Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion

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Author(s): Jason Teo (Universiti Malaysia Sabah, Malaysia), Lynnie D. Neri (Universiti Malaysia Sabah, Malaysia), Minh H. Nguyen (University of New South Wales, Australia)and Hussein A. Abbass (University of New South Wales, Australia)
Copyright: 2008
Pages: 33
Source title: Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Source Author(s)/Editor(s): Lam Thu Bui (University of New South Wales, Australia)and Sameer Alam (University of New South Wales, Australia)
DOI: 10.4018/978-1-59904-498-9.ch011

Keywords: Artificial Intelligence / Computational Intelligence / Computer Science & IT / Information Science Reference

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Abstract

This chapter will demonstrate the various robotics applications that can be achieved using evolutionary multi-objective optimization (EMO) techniques. The main objective of this chapter is to demonstrate practical ways of generating simple legged locomotion for simulated robots with two, four and six legs using EMO. The operational performance as well as complexities of the resulting evolved Pareto solutions that act as controllers for these robots will then be analyzed. Additionally, the operational dynamics of these evolved Pareto controllers in noisy and uncertain environments, limb dynamics and effects of using a different underlying EMO algorithm will also be discussed.

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Walking with EMO: Multi-Objective Robotics for Evolving Two, Four, and Six-Legged Locomotion

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