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13th International Conference on Computational Collective Intelligence (ICCCI)

作者： Shaghaghi, Sahand Hamid, Owais Nehaniv, Chrystopher L. Univ Waterloo Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9783030881139;9783030881122

Evolution has been a topic of interest that has been explored extensively throughout recent history. Ever since the proposal of the evolutionary theory by Darwin, there have been attempts made to validate, extend, explore and exploit the different aspects of this theory. This study sets forth methods to explore elements of what has been proposed by Darwinian evolution using Braitenberg vehicles, simple machines with sensory-motor couplings in an environmental context. In this study, a simulation environment is set up based upon the descriptions of neurophysiologist Valentino Braitenberg. This simulation environment is then utilized to carry out the thought experiments envisioned by Braitenberg for the simplest nontrivial kinds of vehicles. The methodology and results for the carried out experiments are detailed in this paper. Apart from an understanding of whether the environmental setup affects evolution, specifically the number of light sources (stimuli) and location of these light sources, the experiments show an interesting trend regarding dynamic equilibrium of the evolutionary process, the ramifications of which might not have been understood well enough previously. It is concluded that ecological setup, as well as the initial genetic makeup of the vehicles, play a crucial role in the evolution of vehicles in scenarios laid out in this study. Further, the placement of stimuli (location of lights) and the number of the stimuli have a visible effect on the survivability of vehicle types (species).

关键词： Braitenberg vehicles Multi-agent robotics evolutionary robotics

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The Virtual Programmable Logic Device, a Novel Machine Learning Architecture

The Virtual Programmable Logic Device, a Novel Machine Learn...

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Congress on evolutionary Computation

作者： Fraser Borrett Mark Beckerleg Department of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel architecture for robotic control, called a Virtual Programmable Logic Device (VPLD) where the operation of the device can be dynamically configured using machine learning methods. The VPLD is based on the structure of a Programable Logic Device (PLD) comprising of a two-dimensional feed-forward array of function blocks, with each block containing multiplexers and function elements. However, the VPLD is implemented in software rather than hardware allowing the VPLD to be run on a CPU based system, such as PC's and ARM based embedded systems. The operation of the VPLD is determined by a configuration bitstream which configures the multiplexers (routing) and function elements of each block. A genetic algorithm is used to evolve the configuration bitstream to produce the walking gait of a hexapod robot. The controller performance and the evolutionary efficiency of the VPLD are compared with an evolved artificial neural network (ANN), and an evolvable hardware (EHW) device. It was found the VPLD and ANN had similar controller performance and evolutionary efficiency, while the EHW had a comparable controller performance however its evolutionary efficiency was poor. It is shown that the VPLD is a viable alternative to an ANN for evolutionary robotic control.

关键词： Performance evaluation Multiplexing Legged locomotion evolutionary robotics Programmable logic devices Artificial neural networks Machine learning

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Automating Robot Design with Multi-Level Evolution

Automating Robot Design with Multi-Level Evolution

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Congress on evolutionary Computation

作者： Geoff Nitschke David Howard Bilal Aslan Department of Computer Science University of Cape Town South Africa Data61 CSIRO Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In evolutionary robotics, Multi-Level Evolution (MLE) has been demonstrated for effective robot designs using a bottom-up approach, first evolving which materials to use for modular components and then how these components are connected into a functional robot design. This paper evaluates MLE robotic design, as an evolutionary design method on various task (robot ambulation) environments in comparison to human designed robots (pre-designed robot controller-morphology couplings). Results indicate that the MLE method evolves robots that are effective across increasingly difficult (locomotion) task environments, out-performing pre-designed robots, and thus provide further support for the efficacy of MLE as an evolutionary robotic design method. Furthermore, results indicate the MLE method enables the evolution of suitable robotic designs for various environments, where such designs would be non-intuitive and unlikely in conventional robotic design.

关键词： evolutionary robotics Couplings Maximum likelihood estimation Design methodology Morphology Robustness Complexity theory

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Automatic synthesizing multi-robot cooperation strategies based on Brain Storm robotics

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： Yang, Jian Shi, Yuhui Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

With the increasing task complexity and environmental uncertainty, it is hard to achieve adaptability and robustness by manual design methods for multi-robot cooperation tasks. Automatic synthesis approaches with trial and error mechanisms are getting more and more attention. By encoding the strategies to be designed as "ideas'', the newly proposed Brain Storm robotics (BSR) framework can obtain the sufficiently good solutions for particular tasks after a series of operations on the ideas. However, the original BSR only shows designing the rule base for a fuzzy controller. This paper proposes an automatic design approach for neural network-based strategies for robotic swarms with the BSR framework to realize cooperative behaviors. Two design cases are studied: one is the direct strategy search for a swarm aggregation behavior;the other is synthesizing a backpropagation neural network-based controller for coordinated formation control, which has both evolution and learning characteristics. The results show that the proposed method can automatically find control strategies with scalability for multi-robot cooperation, which has the potential for further development. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Brain Storm robotics Brain Storm Optimization Multi-robot systems Swarm robotics evolutionary robotics

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AutoMoDe-Mate: Automatic off-line design of spatially-organizing behaviors for robot swarms

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SWARM AND evolutionary COMPUTATION 2022年 74卷

作者： Mendiburu, Fernando J. Ramos, David Garzon Morais, Marcos R. A. Lima, Antonio M. N. Birattaria, Mauro Univ Libre Bruxelles IRIDIA Ave Franklin Roosevelt 50 B-1050 Brussels Belgium Univ Fed Campina Grande R Aprigio Veloso 882 BR-58428830 Campina Grande PB Brazil

We present Mate , an automatic off-line design method specialized in the design of spatially-organizing behav-iors for robot swarms. Mate belongs to the family of modular methods known as AutoMoDe. We introduce Mate to study the automatic design of collective behaviors for missions in which the swarm is subject to spa-tial distribution constrains. In this paper, we produce control software for three missions with specifications related to the distribution of the swarm in the environment. We conduct experiments in simulation and with a swarm of 20 e-puck robots. Alongside Mate , we also conduct experiments with two other automatic design methods: Chocolate -a state-of-the-art instance of AutoMoDe;and EvoSpace -a method based on neuro-evolution. Early studies conducted with existing modular design methods have shown their limitations in the design of spatially-organizing behaviors for robots that operate under spatial constrains. By introducing a spe-cialized method like Mate , we expect to overcome these limitations. The aggregate results of our experiments show that Mate performs significantly better than Chocolate and EvoSpace in the missions we consider.

关键词： Swarm robotics Automatic design Spatially-organizing behaviors Relative positioning AutoMoDe evolutionary robotics

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Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms

Ensemble Feature Extraction for Multi-Container Quality-Dive...

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Cazenille, Leo Ochanomizu Univ Tokyo Japan

ISBN: (纸本)9781450383509

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.

关键词： Quality-Diversity algorithms AURORA evolutionary robotics Ensemble representation learning Auto-encoders Deep Learning

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Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

Rapidly adapting robot swarms with Swarm Map-based Bayesian ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Bossens, David M. Tarapore, Danesh Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

ISBN: (纸本)9781728190778

Rapid performance recovery from un-foreseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion;and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm;for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.

关键词： Swarm robotics evolutionary robotics Bayesian optimisation

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Vision-Based Autonomous Navigation with evolutionary Learning 19th

Vision-Based Autonomous Navigation with Evolutionary Learnin...

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19th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Moya-Albor, Ernesto Ponce, Hiram Brieva, Jorge Coronel, Sandra L. Chavez-Domingue, Rodrigo Univ Panamer Fac Ingn Augusto Rodin 498 Ciudad De Mexico 03920 Mexico Inst Politecn Nacl UPIITA Dept Ingn Av IPN 2580 Col La Laguna Ticoman Ciudad De Mexico 07340 Mexico

ISBN: (纸本)9783030608873;9783030608866

In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions were tuned applying a distributed evolutionary learning based on social wound treatment inspired in the Megaponera analis ant. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The results show that the optimization of the input fuzzy membership functions improves the navigation behavior against an empirical tuning of them.

关键词： Optical flow Hermite transform V-rep evolutionary robotics Metaheuristic optimization Vision-based control navigation

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Evolving Gaits for Damage Control in a Hexapod Robot

Evolving Gaits for Damage Control in a Hexapod Robot

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Mailer, Christopher Nitschke, Geoff Raw, Leanne Univ Cape Town Cape Town South Africa

ISBN: (纸本)9781450383509

Autonomous robots are increasingly used in remote and hazardous environments, where damage to sensory-actuator systems cannot be easily repaired. Such robots must therefore have controllers that continue to function effectively given unexpected malfunctions and damage to robot morphology. This study applies the Intelligent Trial and Error (IT&E) algorithm to adapt hexapod robot control to various leg failures and demonstrates the IT&E map-size parameter as a critical parameter in influencing IT&E adaptive task performance. We evaluate robot adaptation for multiple leg failures on two different map-sizes in simulation and validate evolved controllers on a physical hexapod robot. Results demonstrate a trade-off between adapted gait speed and adaptation duration, dependent on adaptation task complexity (leg damage incurred), where map-size is crucial for generating behavioural diversity required for adaptation.

关键词： evolutionary robotics Quality-Diversity Algorithms Bayesian optimisation Damage recovery Behavioural diversity

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Biodiversity in Evolved Voxel-based Soft Robots

Biodiversity in Evolved Voxel-based Soft Robots

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Medvet, Eric Bartoli, Alberto Pigozzi, Federico Rochelli, Marco Univ Trieste DIA Trieste Italy

ISBN: (纸本)9781450383509

In many natural environments, there are different forms of living creatures that successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce biodiversity in non-biological agents, while still optimizing them for a particular task, might increase their applicability to scenarios where human response to unexpected changes is not possible. In this work, we focus on Voxel-based Soft Robots (VSRs), a form of robots that grants great freedom in the design of both body and controller and is hence promising in terms of biodiversity. We use evolutionary computation for optimizing, at the same time, body and controller of VSRs for the task of locomotion. We investigate experimentally whether two key factors-evolutionary algorithm (EA) and representation-impact the emergence of biodiversity and if this occurs at the expense of effectiveness. We devise a way for measuring biodiversity, systematically characterizing the robots shape and behavior, and apply it to the VSRs evolved with three EAs and two representations. The experimental results suggest that the representation matters more than the EA and that there is not a clear trade-off between diversity and effectiveness.

关键词： evolutionary robotics Modularity Neuroevolution Representation Diversity

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