In this work a dimension estimation system is developed for logistic applications. The purpose of the system is to find the oriented minimum bounding box of a package. The volume of the bounding box and the volumetric weight of the package were determined. Intel® RealSense™ Depth Camera D415 was used to obtain point cloud of the package view. Then smoothing and filtering algorithms were applied to eliminate the noise and the distortion. The object is isolated from the background and the minimum bounding box is determined. Different geometric shapes were tested including: hardboard calibrated cube, cube with an uneven top, uncalibrated box, cube with a sloped side, small cylinder, tube and cylinder with irregular top. Statistical analysis of the measurements revealed an average error rates less than 0.5cm for normal work conditions. This error rate is acceptable for most logistic operations
KEYWORDS: Control systems, Signal attenuation, Electronic filtering, Actuators, Systems modeling, Matrices, Sensors, Spatial filters, Feedback control, Finite element methods
Much research in this decade has concentrated on the application of modal space to simplify control design and system monitoring. This investigation will address the fidelity limitations of the modal filter based classical control strategies. Specifically, when the eigen-parameters shift, in concert with semi-global changes in the systems structural properties resulting in similar mode shape but dramatic shift in frequency. Expressly, this examination will concentrate on two spatially similar five bay box trusses of exactly the same geometry (connections and element lengths) and different materials (steel and plastic) and some shift in cross-sectional dimension. The limitation of these parameter shifts should present a practical bound for future application. The investigation incorporated both analytic and experimentally based models of the structural frequency response and residue matrices to analytically model the control problem. The control is investigated for the lower frequency modes due to experimental model limitations. Investigation includes single and multi-mode control.
Applications of vision based remotely operated robotic systems range from planetary exploration to hazardous waste remediation. For space applications, where communication time- lags are large, the target selection and robot positioning tasks may be performed sequentially, differing from conventional tele-robotic maneuvers. For these point-and-move systems, the desired target must be defined in the image planes of the cameras either by an operator or through image processing software. Ambiguity of the target specification will naturally lead to end-effector positioning errors. In this paper, the target specification error covariance is shown to transform linearly to the end-effector positioning error. In addition, a methodology for target specification error optimal estimation of camera view parameters of a vision based robotic system is presented. A cost function is examined based on minimizing the end-effector error covariance matrix. Simulation and experimental results are presented.
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