Automation of crane control for block lifting based on deep reinforcement learning

Abstract

In shipyards, blocks are controlled by connecting the crane and block with wires during block erection. During block lifting, if a block is not carefully controlled, it will cause damage. Block lifting using crane operation is performed by controlling the number of wires, hooks, and equalizers. Consequently, predicting stable block lifting is difficult. In this study, we proposed a control method to determine static equilibrium. Initially, an algorithm for finding the initial equilibrium state of the block (IES algorithm) was proposed, followed by deep reinforcement learning (DRL)-based method for block lifting. The position, orientation, angular velocity of the block, and hoisting speed of the wires were applied as the DRL state. The control input of the crane was calculated by deriving the hoisting speed of the wires. To verify the proposed method, comparative studies on the application of the IES algorithm were carried out, and further block movement was compared. Conclusively, the proposed method effectively increased block lifting safety.