We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that ar...
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We give an overview of evolutionary robotics research at Sussex over the last five years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots, simulated robots, co-evolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.
evolutionary robotics (ER) has emerged as a fast growing field in the last two decades and has earned the attention of a number of researchers. Principles of biological evolution are applied in the form of evolutionar...
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evolutionary robotics (ER) has emerged as a fast growing field in the last two decades and has earned the attention of a number of researchers. Principles of biological evolution are applied in the form of evolutionary techniques for solving the complicated problems in the areas of robotic design and control. The diversity and the intensity of this growing field is presented in this paper through the contributions made by several researchers in the categories of robot controller design, robot body design, co-evolution of body and brain and in transforming the evolved robots in physical reality. The paper discusses some of the recent achievements in each of these fields along with some expected applications which are likely to motivate the future research. For the quick reference of the readers, a digest of all the works is presented in the paper, spanning the years and the areas of the research contributions.
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 p...
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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
We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER ...
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We survey developments in artificial neural networks, in behavior-based robotics, and in evolutionary algorithms that set the stage for evolutionary robotics (ER) in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments, which is an essential aspect of real cognition that is often either bypassed or modeled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion, the origins of learning, and the ontogenetic acquisition of entrainment.
An evolutionary robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated t...
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An evolutionary robotics (ER) approach to the task of odor source localization is investigated. In particular, Continuous Time Recurrent Neural Networks (CTRNNs) are evolved for odor source localization in simulated turbulent odor plumes. In the experiments, the simulated robot is equipped with a single chemical sensor and a wind direction sensor. Three main contributions are made. First, it is shown that the ER approach can be successfully applied to odor source localization in both low-turbulent and high-turbulent conditions. Second, it is demonstrated that a small neural network is able to successfully perform all three sub-tasks of odor source localization: (i) finding the odor plume, (ii) moving toward the odor source, and (iii) identifying the odor source. Third, the analysis of the evolved behaviors reveals two novel odor source localization strategies. These strategies are successfully re-implemented as finite state machines, validating the insights from the analysis of the neural controllers. (C) 2013 Elsevier B.V. All rights reserved.
In evolutionary robotics, a suitable robot control system is developed automatically through evolution due to the interactions between the robot and its environment. It is a complicated task, as the robot and the envi...
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In evolutionary robotics, a suitable robot control system is developed automatically through evolution due to the interactions between the robot and its environment. It is a complicated task, as the robot and the environment constitute a highly dynamical system. Several methods have been tried by various investigators to solve this problem. This paper provides a survey on some of these important studies carried out in the recent past.
Taking inspiration from the navigation ability of humans, this study investigated a method of providing robotic controllers with a basic sense of position. It incorporated robotic simulators into robotic controllers t...
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Taking inspiration from the navigation ability of humans, this study investigated a method of providing robotic controllers with a basic sense of position. It incorporated robotic simulators into robotic controllers to provide them with a mechanism to approximate the effects their actions had on the robot. Controllers with and without internal simulators were tested and compared. The proposed controller architecture was shown to outperform the regular controller architecture. However, the longer an internal simulator was executed, the more inaccurate it became. Thus, the performance of controllers with internal simulators reduced over time unless their internal simulator was periodically corrected.
This paper utilizes evolutionary robotics techniques as a hypothesis generator to explore optical variables and control strategies that could be used to solve a driving-like braking task. Given such a task, humans exh...
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This paper utilizes evolutionary robotics techniques as a hypothesis generator to explore optical variables and control strategies that could be used to solve a driving-like braking task. Given such a task, humans exhibit two different braking behaviors: continuously regulated braking and impulsive braking. Based on an oft-used experimental task in human perception/action research, a series of evolutionary robotics simulations were developed to explore the space of possible braking strategies by examining how braking strategies change as the optical information is manipulated. Our results can be summarized as follows: (1) behaviors similar to human behavior were observed only when the constraints were selected correctly;(2) the optical variables and proportional rate yielded significantly better braking performance;(3) two different classes of impulsive braking behaviors were observed, including one not reported in previous studies: discrete impulsive braking and oscillatory impulsive braking;(4) the optical variable is used to initiate and terminate braking;(5) the evolved model agents use a proportional rate control strategy to regulate braking continuously. We argue that combining psychological experiments and evolutionary robotics simulations is a promising research methodology that is useful for testing existing hypotheses and generating new ones.
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