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Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness

Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness

Chin Kim On, Jason Teo, Azali Saudi
Copyright: © 2010 |Pages: 25
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
DOI: 10.4018/978-1-60566-766-9.ch028
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MLA

On, Chin Kim, et al. "Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness." Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., IGI Global, 2010, pp. 574-598. https://doi.org/10.4018/978-1-60566-766-9.ch028

APA

On, C. K., Teo, J., & Saudi, A. (2010). Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness. In E. Olivas, J. Guerrero, M. Martinez-Sober, J. Magdalena-Benedito, & A. Serrano López (Eds.), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 574-598). IGI Global. https://doi.org/10.4018/978-1-60566-766-9.ch028

Chicago

On, Chin Kim, Jason Teo, and Azali Saudi. "Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness." In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., 574-598. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-766-9.ch028

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Abstract

The utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. It explains the comparison performances among the elitism without archive and elitism with archive used in the evolutionary multi-objective optimization (EMO) algorithm in an evolutionary robotics study. Furthermore, the controllers’ moving performances, tracking ability and robustness also have been demonstrated and tested with four different levels of environments. The experimentation results showed the controllers allowed the robots to navigate successfully, hence demonstrating the EMO algorithm can be practically used to automatically generate controllers for phototaxis and RF-localization behaviors, respectively. Understanding the underlying assumptions and theoretical constructs through the utilization of EMO will allow the robotics researchers to better design autonomous robot controllers that require minimal levels of human-designed elements.

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