Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization

• Published in: International journal of advanced robotic systems Vol. 17; no. 5; p. 172988142095901
• Main Authors: Ma, Tao, Liu, Shuhai, Xiao, Huaping
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.09.2020; Sage Publications Ltd; SAGE Publishing
• Subjects: Ant colony optimization; Control algorithms; Cooperative control; Leak detection; Multiple robots; Natural gas; Offshore; Offshore drilling rigs; Offshore engineering; Offshore platforms; Offshore structures; Search algorithms; Search methods; Search process; Software; Offshore platform; multirobot collaboration; ant colony optimization; natural gas leak
• ISSN: 1729-8806; 1729-8814; 1729-8814
• DOI: 10.1177/1729881420959012

Natural gas leakage on offshore platforms has a great impact on safety production, and effective and reasonable leakage detection methods can prevent the harm caused by natural gas leakage. This article proposes a method based on ant colony optimization (ACO) for multirobots to collaboratively search for leaking natural gas sources on offshore platforms. First, analyze the structure and environment of the offshore platform, use Fluent software to simulate the diffusion process of natural gas leaked from the platform, and establish a diffusion model of natural gas leaked from various aspects, such as the layout of different platforms, the number of leaked gas sources, and the concentration of leaked gas sources. In terms of multirobot cooperative control, we analyzed and improved the ant colony algorithm and proposed a multirobot cooperative search strategy for gas search, gas tracking, and gas source positioning. The multirobot search process was simulated using MATLAB software, and the robot on the detection effect of multirobots was analyzed in many aspects, such as quantity, location of leak source, and a number of leak sources, which verified the feasibility and effectiveness of the multirobot control strategy based on optimized ACO. Finally, we analyze and compare the two control algorithms based on ACO and cuckoo search algorithm (CSA). The results show that the ACO-based multirobot air source positioning effect is significantly better than CSA.