Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios
Open Access
- 10 May 2023
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 12 (10), 2178
- https://doi.org/10.3390/electronics12102178
Abstract
Factory safety inspections are crucial for maintaining a secure production environment. Currently, inspections are predominantly performed manually on a regular basis, leading to low efficiency and a high workload. Utilizing inspection robots can significantly improve the reliability and efficiency of these tasks. The development of robot localization and path planning technologies ensures that factory inspection robots can autonomously complete their missions in complex environments. In response to the application requirements of factory inspections, this paper investigates mapping, localization, and path planning methods for robots. Considering the limitations of cameras and laser sensors due to their inherent characteristics, as well as their varying applicability in different environments, this paper proposes SLAM application systems based on multi-line laser radar and visual perception for diverse scenarios. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that combines the A-star algorithm and time elastic band method is introduced. This approach effectively resolves the problem of path planning becoming trapped in local optima in complex environments, subsequently enhancing the inspection efficiency of robots. Experimental results demonstrate that the designed SLAM and path planning methods can satisfy the inspection requirements of robots in complex scenarios, exhibiting excellent reliability and stability.Keywords
Funding Information
- Graduate Education and Teaching Quality Improvement Project of Beijing University of Architecture and Architecture (J2023017)
- Lecturer Support Plan Project of Beijing University of Architecture and Architecture (YXZJ20220804)
- Open Project of Anhui Provincial Key Laboratory of Intelligent Building and Building Energy Efficiency, Anhui Jianzhu University (IBES2020KF06)
- BUCEA Post Graduate Innovation Project
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