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Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Author

Listed:
  • Rafal Szczepanski

    (Institute of Engineering and Technology, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland)

  • Artur Bereit

    (Institute of Engineering and Technology, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland)

  • Tomasz Tarczewski

    (Institute of Engineering and Technology, Nicolaus Copernicus University, Grudziadzka 5/7, 87-100 Toruń, Poland)

Abstract

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The artificial potential field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of artificial potential field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel artificial potential field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

Suggested Citation

  • Rafal Szczepanski & Artur Bereit & Tomasz Tarczewski, 2021. "Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality," Energies, MDPI, vol. 14(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6642-:d:655961
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    Citations

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    Cited by:

    1. Zhuoling Jiang & Xiaodong Zhang & Pei Wang, 2023. "Grid-Map-Based Path Planning and Task Assignment for Multi-Type AGVs in a Distribution Warehouse," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    2. Kazuki Nonoyama & Ziang Liu & Tomofumi Fujiwara & Md Moktadir Alam & Tatsushi Nishi, 2022. "Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization," Energies, MDPI, vol. 15(6), pages 1-20, March.

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