Beyond Trial and Error (2025-01)¶

Automation, including the use of robots, is increasingly presented as a potential solution to the lagging efficiency of the construction industry. Buildings designed for robotic construction may be different than traditional buildings, and computation can help reveal these differences. In response, the goal of this research is to assess the challenges, opportunities, and potential of robotic construction to be incorporated into early-stage design exploration and multi-objective optimization. It evaluates robotic capabilities for performing construction tasks, determines how designs with varying structural systems can leverage robotic constructability assessment, and shows which tasks and design types offer good venues for evaluating construction metrics during design optimization. The first part reviews literature in design, fabrication, and construction, covering design-based robotic research in which robots are replacing traditionally human tasks in standard construction, as well as research in which a custom design can only be built with a novel construction approach. The role of robots in construction is then examined through a task-based review, which maps existing robots and their capabilities to potential subtasks during sequential construction phases, from initial offsite preparation to completion. Finally, a case study built on insights from the literature review introduces an initial construction metric tied to cumulative discrete material delivery time. This metric helps distinguish between high and low-quality designs in terms of constructability, showcasing how considering robotics can enhance early-stage design assessment. A trade-off between embodied carbon and a robotic construction score emerged in the design options, with the number of elements affecting both factors. The case study suggests that adjustments to the building massing can lower embodied carbon without negatively impacting the construction score. Similar relationships could be uncovered by incorporating the objectives and constraints identified in this paper in future optimization workflows.