Abstract

The coating process of a ship-hull interior requires automation owing to significant occupational hazards associated with its working environment. The interior of a ship hull is large and structurally complex, and hence requires a highly redundant manipulator for such automation. This study proposed a control framework for redundancy resolution based on a pre-generated End-Effector(EE) reference path for an 8 Degree-of-Freedom (DoF) planar manipulator, used for coating automation. The proposed method employed tools such as Reinforcement Learning (RL) and Null Space Projection-based Reinforcement Learning (NSP-based RL). In RL, the action directly specifies the joint angular velocities, whereas in NSP-based RL, the NSP objective function’s gradient vector is generated. RL- and NSP-based RL share the same reward function and observation space. To mitigate the reward dominance and divergence issues that can arise in learning-based approaches, this study incorporated sequential sub-goal tracking and path planning information into the state definition. The proposed methods were evaluated in cluttered environments and compared with conventional NSP approaches. Experimental results showed that RL achieved superior obstacle avoidance, while NSP-based RL produced smoother and more consistent EE trajectories and velocities across all targets.

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