Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.

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Publication Details

Journal
Technologies
Published
2024-12-10
DOI
https://doi.org/10.3390/technologies12120255
Citations
4
Primary Topic
Mosquito-borne diseases and control
Type
article
Field-Weighted Citation Impact
2.17

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Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control

Zhenyuan Yang, Mohan Rajesh Elara, Prithvi Krishna Chittoor, Sriniketh Konduri et al.
4 citations
Technologies
Mosquito-borne diseases and control
2.17
article

Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control

Zhenyuan Yang, Mohan Rajesh Elara, Prithvi Krishna Chittoor, Sriniketh Konduri, D. Bhanu Priya, Grace Hephzibah Jaichandar
article en
4 citations

Abstract

The mosquitoe population is reaching critical levels globally, posing significant threats to public health and ecosystems due to their role as vectors for diseases. This paper presents the development of a mobile robotic platform named Boa Fumigator with autonomous fumigation and prioritized path planning capabilities in urban landscapes. The robot’s locomotion is based on a differential drive, facilitating easier maneuverability on semi-automated planar surfaces in landscaping infrastructure. The robot’s fumigator payload consists of a spray gun and a chemical tank, which can pan and fumigate up to 4.5 m from the ground. The system incorporates a wireless charging mechanism to allow for the autonomous charging of the mosquito catchers. A genetic algorithm fused with an A*-based prioritized path planning algorithm is developed for efficient navigation and charging of mosquito catchers. The algorithm, designed for maximizing charging efficiency, considers the initial charge percentage of mosquito catchers and the time required for fumigation to determine the optimal path for charging and fumigation. The experiment results show that the path planning algorithm can generate an optimized path for charging and fumigating multiple mosquito catchers based on their initial charge percentage. This paper concludes by summarizing the key findings and highlighting the significance of the fumigation robot in landscaping applications.

TechnologiesVol. 12(12)
SRM Institute of Science and Technology (IN), Singapore University of Technology and Design (SG), Singapore Polytechnic (SG)
Singapore University of Technology and Design
Sustainable cities and communities
Openalex Percentile: Top 11%
Mosquito-borne diseases and control
2.17
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Boa Fumigator: An Intelligent Robotic Approach for Mosquito Control — Zhenyuan Yang, Mohan Rajesh Elara, et al. · Technologies (2024) | TGRS Research Map | TGRS