Behaviour Based Navigational Control of Humanoid Robot Using Genetic Algorithm Technique in Cluttered Environment

Behaviour Based Navigational Control of Humanoid Robot Using Genetic Algorithm Technique in Cluttered Environment

Asita K. Rath Dayal R. Parhi  Harish C. Das  Priyadarshi B. Kumar 

Centre of Biomechanical Science, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha 751030, India

Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela, Odisha 769008, India

Mechanical Engineering Department, National Institute of Technology, Shillong, Meghalaya 793003, India

Corresponding Author Email: 
asitr06@gmail.com
Page: 
32-36
|
DOI: 
https://doi.org/10.18280/mmc_a.910105
Received: 
23 January 2018
| |
Accepted: 
17 April 2018
| | Citation

OPEN ACCESS

Abstract: 

Humanoids are popular than their wheeled counterparts by the virtue of their ability to mimic the human behaviour and replace human efforts if required. Navigation and path planning is a complex and challenging problem for humanoids as it involves careful consideration of the navigational parameters. This paper introduces the path planning of a humanoid robot utilizing genetic hereditary calculation. The objective of the paper is to design a navigational controller using genetic algorithm for path planning of a humanoid in a complex environment cluttered with obstacles. The basic working of a genetic algorithm has been explained and an objective function for path optimization has been formulated using the logic of the genetic algorithm. The working of the controller has been tested both in simulation and experimental platforms using NAO humanoid robot. Finally, the results obtained from both the environments have been compared against each other with a good agreement between them.

Keywords: 

GA, humanoid robot, navigation, path planning

1. Introduction
2. Basic Overview of Genetic Algorithm
3. Genetic Algorithm Application
4. Control Architecture of Genetic Algorithm
5. Implementation of GA in Humanoid Path Planning
6. Conclusion
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