Data-Based Reachability Analysis and Optimized Robot Positioning for Co-Design of Construction Processes.

Growing demand for new buildings and infrastructure, combined with stagnating productivity, is making new innovations and automation in the construction sector crucial. Already during the design and planning phase the feasibility of the construction process has to be analyzed in order to avoid problems during the construction phase. This includes the reachability of building elements and the positioning of construction robots. However, computing the reachability of target poses using inverse kinematics (IK) is time-consuming and does not allow for an immediate feedback. For this reason, a Random Forest classifier is trained to replace the calculation of IK. This decreases the computation time by a factor of more than 10 ⁴ and allows for immediate feedback on reachability. It achieves a high accuracy of around 97 %, which is shown for two exemplary robots. Due to the reduced computational effort, the classifier is used to optimize the robot base positions. The number of different base positions is minimized such that each target pose is reachable from at least one position. The optimization is successfully applied to a realistic pavilion with an accuracy of more than 99.99 %. This contribution shows the immense advantage of using the proposed method to speed up planning of construction processes and enable co-design to improve and guarantee the feasibility of the construction process for a specific building design.