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In construction applications, a robot is commonly seen a semi-automated tool or a piece of equipment that assists with specialized work tasks. However, as robots become more technically capable and widely available, they may be seen more as a teammate or co-worker that collaborates with human crews. Using a survey questionnaire, 63 project managers from two national construction management firms in the US were shown videos of three different applications of robotic systems, each exhibiting different characteristics, and were asked to share their perceptions of the robot. Through a between and across group comparison of their responses, we found that a robot was more likely to be seen as a teammate when its movement was less unpredictable, it was seen as more productive than human workers, it was considered durable, it remained constantly active, it took its surroundings into account before moving, it worked well alongside human workers, it was not unreliable, and it made the task more predictable. These findings identify clear challenges for human-robot teaming and the design of robotic systems for construction applications. 

Construction, the last major analog and craft manufacturing industry, is showing early signs of industrialization through the emergence of new robotic and automated systems that can perform construction tasks in situ. While much is understood about the technical and economic challenges to be overcome for widespread adoption of robotics, less is known about the human barriers to adoption, and much less is summarized. Considering the amount of human cooperation required by existing robotic applications, a comprehensive review of barriers that are cognitive or perceptual in nature using a systematic literature assessment methodology is warranted. However, such a review is not straightforward to design. While matters of cognition and perception as pertinent to construction and automation may be queried directly from the literature, there is no certainty that a review based on directly querying abstract phenomena (i.e., perception) could be comprehensive. Thus, systematically reviewing this topic calls for a robust methodology for the design of database queries. In this paper, we perform text analysis with the quanteda package for R in order to (1) understand the language composition of an initial review corpus, and (2) with that understanding design further queries to capture additional articles otherwise not possible through standard query design. Findings indicate that performing text analysis on a systematic review design can produce valuable insight into a review corpus and inform queries that capture additional unique literature relevant to the review.