Synthesis of Valid Camera Poses for the Inspection of Triangular Facets in a 3D Mesh.

Automation of visual quality inspection tasks in manufacturing with machine vision is beginning to be the de facto standard for quality inspection as manufacturers realize that machines produce more reliable, consistent and repeatable analyses much quicker than a human operator ever could. These methods generally rely on the installation of cameras to inspect and capture images of parts; however, there is yet to be a method proposed for the deployment of cameras which can rigorously quantify and certify the performance of the system when inspecting a given part. Furthermore, current methods in the field yield unrealizable exact solutions, making them impractical or impossible to actually install in a factory setting. This work proposes a set-based method of synthesizing continuous pose intervals for the deployment of cameras that certifiably satisfy constraint-based performance criteria within the continuous interval.

Architecture for a Mobile Robotic Camera Positioning System for Photogrammetric Data Acquisition in Hydroelectric Tunnels.

The structural condition of hydroelectric tunnels is important to the overall performance, safety, and longevity of generating stations. Significant effort is required to inspect, monitor, and maintain these tunnels. Photogrammetry is an effective method of collecting highly accurate visual and spatial data. However, it also presents the complex challenge of positioning a camera at thousands of difficult-to-reach locations throughout the large and varying-diameter tunnels. A semi-automated robotic camera positioning system was developed to enhance the collection of images within hydroelectric tunnels for photogrammetric inspections. A continuous spiral image network was developed to optimize the collection speed within the bounds of photography and capture-in-motion constraints. The positioning system and image network optimization reduce the time and effort required while providing the ability to adapt to different and varying tunnel diameters. To demonstrate, over 28,000 images were captured at a ground sampling distance of 0.4 mm in the 822 m long concrete-lined section of the Grand Falls Generating Station intake tunnel.

Practical Applications of a Set-Based Camera Deployment Methodology.

This work establishes a complete methodology for solving continuous sets of camera deployment solutions for automated machine vision inspection systems in industrial manufacturing facilities. The methods presented herein generate constraints that realistically model cameras and their associated intrinsic parameters and use set-based solving methods to evaluate these constraints over a 3D mesh model of a real part. This results in a complete and certifiable set of all valid camera poses describing all possible inspection poses for a given camera/part pair, as well as how much of the part's surface is inspectable from any pose in the set. These methods are tested and validated experimentally using real cameras and precise 3D tracking equipment and are shown to accurately align with real imaging results according to the hardware they are modelling for a given inspection deployment. In addition, their ability to generate full inspection solution sets is demonstrated on several realistic geometries using realistic factory settings, and they are shown to generate tangible, deployable inspection solutions, which can be readily integrated into real factory settings.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator.

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.

Well-conditioned model predictive control.

Model-based predictive control is an advanced control strategy that uses a move suppression factor or constrained optimization methods for achieving satisfactory closed-loop dynamic responses of complex systems. While these approaches are suitable for many processes, they are formulated on the selection of certain parameters that are ambiguous and also computationally demanding which makes them less suited for tight control of fast processes. In this paper, a new dynamic matrix control (DMC) algorithm is proposed that reduces inherent ill-conditioning by allowing the process prediction time step to exceed the control time step. The main feature, that stands in contrast with current DMC approaches, is that the original open-loop data are used to evaluate a "shifting factor" m in the controller matrix where m replaces the move suppression coefficient. The new control algorithm is practically demonstrated on a fast reacting process with better control being realized in comparison with DMC using move suppression. The algorithm also gives improved closed-loop responses for control simulations on a multivariable nonlinear process having variable dead-time, and on other models found in the literature. The shifting factor m is generic and can be effectively applied for any control horizon.