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Congress on evolutionary Computation

作者： Antin Phillips Mathys C. Du Plessis Nelson Mandela University Port Elizabeth South Africa

This research proposed augmenting evolutionary robotics controllers with their own robotic simulator. The motivation behind this idea is that the controller could use the simulator to approximate the effects of its actions. This would allow the controller to maintain an approximation of the robot’s location and orientation. It was hypothesised that this information could enhance a controller’s navigation ability. Both regular and augmented controllers were tested and compared on a task that required them to employ adaptive navigation strategies to efficiently solve the task. Augmented controllers were shown to perform better and possess superior behaviours in comparison to regular controllers.

关键词： evolutionary robotics Navigation Neural networks Robot sensing systems Sensors Task analysis Erbium

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evolutionary Modular robotics: Survey and Analysis

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2019年第3-4期95卷 815-828页

作者： Alattas, Reem J. Patel, Sarosh Sobh, Tarek M. Univ Bridgeport Sch Engn Interdisciplinary Robot Intelligent Sensing & Con 221 Univ Ave Bridgeport CT 06604 USA

This paper surveys various applications of artificial evolution in the field of modular robots. evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including;self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.

关键词： evolutionary robotics Modular robots Task-based design Self-assembly Self-reconfiguration Self-repair Self-reproduction

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evolutionary Developmental robotics: Improving Morphology and Control of Physical Robots

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ARTIFICIAL LIFE 2017年第2期23卷 169-185页

作者： Vujovic, Vuk Rosendo, Andre Brodbeck, Luzius Iida, Fumiya ETH Inst Robot & Intelligent Syst Zurich Switzerland Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England

evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated during the evolutionary process and autonomously built using additive fabrication. We present two cases with evo-devo experiments and one with evolution, and we hypothesize that the addition of a developmental stage can be used within robotics to improve performance. Moreover, our results show that a nonlinear system-environment interaction exists, which explains the nontrivial locomotion patterns observed. In the future, robots will be present in our daily lives, and this work introduces for the first time physical robots that evolve and grow while interacting with the environment.

关键词： evolutionary robotics developmental robotics evo-devo genetic algorithms ontogeny phylogenetic

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Unleashing the Potential of evolutionary Swarm robotics in the Real World

Unleashing the Potential of Evolutionary Swarm Robotics in t...

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Genetic and evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9781450343237

We provide a summary of our real-world experiments with a swarm of aquatic surface robots with evolved control. Robotic control was synthesized in simulation, using offline evolutionary robotics techniques, and then successfully transferred to a real swarm. Our study presents one of the first demonstrations of evolved control in a swarm robotics system outside of controlled laboratory conditions. Original publication: M. Duarte, V. Costa, J. Comes, T. Rodrigues, F. Silva, S. M. Oliveira, and A. L. Christensen. Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE, 11(3):e0151834, 2016.

关键词： reality gap evolutionary robotics swarm robotics aquatic robots

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Assessing the Effect of Self-Assembly Ports in evolutionary Swarm robotics

Assessing the Effect of Self-Assembly Ports in Evolutionary ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Ripon, Kazi Shah Nawaz Jakobsen, Eirik Tannum, Christopher Montanier, Jean-Marc Norwegian Univ Sci & Technol Dept Comp & Informat Sci Trondheim Norway Protolab Softbank Robot Europe Paris France

ISBN: (纸本)9781509042401

Self-assembly in swarm robotics is essential for a group of robots in achieving a common goal that is not possible to achieve by a single robot. Self-assembly also provides several advantages to swarm robotics. Some of these include versatility, scalability, re-configurability, cost-effectiveness, extended reliability, and capability for emergent phenomena. This work investigates the effect of self-assembly in evolutionary swarm robotics. Because of the lack of research literature within this paradigm, there are few comparisons of the different implementations of self-assembly mechanisms. This paper reports the influence of connection port configuration on evolutionary self-assembling swarm robots. The port configuration consists of the number and the relative positioning of the connection ports on each of the robot. Experimental results suggest that configuration of the connection ports can significantly impact the emergence of self-assembly in evolutionary swarm robotics.

关键词： evolutionary robotics self-assembly machine learning connection port configuration docking mechanism

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Assessing the Effect of Self-Assembly Ports in evolutionary Swarm robotics

Assessing the Effect of Self-Assembly Ports in Evolutionary ...

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IEEE Symposium Series on Computational Intelligence

作者： Kazi Shah Nawaz Ripon Eirik Jakobsen Christopher Tannum Jean-Marc Montanier Department of Computer and Information Science Norwegian University of Science and Technology Norway protolab - Softbank Robotics Europe

ISBN: (纸本)9781509042418

关键词： evolutionary robotics self-assembly machine learning connection port configuration docking mechanism

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Emergence of flocking behaviors transferring previously evolved alignment robot controllers

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EVOLVING SYSTEMS 2025年第1期16卷 1-12页

作者： Sendra-Arranz, Rafael Gutierrez, Alvaro Univ Politecn Madrid ETS Ingn Telecomunicac Madrid 28040 Spain

Flocking is a crucial collective behavior in swarm robotics. Reynolds introduced the boids model as a means to imitate flocking behaviors in artificial agents. This model relies on three fundamental local rules: separation, cohesion, and alignment. This paper examines the development of flocking behaviors only through the evolution of the alignment rule. Initially, we employ a genetic algorithm to develop the alignment behavior inside a group of stationary robots. The advanced alignment robot controller is a continuous-time recurrent neural network (CTRNN). Afterwards, we include the developed controller into a three-layered subsumption architecture in order to accomplish flocking behavior. Aside from the advanced alignment behavior, the architecture also incorporates a rudimentary manually designed obstacle avoidance behavior and a subroutine for moving forward. The initial experiment centers on the progression of alignment among the robots. Advanced communication techniques result in a scalable and precise alignment, where both the message content and its related context are very pertinent. The second experiment investigates the development of flocking behavior. The results indicate that the suggested subsumption architecture is capable of achieving efficient flocking behaviors. In addition, the robot swarm has the ability to navigate around barriers and continue to exhibit flocking behavior once the impediments have been bypassed. Our research indicates that the formation of a cohesive group can occur by implementing a single developed rule, complemented with well designed actions for avoiding obstacles and navigating the environment.

关键词： Swarm robotics evolutionary robotics Flocking Emergence of communication Orientation consensus Alignment

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Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

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APPLIED INTELLIGENCE 2021年第9期51卷 6467-6496页

作者： Husbands, Phil Shim, Yoonsik Garvie, Michael Dewar, Alex Domcsek, Norbert Graham, Paul Knight, James Nowotny, Thomas Philippides, Andrew Univ Sussex Dept Informat Ctr Computat Neurosci & Robot Brighton E Sussex England Pai Chai Univ Dept Game & Multimedia Engn Daejeon South Korea Univ Sussex Dept Informat CCNR Brighton E Sussex England Univ Sussex Sch Life Sci CCNR Brighton E Sussex England

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morpho

关键词： evolutionary robotics Biorobotics Visual navigation Neural dynamics Chaotic dynamics Evolvable hardware

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Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F-Race

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2025年第1期7卷

作者： Ramos, David Garzon Pagnozzi, Federico Stutzle, Thomas Birattari, Mauro Univ Libre Bruxelles ULB IRIDIA Brussels Belgium

Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single-objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi-criteria case. We conduct experiments with a swarm of e-puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or l2$l<^>{2} $-norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F-race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated. The study explores a novel approach to the automatic design of robot swarms using a bi-criteria optimization method called Mandarina. It compares Mandarina with other methods, focusing on performance across sequential tasks. The results highlight Mandarina's effectiveness in generating robot behavior that balances multiple design criteria without needing human intervention, showcasing its potential for complex *** (c) 2024 WILEY-VCH GmbH

关键词： automatic design bi-criteria design evolutionary robotics Iterated F-race swarm robotics

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High resilience in robotics with a multi-objective evolutionary algorithm

High resilience in robotics with a multi-objective evolution...

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15th Annual Conference on Genetic and evolutionary Computation, GECCO 2013

作者： Koos, Sylvain Cully, Antoine Mouret, Jean-Baptiste ISIR CNRS UMR Université Pierre et Marie Curie-Paris 6 4 place Jussieu F-75252 Paris Cedex 05 France

No abstract available.

ISBN: (纸本)9781450319645

No abstract available.

关键词： legged robot recovery resilience evolutionary robotics

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