Journal of Computational Applied Mechanics

Implementation of Behavior-Based Navigation Algorithm on Four-Wheel Steering Mobile Robot

Document Type : Research Paper

Authors

1 Mechatronics Laboratory, Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran

2 Assistant Professor, Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran.

Abstract

In recent years, wheeled autonomous mobile robots have become widely used in a number of industrial applications. Therefore, accurate and efficient controllers are required in order to assure safe and accurate navigation of these vehicles. In this study, an effective behavior-based navigation algorithm (BBNA) is applied to control the trajectory of the four-wheel steering (FWS) mobile robot. The BBNA combines the ‘Goal-to-Goal’ and ‘Obstacle Avoidance’ behaviors into one comprehensive navigation strategy. With this algorithm, many switching between modes occurs over a short amount of time, which increases the risk of creating the chattering phenomenon. Due to overcoming this phenomenon, an additional mode is considered between the ‘Go-to-Goal’ and ‘Obstacle Avoidance’ modes that is called ‘Follow-Wall’ behavior. At first, the BBNA was designed to control the navigation of a point mass robot. One of the significant characteristics of BBNA is that its control commands can be used to calculate the linear and angular velocity of a unicycle mobile robot. Thus, the BBNA can navigate the unicycle mobile robot successfully to the goal position. In order to apply the BBNA to an FWS mobile robot, its dynamic equations must be converted to those of a unicycle mobile robot. The present study determines the dynamic equations of the FWS mobile robot by using the Ackermann- Jeantnat model of steering. Since these equations are the same as those for the unicycle mobile robot, the FWS mobile robot can be controlled by the BBNA. Finally, the implementation of the BBNA for the FWS mobile robot is simulated using MATLAB software. Simulated results indicate that BBNA generates an optimal path by perfectly switching between ‘Go to Goal’, ‘Obstacle Avoidance’, and ‘Follow Wall’ modes, which keeps the FWS mobile robot arriving at the goal position.

Keywords

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Journal of Computational Applied Mechanics
Volume 52, Issue 4
December 2021
Pages 619-641
  • Receive Date: 04 September 2021
  • Revise Date: 20 December 2021
  • Accept Date: 26 December 2021