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

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.