Pathfinding in Robotics and Automation for Sustainable Construction Practices

With the increasing demand of new buildings and retrofits, the need for construction continues to rise despite the industry’s multitude of health risks, safety concerns, and time overruns. As the 9th largest industry by GDP in the United States, construction has been slow to adopt new methods to improve efficiency and worker well-being. In a similar industry, manufacturing has witnessed higher productivity and safer practices due to the acceptance and integration of new technology including robotics and automated workflows. Though construction and manufacturing cannot be directly compared due to the scale and complexity of the end product, automated practices in manufacturing can be adapted to improve construction and health of the labor force. One specific robotic platform, drones, are becoming increasingly popular in construction with use cases such as surveying and surveillance. Spray coating technology for drones are also being developed though their architectural use cases are privately withheld and to be studied. As a case study, this thesis explores the application of spray polyurethane foam (SPF) and paint to present a framework to conduct construction spray coating applications by a spray coating drone. The study utilizes computational methods adapted from automotive manufacturing for path planning, data from toxicity studies, and fall risk statistics to compare time of execution, chemical exposure, and risk of fall between a human, single drone, and dual drone applicators. The study signifies that the use of a spray coating drone to complete SPF applications can cut the amount of time required for the task by at least half, reduce exposure by approximately 60%, and negate any height risk during application. From mapping toxicity, comparing speed, and quantifying height risks, this proof of concept serves as a call to action for this technology and suggests new roles for construction workers to emerge from working alongside robots.