Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites

a r t i c l e i n f o Unlike robotics in the manufacturing industry, on-site construction robotics has to consider and address two unique challenges: 1) the rugged, evolving, and unstructured environment of typical work sites; and 2) the reversed spatial relationship between the product and the manipulator, i.e., the manipulator has to travel to and localize itself at the work face, rather than a partially complete product arriving at an anchored manipulator. The presented research designed and implemented algorithms that address these challenges and enable autonomous robotic assembly of freeform modular structures on construction sites. Building on the authors' previous work in computer-vision-based pose estimation, the designed algorithms enable a mobile robotic manipulator to: 1) autonomously identify and grasp prismatic building components (e.g., bricks, blocks) that are typically non-unique and arbitrarily stored on-site; and 2) assemble these components into pre-designed modular structures. The algorithms use a single camera and a visual marker-based metrology to rapidly establish local reference frames and to detect staged building components. Based on the design of the structure being assembled, the algorithms automatically determine the assembly sequence. Furthermore, if a 3D camera is mounted on the manipulator, 3D point clouds can be readily captured and registered into a same reference frame through our marker-based metrology and the manipulator's internal encoders, either after construction to facilitate as-built Building Information Model (BIM) generation, or during construction to document details of the progress. Implemented using a 7-axis KUKA KR100 robotic manipulator, the presented robotic system has successfully assembled various structures and created as-built 3D point cloud models autonomously, demonstrating the designed algorithms' effectiveness in autonomous on-site construction robotics applications. Several studies have argued that among all industries, construction has seen a significant productivity decrease over the last several decades compared to other industries [30]. Construction has also been documented to have some of the highest rates of workspace injuries and fatalities [6]. Automation and robotics in construction (ARC) has the potential to relieve human workers from repetitive and dangerous tasks, and has been extensively promoted in the literature as a means of improving construction productivity and safety [3]. Compared to the tangible benefits of automation and robotics identified by the manufacturing industry, the construction industry is still exploring feasible and broadly deployable ARC applications [3]. This can be attributed to several commercial and technical challenges. From the commercial perspective, the fragmented and risk-averse nature of …