In crab pond environments, obstacles such as long aerobic pipelines, aerators, and ground cages are usually sparsely distributed. Automatic feeding boats can navigate while avoiding obstacles and execute feeding tasks along planned paths, thus improving feeding quality and operational efficiency. In large-scale crab pond farming, a single feeding operation often fails to achieve the complete coverage of the bait casting task due to the limited boat load. Therefore, this study proposes a multi-voyage path planning scheme for feeding boats. Firstly, a complete coverage path planning algorithm is proposed based on an improved genetic algorithm to achieve the complete coverage of the bait casting task. Secondly, to address the issue of an insufficient bait loading capacity in complete coverage operations, which requires the feeding boat to return to the loading wharf several times to replenish bait, a multi-voyage path planning algorithm is proposed. The return point of the feeding operation is predicted by the algorithm. Subsequently, the improved Q-Learning algorithm (I-QLA) is proposed to plan the optimal multi-voyage return paths by increasing the exploration of the diagonal direction, refining the reward mechanism and dynamically adjusting the exploration rate. The simulation results show that compared with the traditional genetic algorithm, the repetition rate, path length, and the number of 90° turns of the complete coverage path planned by the improved genetic algorithm are reduced by 59.62%, 1.27%, and 28%, respectively. Compared with the traditional Q-Learning algorithm, average path length, average number of turns, average training time, and average number of iterations planned by the I-QLA are reduced by 20.84%, 74.19%, 48.27%, and 45.08%, respectively. The crab pond experimental results show that compared with the Q-Learning algorithm, the path length, turning times, and energy consumption of the I-QLA algorithm are reduced by 29.7%, 77.8%, and 39.6%, respectively. This multi-voyage method enables efficient, low-energy, and precise feeding for crab farming.
Sun et al. (Wed,) studied this question.