Farid Arvani

Dynamic Model of a Mobile Offshore Drilling Unit in Deep Water Environments for Drilling Simulation

In this study, a dynamic model of a Mobile Offshore Drilling Unit (MODU) is described that simulates drilling scenarios, imposed by the environmental factors in offshore drilling. The Response Amplitude Operators (RAOs) of an industry-recognized semi-submersible MODU are modeled for all six degrees of freedom. A stochastic modeling of waves in the North Sea is used and heave disturbance induced by elevation motion of sea surface is modeled using the JONSWAP spectrum. A bond graph model of a MODU predicts axial vibration, torsional vibration, and coupling between axial and torsional vibration due to bit-rock interaction. Axial and torsional submodels use a lumped-segment approach. The model can predict the expected coupling between Weight On Bit (WOB), bit speed, and bit-rock interface conditions. A series of sensitivity analyses were performed to investigate the significance of MODU motion on WOB fluctuations.Copyright

Sampling-based path planning for robust feature-based visual servoing

Classical image-based visual servo methods regulate error in the image space and undergo difficulties when the initial and desired robot positions are distant. It is not trivial to introduce constraints in the realized trajectories and to ensure convergence due to the nonlinearity of the system. This paper proposes a trajectory planning scheme based on Probabilistic Roadmaps (PRM) in order to achieve more robust visual servoing through the introduction of desired constraints at the task planning level such as visibility and occlusion avoidance constraints that ensure the object remains in the camera field of view (FOV). Off-line path planning is performed on a 5DOF robot arm to confirm the validity of the approach.

Range estimation using TDL neural networks and application to image-based visual servoing

In this paper, a new approach is proposed to estimate the depth of the target using active monocular stereo. The proposed method employs a tapped delay line (TDL) neural network to approximate the depth of the target. It is shown that the proposed method is less computationally expensive and functions well in case of occluded or unmatched features making it more robust than similar methods such as homography-based techniques. Experimental results validate the robustness and accuracy of the approach. The application of the proposed method to image-based visual servo (IBVS) of a 5 degrees of freedom manipulator is discussed.