Research and Application of a Dynamic Priority Planning Algorithm for Multi-AGVs in Modern Fishery and Aquaculture Environment

Due to the low quality and efficiency of the paths generated by traditional AGV path planning algorithms, they are difficult to apply to modern fishing and aquaculture environments. Therefore, based on the traditional A * algorithm, a motion rationalization inspection strategy was first added in the 8-neighborhood extension to avoid the problems of AGV getting too close to surrounding obstacles and navigating between obstacles; Subsequently, the heuristic function of the A * algorithm was improved by introducing dynamic weight factors that combine path information, reducing blind searches and accelerating algorithm speed; Afterwards, a node deletion strategy was adopted to reduce the actual turning times of the AGV and make the path smoother. Then, the key points of the improved A * algorithm are fused into the DWA algorithm as navigation points to solve the problem of traditional DWA algorithm easily getting stuck in local optima during obstacle avoidance. Finally, based on the improved A * fusion DWA algorithm and combined with dynamic priority avoidance strategy, a multi AGV dynamic path planning algorithm based on modern fishery environment is proposed. Finally, simulation experiments in multiple scenarios showed that the improved A * algorithm reduced the average total turning angle by 37% compared to traditional A *, reduced the average number of turning points by 50.6%, and increased the algorithm speed by 58.2%; The fusion of the improved A * algorithm and DWA algorithm effectively avoids the problem of traditional DWA algorithm easily getting stuck in local optima; In the scenario of multi AGV collaboration, through 150 random task experiments, it was finally found that compared with the traditional fixed priority strategy, the algorithm of dynamic priority strategy reduced the average time by about 15 seconds, thus solving the problem that traditional AGV path planning algorithms are difficult to adapt to modern fishery and aquaculture environments.