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Behavioral Path Planning

Behavioral Path Planning

Copyright: © 2013 |Pages: 28
ISBN13: 9781466620742|ISBN10: 1466620749|EISBN13: 9781466620759
DOI: 10.4018/978-1-4666-2074-2.ch006
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MLA

Ritu Tiwari, et al. "Behavioral Path Planning." Intelligent Planning for Mobile Robotics: Algorithmic Approaches, IGI Global, 2013, pp.126-153. https://doi.org/10.4018/978-1-4666-2074-2.ch006

APA

R. Tiwari, A. Shukla, & R. Kala (2013). Behavioral Path Planning. IGI Global. https://doi.org/10.4018/978-1-4666-2074-2.ch006

Chicago

Ritu Tiwari, Anupam Shukla, and Rahul Kala. "Behavioral Path Planning." In Intelligent Planning for Mobile Robotics: Algorithmic Approaches. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2074-2.ch006

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Abstract

A large part of our everyday motion is governed by behaviors. We necessarily do not look at the entire map and formulate the best way out, but rather take instinctive actions regarding our motion. We naturally reach the desired locations fairly easily and near optimally. With the same inspirations in mind, in this chapter, the authors explore the behavioral systems for the task of motion of the mobile robot. In this chapter, they study two different algorithms, fuzzy inference systems and artificial neural networks. The fuzzy systems are governed by a set of rules, which determine the behavior of the system, for any applied input. The major task involves the use of fuzzy sets for the output computations. As per the theory of these sets, every input belongs to every set by a varying degree called as the membership degree. The authors use this concept of fuzzy-based inference to design a system for the motion of the mobile robot. They further introduce the neural networks paradigm, which is an inspiration from the human brain for problem solving. Neural networks process applied input layer wise by unit processing centers known as artificial neurons. These systems may be trained by a training database that is particular to a problem. The authors use both these algorithms to design systems for behavioral path planning of mobile robots.

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