3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture point-clouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.
The composite sheet layup process involves stacking several layers of a viscoelastic prepreg sheet and curing the laminate to manufacture the component. Demands for automating functional tasks in the composite manufacturing processes have dramatically increased in the past decade. A simulation system representing a digital twin of the composite sheet can aid in the development of such an autonomous system for prepreg sheet layup. While Finite Element Analysis (FEA) is a popular approach for simulating flexible materials, material properties need to be encoded to produce high-fidelity mechanical simulations. We present a methodology to predict material parameters of a thin-shell FEA model based on real-world observations of the deformations of the object. We utilize the model to develop a digital twin of a composite sheet. The method is tested on viscoelastic composite prepreg sheets and fabric materials such as cotton cloth, felt and canvas. We discuss the implementation and development of a high-speed FEA simulator based on the VegaFEM library [29]. By using our method to identify sheet material parameters, the sheet simulation system is able to predict sheet behavior within 5 cm of average error and have proven its capability for 10 fps real-time sheet simulation.
3D reconstruction technology is used in a wide variety of applications. Currently, automatically creating accurate pointclouds for large parts requires expensive hardware. We are interested in using low-cost depth cameras mounted on commonly available industrial robots to create accurate pointclouds for large parts automatically. Manufacturing applications require fast cycle times. Therefore, we are interested in speeding up the 3D reconstruction process. We present algorithmic advances in 3D reconstruction that achieve a sub-millimeter accuracy using a low-cost depth camera. Our system can be used to determine a pointcloud model of large and complex parts. Advances in camera calibration, cycle time reduction for pointcloud capturing, and uncertainty estimation are made in this work. We continuously capture pointclouds at an optimal camera location with respect to part distance during robot motion execution. The redundancy in pointclouds achieved by the moving camera significantly reduces errors in measurements without increasing cycle time. Our system produces sub-millimeter accuracy.
We present a framework for identifying, communicating, and addressing risk in shared-autonomy mobile manipulator applications. This framework is centered on the capacity of the mobile manipulator to sense its environment, interpret complex and cluttered scenes, and estimate the probability of actions and configuration states that may result in task failures, such as collision (i.e., identifying “risk”). If the threshold for acceptable risk is exceeded, a remote operator is notified and presented with timely, actionable information in which the person can quickly assess the situation and provide guidance for the robot. This framework is demonstrated with a use case in which a mobile manipulator performs machine tending and material handling tasks.
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