Robot-based facade spatial assembly optimization

Abstract Robotic involvement in construction is still in its initial stages compared to other industries. Conventionally, the facade panel picking position is done manually by trial and error. The designer chooses a place to pick up a facade piece within reach of the robot arm, and then simulates the entire pick and place process in a digital model before applying to the assembly on the construction job site. After that the designer might detect errors, collisions, or singularities, which require the designer to modify the position of picking by changing the location or orientation of the module, thus repeating the simulation cycle until they reach a satisfactory result with no errors or collisions. This work is usually considered monotonous, inefficient, and time consuming. Therefore, this research proposes an optimization process implemented in design stage of construction project via static performance criteria in order to automatically search for the best picking location within the reach of the robot arm. The goal is to automate the process of robot location finding and solve the limitations of modular robot assembly simulation processes in order to allow for effective execution during the robotic construction implementation. The proposed approach, called iFobot, consists of three modules: Facade Generative Modeling (iFobot-D), Robot Position Optimization (iFobot-B), and Culminating Feedback to BIM (iFobot-L). Specifically, the scope of the paper is limited to the robot arm and facade picking and placing location finding processes. This research allows initial assessment of the possible assembly process as regards the dimensions of modules and hence the overall dimensions of the system, which subsequently influences assembly implementation in the construction job site. A set of generative algorithms were developed using commercially developed visual programming language that automatically populate facade modules on the building envelope, find the robot and facade assembly locations with their quantity take-off, and integrate the module with the BIM environment. A case study has been developed to validate and test the proposed system. The results prove that the system generates optimized locations for the robot arm workstations with the lowest possible collision and reachability rate while addressing robot operation time reduction, thus reducing risks encountered during facade assembly and increasing productivity. Moreover, the iFobot is predicted to influence decision making during the facade assembly process on a physical construction job site.