Improved Coverage Path Planning for Indoor Robots Based on BIM and Robotic Configurations

Zhengyi Chen (), Keyu Chen (), Changhao Song (), Xiao Zhang (), Boyu Wang () and Jack C. P. Cheng ()
Additional contact information
Zhengyi Chen: The Hong Kong University of Science and Technology
Keyu Chen: Hainan University
Changhao Song: The Hong Kong University of Science and Technology
Xiao Zhang: The Hong Kong University of Science and Technology
Boyu Wang: The Hong Kong University of Science and Technology
Jack C. P. Cheng: The Hong Kong University of Science and Technology

Chapter Chapter 137 in Proceedings of the 28th International Symposium on Advancement of Construction Management and Real Estate, 2024, pp 1975-1994 from Springer

Abstract: Abstract This paper proposes an improved CPP system based on building information modeling (BIM) and robotic configurations for indoor robots. Firstly, BIM is semantically enriched for CPP tasks, based on which semantic trapezoidal grid maps are constructed with accurate information. Secondly, a new concept called “Coverage Bonus” is integrated into the coverage pattern analysis, which can be applied in the scenario that robot’s coverage size is different from its body size. Finally, the coverage sequence is optimized by solving Cluster Generalized Traveling Salesman Problem (CGTSP), and the generated paths can observe the travel rules and obtain high efficiency. The CPP system is validated in a representative university building floor and a typical anti-epidemic robot. It is demonstrated that the BIM-based TGM generation method can improve mapping quality compared to Classical methods. In addition, the improved CPP algorithm and one of its ablation studies achieve the best and similar coverage performance (e.g., 97% coverage ratio). However, the improved CPP algorithm outperforms this ablation study in satisfying the coverage rules strictly.

Keywords: Coverage path planning; Indoor robots; Building information modelling; Coverage bonus; Cluster Generalized Traveling Salesman Problem (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-97-1949-5_138

Ordering information: This item can be ordered from
http://www.springer.com/9789819719495

DOI: 10.1007/978-981-97-1949-5_138

Access Statistics for this chapter

More chapters in Lecture Notes in Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().