In this paper a bio-inspired approach to tactile data processing has been followed in order to design and test a hardware-software robotic architecture that works on the parallel processing of a large amount of tactile sensing signals. The working principle of the architecture bases on the cellular nonlinear/neural network (CNN) paradigm, while using both hand shape and spatial-temporal features obtained from an array of microfabricated force sensors, in order to control the sensory-motor coordination of the robotic system. Prototypical grasping tasks were selected to measure the system performances applied to a computer-interfaced robotic hand. Successful grasps of several objects, completely unknown to the robot, e.g. soft and deformable objects like plastic bottles, soft balls, and Japanese tofu, have been demonstrated.

关键词： ROBOT hands ROBOTS -- Control systems evolutionary robotics robotics

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Exploring Bipedal Hopping through Computational Evolution

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ARTIFICIAL LIFE 2019年第3期25卷 236-249页

作者： Moore, Jared M. Shine, Catherine L. McGowan, Craig P. McKinley, Philip K. Grand Valley State Univ Sch Comp & Informat Syst Allendale MI 49401 USA Hartpury Coll Dept Anim Sci Hartpury England Univ Idaho Dept Biol Sci Moscow ID 83843 USA Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Bipedal hopping is an efficient form of locomotion, yet it remains relatively rare in the natural world. Previous research has suggested that the tail balances the angular momentum of the legs to produce steady state bipedal hopping. In this study, we employ a 3D physics simulation engine to optimize gaits for an animat whose control and morphological characteristics are subject to computational evolution, which emulates properties of natural evolution. Results indicate that the order of gene fixation during the evolutionary process influences whether a bipedal hopping or quadrupedal bounding gait emerges. Furthermore, we found that in the most effective bipedal hoppers the tail balances the angular momentum of the torso, rather than the legs as previously thought. Finally, there appears to be a specific range of tail masses, as a proportion of total body mass, wherein the most effective bipedal hoppers evolve.

关键词： evolutionary robotics bipedal hopping biomechanics genetic algorithm gait analysis

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'Feeling' the flow of time through sensorimotor co-ordination

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CONNECTION SCIENCE 2004年第4期16卷 301-324页

作者： Tuci, E Trianni, V Dorigo, M IRIDIA B-1050 Brussels Belgium

In this paper, we aim to design decision-making mechanisms for a simulated Khepera robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response. Contrary to other previous similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent controller, which in turn are tightly linked to the mechanisms for sensorimotor coordination. The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of 'feeling' time through the flow of sensations determined by its actions. Further analysis of the evolved solutions reveals the nature of the selective pressures that facilitate the evolution of fully discriminating and signalling agents. Moreover, we show that, by simply working on the nature of the fitness function, it is possible to bring forth discrimination mechanisms that generalize to conditions never encountered during evolution.

关键词： evolutionary robotics dynamic neural networks time-based decision making mechanisms robustness

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Evolution of Fuzzy Controllers for Robotic Vehicles: The Role of Fitness Function Selection

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2009年第4期56卷 469-484页

作者： Doitsidis, L. Tsourveloudis, N. C. Piperidis, S. Tech Univ Crete Intelligent Syst & Robot Lab Dept Prod Engn & Management Khania 73100 Greece

An important issue not addressed in the literature, is related to the selection of the fitness function parameters which are used in the evolution process of fuzzy logic controllers for mobile robot navigation. The majority of the fitness functions used for controllers evolution are empirically selected and (most of times) task specified. This results to controllers which heavily depend on fitness function selection. In this paper we compare three major different types of fitness functions and how they affect the navigation performance of a fuzzy logic controlled real robot. Genetic algorithms are employed to evolve the membership functions of these controllers. Further, an efficiency measure is introduced for the systematic analysis and benchmarking of overall performance. This measure takes into account important performance results of the robot during experimentation, such as the final distance from target, the time needed to reach its final position, the time of sensor activation, the mean linear velocity e.t.c. In order to examine the validity of our approach a low cost mobile robot has been developed, which is used as a testbed.

关键词： Mobile robots evolutionary robotics Fuzzy logic

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Place cognition and active perception: a study with evolved robots

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CONNECTION SCIENCE 2009年第1期21卷 3-14页

作者： Miglino, Orazio Ponticorvo, Michela Bartolomeo, Paolo Univ Naples Federico II Dept Relat Sci Naples Italy Hop La Pitie Salpetriere AP HP INSERM U610 Paris France

A study of place cognition and 'place units' in robots produced via artificial evolution is described. Previous studies have investigated the possible role of place cells as building blocks for 'cognitive maps' representing place, distance and direction. Studies also show, however, that when animals are restrained, the spatial selectivity of place cells is partially or completely lost. This suggests that the role of place cells in spatial cognition depends not only on the place cells themselves, but also on representations of the animal's physical interactions with its environment. This hypothesis is tested in a population of evolved robots. The results suggest that successful place cognition requires not only the ability to process spatial information, but also the ability to select the environmental stimuli to which the agent is exposed. If this is so, theories of active perception can make a useful contribution to explaining the role of place cells in spatial cognition.

关键词： place cognition evolved robots evolutionary robotics embodied cognition situated cognition active perception

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Generative representations for the automated design of modular physical robots

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IEEE TRANSACTIONS ON robotics AND AUTOMATION 2003年第4期19卷 703-719页

作者： Hornby, GS Lipson, H Pollack, JB Brandeis Univ Dept Comp Sci Ctr Complex Syst Waltham MA 02454 USA

The field of evolutionary robotics has demonstrated the ability to automatically design the morphology and controller of simple physical robots through synthetic evolutionary processes. However, it is not clear if variation-based search processes can attain the complexity of design necessary for practical engineering of robots. Here., we demonstrate an automatic design system that produces complex robots by exploiting the principles of regularity, modularity, hierarchy, and reuse. These techniques are already established principles of scaling in engineering design and have been observed in nature, but have not been broadly used in artificial evolution. We gain these advantages through the use of a generative representation, which combines a programmatic representation with an algorithmic process that compiles the representation into a detailed construction plan. This approach is shown to have two benefits: it can reuse components in regular and hierarchical ways, providing a systematic way to create more complex modules from simpler ones;and the evolved representations can capture intrinsic properties of the design space, so that variations in the representations move through the design space more effectively than equivalent-sized changes in a nongenerative representation. Using this system, we demonstrate for the first time the evolution and construction of modular, three-dimensional, physically locomoting robots, comprising many more components than previous work on body-brain evolution.

关键词： design automation evolutionary robotics generative representations

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