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

Rafal Szczepanski, Artur Bereit and Tomasz Tarczewski
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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

Energies, 2021, vol. 14, issue 20, 1-14

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.

Keywords: local path planning problem; artificial potential field; augmented reality; mobile robot (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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