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evolutionary robotics Applied to Hexapod Locomotion: a Comparative Study of Simulation Techniques

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2019年第3-4期96卷 363-385页

作者： Pretorius, Christiaan J. du Plessis, Mathys C. Gonsalves, John W. Nelson Mandela Univ Dept Math & Appl Math Port Elizabeth South Africa Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

The evolutionary robotics (ER) process has been applied extensively to developing control programs to achieve locomotion in legged robots, as an automated alternative to the arduous task of manually creating control programs for such robots. The evolution of such controllers is typically performed in simulation by making use of a physics engine-based robotic simulator. Making use of such physics-based simulators does, however, have certain challenges associated with it, such as these simulators' computational inefficiency, potential issues with lack of accuracy and the human effort required to construct such simulators. The current study therefore proposed and investigated an alternative method of simulation for a hexapod (six-legged) robot in the ER process, and directly compared this newly-proposed simulation method to traditional physics-based simulation. This alternative robotic simulator was built based solely on experimental data acquired directly from observing the behaviour of the robot. This data was used to construct a simulator for the robot based on Artificial Neural Networks (ANNs). To compare this novel simulation method to traditional physics simulation, the ANN-based simulators were used to evolve simple open-loop locomotion controllers for the robot in simulation. The real-world performance of these controllers was compared to that of controllers evolved in a more traditional physics-based simulator. The obtained results indicated that the use of ANN-based simulators produced controllers which could successfully perform the required locomotion task on the real-world robot. In addition, the controllers evolved using the ANN-based simulators allowed the real-world robot to move further than those evolved in the physics-based simulator and the ANN-based simulators were vastly more computationally efficient than the physics-based simulator. This study thus decisively indicated that ANN-based simulators offer a superior alternative to widely-used physics

关键词： Hexapod Deep Learning evolutionary robotics Artificial Neural Networks Locomotion System identification

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Adaptive Navigation Strategies in evolutionary robotics Controllers with Location Perception

Adaptive Navigation Strategies in Evolutionary Robotics Cont...

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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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General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology

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SCIENTOMETRICS 2018年第2期117卷 1265-1283页

作者： Coccia, Mario CNR Natl Res Council Italy Rome Italy Arizona State Univ Tempe AZ 85287 USA IRCRES CNR Coll Carlo Alberto Via Real Coll 30 I-10024 Turin Italy

How do research fields evolve? This study confronts this question here by developing an inductive analysis based on emerging research fields of human microbiome, evolutionary robotics and astrobiology (also called exobiology). Data analysis considers papers associated with subject areas of authors from starting years to 2017 per each research field under study. Findings suggest some empirical properties of the evolution of research fields: the first property states that the evolution of a research field is driven by few disciplines (3-5) that generate more than 80% of documents (concentration of scientific production);the second property states that the evolution of research fields is path-dependent of critical disciplines: they can be parent disciplines that have originated the research field or new disciplines emerged during the evolution of science;the third property states that the evolution of research fields can be also due to a new discipline originated from a process of specialization within applied or basic sciences and/or convergence between disciplines. Finally, the fourth property states that the evolution of specific research fields can be due to both applied and basic sciences. These results here can explain and generalize some characteristics of the evolution ofscientific fields in the dynamics of science. Overall, then, this study begins the process of clarifying and generalizing, as far as possible, the generalproperties of the evolution of research fields to lay a foundation for the development of sophisticated theories of the evolution of science.

关键词： Research fields Evolution of science Dynamics of science Convergence in science Applied sciences Basic sciences Human microbiome evolutionary robotics Astrobiology Exobiology A19 C00 I23 L30

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Robotic Snake Simulation using Ensembles of Artificial Neural Networks in evolutionary robotics 18

Robotic Snake Simulation using Ensembles of Artificial Neura...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Woodford, Grant W. du Plessis, Mathys C. Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

ISBN: (纸本)9781450356183

The evolutionary robotics process requires the evaluation of large numbers of robot controllers in order to determine their relative fitnesses. The evaluation of many controllers is typically performed in simulation instead of real-world hardware in order to speed up the evolutionary process and avoid damage to robot hardware. Physics-based simulators are traditionally used in robotics for evaluating controllers. Effective traditional simulators may require a high level of accuracy and their creation requires specialised knowledge of the dynamics of the robotic system. Alternatively, Artificial Neural Network based simulators are relatively simple to construct, are highly accurate, efficient and assume little specialised knowledge of the dynamics involved. This paper proposes a novel controller evolution method that integrates uncertainty information from an ensemble of Artificial Neural Network based simulators. By estimating the accuracy of simulator predictions during the controller evolution process, robot behaviours that are not accurately simulated are likely to be avoided. A performance comparison of a single versus ensembles configuration of Artificial Neural Network based simulators is investigated. The ensemble approach developed in thiswork has so far outperformed all prior controller evolution methods investigated for the presented problem.

关键词： ACM proceedings evolutionary robotics Simulation Artificial Neural Networks

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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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Concurrent controller and Simulator Neural Network development for a snake-like robot in evolutionary robotics

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robotics AND AUTONOMOUS SYSTEMS 2017年 88卷 37-50页

作者： Woodford, Grant W. du Plessis, Mathys C. Pretorius, Christiaan J. NMMU Dept Comp Sci Port Elizabeth South Africa NMMU Dept Math & Appl Math Port Elizabeth South Africa

evolutionary robotics (ER) is a field of study that has shown much promise in automating the development of robotic controllers and morphologies. The use of simulators as an alternative to real-world robots is often employed to reduce the time required to develop effective controllers in the ER process. However, the development of adequate simulators is often time-consuming and complex. Simulators are usually constructed from physics models and/or are based on empirically collected data. The vast majority of simulation approaches are based on physics models which can become complex and require specialised knowledge. Alternative simulation approaches that simplify and automate the modelling of real-world phenomena can provide certain advantages over traditional approaches. An alternative simulation approach, such as Artificial Neural Networks (ANNs) that model the real-world phenomena based on empirical data are relatively simple to construct and requires little specialised knowledge. ANN-based simulators are traditionally constructed before the ER process can begin and require the sampling of real-world experimental data. Disadvantages to the traditional approach to ANN-based simulator construction are that the simulator must be created before the ER process can be initiated and a large amount of behavioural data must be collected in order to accurately predict future behaviours. Previous research has successfully demonstrated that ANN-based simulators and controllers can be developed concurrently during the ER process for a simple differentially-steered wheeled robot, while the current research has demonstrated the concurrent approach using a complex snake-like robot. The viability of the concurrent approach was demonstrated on a real-world snake-like robot by performing trajectory planning tasks. Influential factors related to the success of the concurrent approach were also studied by investigating the effects various parameter settings had on success. (C) 2016 E

关键词： evolutionary robotics Coevolution Simulator Artificial Neural Networks Snake robot

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Abstraction, Sensory-Motor Coordination, and the Reality Gap in evolutionary robotics

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ARTIFICIAL LIFE 2017年第2期23卷 124-141页

作者： Scheper, Kirk Y. W. de Croon, Guido C. H. E. Delft Inst Technol Delft Netherlands Delft Univ Technol Fac Aerosp Engn NL-2629 HS Delft Netherlands

One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested.

关键词： Sensory-motor control evolutionary robotics reality gap micro air vehicle

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On Self-Adaptive Rate Restarts for evolutionary robotics with Real Rotorcraft 17

On Self-Adaptive Rate Restarts for Evolutionary Robotics wit...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Howard, Gerard David CSIRO 1 Technol Court Brisbane Qld 4068 Australia

ISBN: (纸本)9781450349208

Self-adaptive parameters are increasingly used in the field of evolutionary robotics, as they allow key evolutionary rates to vary autonomously in a context-sensitive manner throughout the optimisation process. A significant limitation to self-adaptive mutation is that rates can be set unfavourably, which hinders convergence. Rate restarts are typically employed to remedy this, but thus far have only been applied in evolutionary robotics for mutation-only algorithms. This paper focuses on the level at which evolutionary rate restarts are applied in population-based algorithms with >1 evolutionary operator. After testing on a real hexacopter hovering task, we conclude that individual-level restarting results in higher fitness solutions without fitness stagnation, and population restarts provide a more stable rate evolution. Without restarts, experiments can become stuck in suboptimal controller/rate combinations which can be difficult to escape from.

关键词： evolutionary robotics self-adaptation evolvable hardware differential evolution

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A Baseline-Realistic Objective Open-Ended Kinematics Simulator for evolutionary robotics 17

A Baseline-Realistic Objective Open-Ended Kinematics Simulat...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Konsella, Riley Chiarulli, Frank Peterson, John Rieffel, John Union Coll 807 Union St Schenectady NY 12308 USA

ISBN: (纸本)9781450349390

Most modern applications of evolutionary robotics (ER) rely upon computer-based physics simulations in order to model the behavior of the systems in question. One of the greatest challenges in the field of ER, therefore, is the development of robust, high-precision and accurate physics simulators that can model all necessary and relevant real-world interactions in an computationally efficient manner. Up until now, most popular ER simulators are nonetheless deficient in one or many of these properties. Here we introduce a new competitive simulator, the Baseline-Realistic Objective Open-Ended Kinematics Simulator (BROOKS) that outperforms other off-the-shelf simulators in most criteria. Our simulator is free, open-sourced, and easy to modify. It can model a wide range of robotic platforms, substrates and environments. Moreover, we claim solutions produced within the BROOKS simulator perform almost identically in the real-world, thereby helping to address one of the most challenging aspects of simulation in evolutionary robotics: the Reality Gap. Ultimately, we believe that BROOKS will establish a new baseline against which all other simulators should be compared.

关键词： evolutionary robotics Simulation Reality

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evolutionary robotics Applied to the Multi-Robot Worst-Case Pursuit-Evasion Problem 7

Evolutionary Robotics Applied to the Multi-Robot Worst-Case ...

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7th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (IEEE UEMCON)

作者： Gregorin, Livia Freire, Eduardo Carvalho, Elyson Molina, Lucas Givigi, Sidney Univ Fed Sergipe Dept Elect Engn Aracaju Brazil Royal Mil Coll Canada Dept Elect & Comp Engn Kingston ON Canada

ISBN: (纸本)9781509014965

This paper proposes the use of an evolutionary robotics approach to solve the worst-case pursuit-evasion problem, in which evaders are considered arbitrarily fast and omniscient, while pursuers have limited sensing and communication capabilities, with no previous knowledge of the environment. Unlike most work in evolutionary robotics, we offer a control system for multiple mobile robots based on finite state machines derived using a genetic algorithm. Results show that, given a sufficient number of robots, the evolved system is capable of clearing a discrete map, including multiply connected maps, of all previous contamination.

关键词： evolutionary robotics finite state machines pursuit-evasion

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