Prefabricated construction has pioneered a new model in the construction industry, where prefabricated component modules are produced in factories and assembled on-site by construction workers, resulting in a highly efficient and convenient production process. Within the construction industry value chain, the smoothing and roughening of precast concrete components are critical processes. Currently, these tasks are predominantly performed manually, often failing to achieve the desired level of precision. This paper designs and develops a robotic system for smoothing and roughening precast concrete surfaces, along with a multi-degree-of-freedom integrated intelligent end-effector for smoothing and roughening. Point-to-point path planning methods are employed to achieve comprehensive path planning for both smoothing and roughening, enhancing the diversity of textural patterns using B-spline curves. In the presence of embedded obstacles, a biologically inspired neural network method is introduced for precise smoothing operation planning, and the A* algorithm is incorporated to enable the robot’s escape from dead zones. Experimental validation further confirms the feasibility of the entire system and the accuracy of the machining path planning methods. The experimental results demonstrate that the proposed system meets the precision requirements for smoothing and offers diversity in roughening, affirming its practicality in the precast concrete process and expanding the automation level and application scenarios of robots in the field of prefabricated construction.

The problem of self-organizing flexible wrapping for swarm robots refers to the spontaneous formation of stable, deformable, and adaptive structures by a robot swarm relying solely on local perception, without a global map or centralized control, to tightly enclose irregular or dynamic targets. This holds significant application value in fields such as environmental containment, disaster rescue, and wide-area monitoring. This paper proposes a self-organizing flexible wrapping method for swarm robots based on local density interactions. First, a local density field model integrating individual area weights is constructed to achieve the quantitative characterization of the target distribution space. Second, a weighted density is calculated based on the distributions of neighborhood robots and targets. Combined with diffusion and collision avoidance mechanisms, a distributed control law is designed to drive the swarm to adaptively converge towards the target boundaries while maintaining a uniform distribution. Finally, simulation and real-world experiments demonstrate that the proposed method can control the robot swarm to form a flexible wrapping structure. Compared with traditional methods, it significantly improves the conformity to complex target boundaries and overall adaptability, thereby verifying the feasibility and effectiveness of the proposed algorithm.