首页|Researchers from University of Michigan Report Recent Findings in Robotics (Enab ling Building Information Model-driven Humanrobot Collaborative Construction Wo rkflows With Closed-loop Digital Twins)
Researchers from University of Michigan Report Recent Findings in Robotics (Enab ling Building Information Model-driven Humanrobot Collaborative Construction Wo rkflows With Closed-loop Digital Twins)
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New research on Robotics is the subjec t of a report. According to news reporting out of Ann Arbor, Michigan, by NewsRx editors, research stated, "The introduction of assistive construction robots ca n significantly alleviate physical demands on construction workers while enhanci ng both the productivity and safety of construction projects. Leveraging a Build ing Information Model (BIM) offers a natural and promising approach to driving r obotic construction workflows." Financial support for this research came from National Science Foundation (NSF). Our news journalists obtained a quote from the research from the University of M ichigan, "However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construct ion work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes asbuilt information for th e ensuing construction and maintenance tasks. This research introduces an intera ctive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot's functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions , while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes in the robot's plan by changing the task sequenc e or workspace geometry or requesting a new motion plan to help the robot overco me the encountered uncertainties. A drywall installation case study is conducted to verify the proposed workflow. In addition, experiments are carried out to ev aluate the system performance using an industrial robotic arm in a research labo ratory setting that mimics a construction site and in the Gazebo simulation."
Ann ArborMichiganUnited StatesNort h and Central AmericaEmerging TechnologiesMachine LearningNano-robotRobo tRoboticsRobotsUniversity of Michigan
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