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Integration of Soft Computing Towards Autonomous Legged Robots

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Autonomous Robotic Systems

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 116))

Abstract

Mobile robots are extensively used in various terrains to handle situations inaccessible to man. Legged robots in particular are tasked to move in uneven terrain. Hence the primary problem of these robots is locomotion in an autonomous fashion. Autonomy is important as the tasks are plagued with many uncertainties. Some of these tasks include leg movement and coordination, navigation, localisation, and stability during movement, all operating in dynamic and unexplored territories.

Classical control and traditional programming methods provide stable and simple solutions in a known environment. The environment that legged robots work in is dynamic and unstructured, and such control methods are not always able to cope with. It is difficult to model the environment to provide the controller with the relevant data and program actions for all possible situations. Hence controllers with abilities to learn and to adapt are needed to solve this problem. Soft computing provides an attractive avenue to deal with these situations.

Soft computing methods are based on biological systems and they provide the following features: generalisation, adaptation and learning. As more is realised about the use and properties of soft computing methods, the development of controller is shifting towards using soft computing. They have properties that can be used to improve the stability, adaptability, and generalisation of the controllers. Some of the more popular methods used are fuzzy logic, artificial neural networks, reinforcement learning and genetic algorithms. They are commonly integrated with classical methods to enhance the features of classical controllers and vice versa. Each soft computing method serves a different purpose, with its advantages and disadvantages, and the methods are often used together to complement each other.

This chapter provides a survey on the different uses of soft computing methods in the different aspects of legged robotics. We see how soft computing methods and classical techniques compliment each other. Two areas of legged robotics are dealt with - control architecture and the problem of navigation. The Central Pattern Generator (CPG) controller and the behaviour-based controller are two architectures presented in this chapter. Various soft computing techniques are used to implement and improve these two controllers.

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Author information

Authors and Affiliations

  1. Department of Mechanical and Production Engineering, National University of Singapore, Singapore, 119260

    Anthony Wong & Marcelo H. Ang Jr.

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  1. Anthony Wong
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  2. Marcelo H. Ang Jr.
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Editors and Affiliations

  1. Singapore Polytechnic, School of Electrical and Electronic Engineering, 500 Dover Road, 139651, Singapore, Republic of Singapore

    Changjiu Zhou

  2. Department of Artificial Intelligence Faculty of Computer Science, Universidad Politécnica de Madrid, 28660 Boadilla del Monte, Madrid, Spain

    Darío Maravall

  3. Belgian Nuclear Research Centre (SCK·CEN), Boeretang 200, 2400, Mol, Belgium

    Da Ruan

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© 2003 Springer-Verlag Berlin Heidelberg

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Wong, A., Ang, M.H. (2003). Integration of Soft Computing Towards Autonomous Legged Robots. In: Zhou, C., Maravall, D., Ruan, D. (eds) Autonomous Robotic Systems. Studies in Fuzziness and Soft Computing, vol 116. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1767-6_12

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  • DOI: https://doi.org/10.1007/978-3-7908-1767-6_12

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2523-7

  • Online ISBN: 978-3-7908-1767-6

  • eBook Packages: Springer Book Archive

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