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International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(1): 26-34

Published online March 25, 2020

https://doi.org/10.5391/IJFIS.2020.20.1.26

© The Korean Institute of Intelligent Systems

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Jun-Ho Jung1 and Dong-Hun Kim2

1Department of Mechatronics Engineering, Kyungnam University, Changwon, Korea
2Department of Electrical Engineering, Kyungnam University, Changwon, Korea

Correspondence to :
Dong-Hun Kim (dhkim@kyungnam.ac.kr)

Received: January 20, 2020; Revised: March 10, 2020; Accepted: March 12, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This paper proposes the local path planning of a mobile robot using a novel grid-based potential method. The proposed method can be easily applied to a robotic system in a real environment because it is designed based on actual sensors. This method solves the local minimum problems that occur frequently in the potential field. A new repulsive field is created between adjacent obstacles that a mobile robot cannot pass through by calculating the distance between the robot and the obstacles. The generated repulsion field causes a mobile robot to escape from the local minimum. MATLAB simulations are used to compare the proposed and conventional potential field methods.

Keywords: Potential function, Grid potential field, Local minimum, Path planning

Jun-Ho Jung has been under M.S. candidate course at kyungnam university, Korea, since 2019.His research interests include mobile robots and intelligent control.

E-mail: jhjeong451@naver.com


Dong-Hun Kim received his B.S., M.S., and Ph.D. degrees from the Department of Electrical Engineering, Hanyang University, Korea, in 1995, 1997, and 2001, respectively. From 2001 to 2003, he was a research associate under several grants in the Department of Electrical and Computer Engineering, Duke University, NC, USA. In 2003, he joined Boston University, MA, USA, as a visiting assistant professor under several grants at the Department of Aerospace and Mechanical Engineering. In 2004, he was engaged in post-doctoral research at the School of Information Science and Technology, the University of Tokyo, Japan. Since 2005, he has been a professor at the Department of Electrical Engineering, Kyungnam University, Korea. His research interests include swarm robotics, mobile robots, decentralized control of autonomous vehicles, intelligent control, and adaptive nonlinear control.

E-mail: dhkim@kyungnam.ac.kr


Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2020; 20(1): 26-34

Published online March 25, 2020 https://doi.org/10.5391/IJFIS.2020.20.1.26

Copyright © The Korean Institute of Intelligent Systems.

Local Path Planning of a Mobile Robot Using a Novel Grid-Based Potential Method

Jun-Ho Jung1 and Dong-Hun Kim2

1Department of Mechatronics Engineering, Kyungnam University, Changwon, Korea
2Department of Electrical Engineering, Kyungnam University, Changwon, Korea

Correspondence to:Dong-Hun Kim (dhkim@kyungnam.ac.kr)

Received: January 20, 2020; Revised: March 10, 2020; Accepted: March 12, 2020

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes the local path planning of a mobile robot using a novel grid-based potential method. The proposed method can be easily applied to a robotic system in a real environment because it is designed based on actual sensors. This method solves the local minimum problems that occur frequently in the potential field. A new repulsive field is created between adjacent obstacles that a mobile robot cannot pass through by calculating the distance between the robot and the obstacles. The generated repulsion field causes a mobile robot to escape from the local minimum. MATLAB simulations are used to compare the proposed and conventional potential field methods.

Keywords: Potential function, Grid potential field, Local minimum, Path planning

Fig 1.

Figure 1.

Grid method.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 2.

Figure 2.

Robot view of obstacles in the direction of a goal point.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 3.

Figure 3.

Magnitude and range of the repulsive field: (a) co = 2, lo = 0.2, (b) co = 2, lo = 0.7, and (c) co = 3.5, lo = 0.7.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 4.

Figure 4.

Weight change of the repulsive potential.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 5.

Figure 5.

New repulsive field.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 6.

Figure 6.

Local minimum with two parallel adjacent cells. Planned path under (a) the conventional potential function method and (b) the proposed potential function method.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 7.

Figure 7.

Local minimum with three U-shaped adjacent cells. Planned path under (a) the conventional potential function method and (b) the proposed potential function method.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Fig 8.

Figure 8.

Local minimum with five U-shaped adjacent cells. Planned path under (a) the conventional potential function method and (b) the proposed potential function method.

The International Journal of Fuzzy Logic and Intelligent Systems 2020; 20: 26-34https://doi.org/10.5391/IJFIS.2020.20.1.26

Table 1 . Time step of path travel for each simulation.

Conventional methodProposed method
Robot1 in Figure 68478
Robot3 in Figure 68275
Robot3 in Figure 77774
Robot3 in Figure 88984

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