Dual-arm cooperative construction robots for wall repair with adaptive spraying and troweling

Plastering in construction involves two key processes: spraying and troweling. Most existing robotic systems can only perform one task, leading to low efficiency and inconsistent surface quality. This paper proposes an integrated dual-arm robotic system that unifies spraying, troweling, defect detection, and real-time repair into a continuous and automated workflow. Firstly, an improved detection model, based on YOLOv8 with Hybrid Attention Transformer (HAT) and Dynamic Snake Convolution (DSCov), increases recognition accuracy by 15.1 %. Secondly, a hybrid force–position control strategy is applied to improve plastering uniformity by adapting to varying wall conditions. Thirdly, a sensor fusion method combining inertial measurement units (IMU) and light detection and ranging (LiDAR) is used to correct deviations caused by uneven flooring. Experimental results show that the robot achieves an average deviation of only 0.38 mm from the target plaster thickness of 10 mm, while surface uniformity is improved with the proposed control and correction methods.