LAUSR

This paper presents an improved Dung Beetle Optimization (DBO) algorithm assisted by a neural network to solve the inverse kinematics of a 5‐DoF (degree‐of‐freedom) warehouse loading and unloading robot. The neural network was trained on pose data of the robot arm's end‐effector and generated predicted joint angles as output. A genetic algorithm was employed to enhance the design of the network. The selected network structure has been demonstrated to reduce the mean Euclidean Distance (ED) of the end‐effector positions to below 9 mm. To achieve more accurate inverse kinematics predictions, experimental tuning was conducted to determine the optimal configuration for the DBO algorithm. Then we developed a hybrid model that integrated the neural network and the DBO algorithm. Experimental results indicated that the positioning errors of the DBO and hybrid algorithms were significantly reduced compared to those of the neural network. Moreover, the proposed model reduced the total inference time by 20.7% and the mean ED by 16.4% compared to the DBO algorithm. In trajectory planning validation experiments, all fitting errors were less than 5 mm, thereby meeting the practical requirements for warehouse handling. Therefore, the proposed neural network‐assisted evolutionary algorithm outperforms both the neural network and DBO algorithms, providing a fast and accurate solution to the inverse kinematics problem of the warehouse manipulator.