ROS-Based Robot Simulation for Repetitive Labor-Intensive Construction Tasks

Abstract

Utilizing autonomous robots to perform repetitive and labor-intensive tasks in the construction industry is one of the most promising directions to explore in order to enhance productivity, safety/health, and quality of construction projects. Such robots must have construction-related knowledge and skills in order to generate task plans capable of dealing with the unique and highly dynamic work environment of typical construction sites. However, autonomous and flexible behavior is currently impossible due to the lack of a robotics-compatible construction knowledge base. To overcome this bottleneck, this study proposes the establishment and utilization of such a construction knowledge base for use in generating autonomous behavior in robots. Specifically, this study provides an implementation of a small, mobile, autonomous robotics platform capable of performing fine-grained construction tasks in dynamic environments. Such tasks include painting, drilling screws, and transporting material and equipment. The platform is tested with a simulated robot based on the KUKA youBot tasked with painting walls in a room containing obstacles. In the simulation results, the proposed approach shows promise in being able to achieve autonomous operation of construction robots. Further development of this study will include implementing a more diverse set of skills, expanding the construction knowledge base, and tailoring localization, navigation planning, and task planning algorithms for the characteristics of the construction sites and the hardware tools used.