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- W2247444669 abstract "Traditional simulators can be complex, time-consuming and require specialized knowledge to develop while still being unable to adequately model reality. Artificial Neural Networks (ANNs) can be trained to simulate real-world robots and therefore serve as an alternative to traditional approaches of robot simulation during the Evolutionary Robotics (ER) process. ANN-based simulators require little specialized knowledge and can automatically incorporate many real-world peculiarities. This paper reports a simulator that consisted of ANNs which were trained to predict changes in the position of a real-world snakelike robot. Navigational behaviours were evolved in simulation and subsequently verified on the real-world robot. This paper demonstrated that ANNs are a viable alternative to traditional simulators for evolving controllers for snake-like robots." @default.
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- W2247444669 date "2015-12-01" @default.
- W2247444669 modified "2023-10-01" @default.
- W2247444669 title "Evolving Snake Robot Controllers Using Artificial Neural Networks as an Alternative to a Physics-Based Simulator" @default.
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- W2247444669 doi "https://doi.org/10.1109/ssci.2015.47" @default.
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