首页|Findings from Nanyang Technological University Yields New Findings on Robotics ( Task Sensing and Adaptive Control for Mobile Manipulator In Indoor Painting Appl ication)
Findings from Nanyang Technological University Yields New Findings on Robotics ( Task Sensing and Adaptive Control for Mobile Manipulator In Indoor Painting Appl ication)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating from Singapore, Singapor e, by NewsRx correspondents, research stated, “Robotic painting, particularly in industrial and construction domains, has attracted considerable attention due t o its precision and uniformity. However, current systems are constrained by inad equate precision and effectiveness in painting, particularly when applied to lar ge-scale surfaces.” Financial support for this research came from Agency for Science Technology & Research (A*STAR). Our news editors obtained a quote from the research from Nanyang Technological U niversity, “This article introduces an advanced adaptive robotic painting system that incorporates a mobile manipulator (MM) designed to enhance both accuracy a nd efficiency in indoor surface painting through two innovative submodules: auto mated trajectory generation and MM adaptive control policy (ACP). Initially, to autonomously generate the accurate trajectory, we propose the attention-aware gr aph network for refining 3-D surface model to significantly enhance the accuracy and efficiency of environment modeling. Following this, the RayCast 3-D mapping technique is introduced for precise projection of 2-D images onto arbitrary 3-D surfaces with its flexibility and adaptability. Furthermore, we introduce an MM ACP comprising a trajectory controller and a close-loop whole-body controller. This dual-controller system enables the MM to swiftly move to target poses and s moothly follow trajectories, with the capability to autonomously switch between control paradigms based on task requirements. In addition, Experimental results demonstrate that the proposed automated trajectory generation strategy, coupled with the MM ACP, significantly improves the accuracy of environmental perception and the efficiency of trajectory generation.”
SingaporeSingaporeAsiaEmerging Tec hnologiesMachine LearningRoboticsRobotsNanyang Technological University
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