Work

Path Planning Design and Experiment for a Recirculating Aquaculture AGV Based on Hybrid NRBO-ACO with Dueling DQN

Year: 2025

Type: article

Abstract: This study introduces an advanced automated guided vehicle (AGV) specifically designed for application in recirculating aquaculture systems (RASs). The proposed AGV seamlessly integrates automated feeding, real-time monitoring, and an intelligent path-planning system to enhance operational efficiency. To achieve optimal and adaptive navigation, a hybrid algorithm is developed, incorporating Newton–Raphson-based optimisation (NRBO) alongside ant colony optimisation (ACO). Additionally, dueling deep Q-networks (dueling DQNs) dynamically optimise critical parameters, thereby improving the algorithm’s adaptability to the complexities of RAS environments. Both simulation-based and real-world experiments substantiate the system’s effectiveness, demonstrating superior convergence speed, path quality, and overall operational efficiency compared to traditional methods. The findings of this study highlight the potential of AGV to enhance precision and sustainability in recirculating aquaculture management.

Source: Drones

DOI: 10.3390/drones9070476

Authors: Zhengjiang Guo, Yingkai Xia, Jiajun Liu, Jian Gao, Peng Wan, Kan Xu

Institutions: Huazhong Agricultural University, Ministry of Agriculture and Rural Affairs, Wuhan Ship Development & Design Institute

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137