Constructing Martian habitats presents significant challenges due to the harsh environmental conditions and limited resources available. In the presented study, a robotic assembly method has been developed that incorporates K-means clustering for task allocation and topological interlocking. The topological interlocking of Voronoi-based components provides an internally force-locked system, which facilitates both the robotic assembly process and the structural stability of the habitat. The clustering is leveraged for production planning objectives, including resource allocation and scheduling operations for assembling components. This method addresses assembly challenges of nonuniform components and facilitates the stacking of prefabricated 3D-printed Voronoi-based components using mobile robots. Experimental tests show that the proposed approach is practical and scalable, offering a feasible solution for autonomous Martian habitat construction. It contributes to laying the groundwork for sustainable autonomous construction systems.

The construction industry faces persistent productivity shortfalls and rising carbon dioxide emissions, which drives a shift toward the use of low-carbon materials and higher degrees of automation. Timber, a renewable and carbon-sequestering material, becomes especially compelling when combined with robotic fabrication. Although rapid advances have been implemented in the last decade, research and practice remain fragmented, and systematic evaluations of technological readiness are scarce. This gap is addressed in this review through critical literature synthesis of robotic timber construction, combining bibliometric analysis with a comparative evaluation of twelve representative case studies from 2020 to 2025. Computational and robotic tools are mapped across the design to fabrication pipeline, and emerging advancements are identified such as digital twins, real-time adaptive workflows, and machine learning driven fabrication, alongside discrete and circular strategies. Barriers to scale up are also assessed, including mid-level technology readiness, regulatory and safety obligations for human–robot interaction, evidence on cost and productivity, and workforce training needs. By clarifying the current level of robotization and specifying both research gaps and industrial prerequisites, this study provides a structured foundation for the next phase of development. It helps scholars by consolidating methods and metrics for rigorous evaluation, and it helps practitioners by highlighting pathways to scalable, certifiable, and circular deployment that align cost, safety, and training requirements.