Mechanism design and multilevel topology-size optimization of a wheel-legged rebar tying robot for unstructured meshes

Purpose This paper aims to address the challenges of wheel slip, instability and low precision encountered by conventional robots on unstructured rebar meshes in major infrastructure projects like nuclear power plants and wind power foundations. A novel wheel-legged hybrid tying robot with high environmental adaptability was developed. The proposed robot, optimized via a coordinated multilevel approach, successfully integrates lightweight design with robust traversability and precise operational stability on flexible rebar grids. Design/methodology/approach A hybrid mobile platform integrating a six-wheeled longitudinal self-guiding mechanism and a two-legged lateral tumbling mechanism was developed. A multilevel optimization framework was constructed: the first level used the Solid Isotropic Material with Penalization–based topology optimization to determine the macro-configuration of the frame; the second level introduced Response Surface Methodology for multi-objective fine-tuning of plate thicknesses. Finally, ADAMS dynamic simulations and physical prototype tests were conducted to verify motion stability. Findings Results indicate that the robot possesses excellent terrain adaptability, with a stable longitudinal angular velocity of 270°/s and a mean velocity of 200 mm/s with minimal fluctuation. Multilevel optimization reduced the frame from 23.65 to 15.65 kg, achieving a cumulative weight reduction of 33.8% while exceeding design thresholds for strength and stiffness. Prototype experiments confirmed that the robot completes four continuous node ties within 20 s, validating the design and optimization strategy. Originality/value This study proposes a unique wheel-legged adaptive configuration and a multilevel optimization method for unstructured environments. It resolves the contradiction between lightweight design and dynamic stability, providing a reliable platform and theoretical reference for intelligent construction in major infrastructure.