We develop a new gradient-based optimization strategy for robotic inspection planning. The algorithm starts from an initial measurement path and evolves this path to decrease path length while maintaining sensor coverage. The approach is powerful enough to handle inspection tasks of complex objects under articulated manipulator constraints. We show the effectiveness of the algorithm in two complex inspection tasks.

Currently, galvanometric scanning systems (like the one used in a scanning laser Doppler vibrometer) rely on a planar calibration procedure between a two-dimensional (2D) camera and the laser galvanometric scanning system to automatically aim a laser beam at a particular point on an object. In the case of nonplanar or moving objects, this calibration is not sufficiently accurate anymore. In this work, a three-dimensional (3D) calibration procedure that uses a 3D range sensor is proposed. The 3D calibration is valid for all types of objects and retains its accuracy when objects are moved between subsequent measurement campaigns. The proposed 3D calibration uses a Non-Perspective-n-Point (NPnP) problem solution. The 3D range sensor is used to calculate the position of the object under test relative to the laser galvanometric system. With this extrinsic calibration, the laser galvanometric scanning system can automatically aim a laser beam to this object. In experiments, the mean accuracy of aiming the laser beam on an object is below 10 mm for 95% of the measurements. This achieved accuracy is mainly determined by the accuracy and resolution of the 3D range sensor. The new calibration method is significantly better than the original 2D calibration method, which in our setup achieves errors below 68 mm for 95% of the measurements.

In-line inspection of advanced components, remains a challenging task in industry. A methodology is discussed which uses numerical simulations to automatically determine the best set of experimental parameters to inspect the structure on defects using active thermography. The inspection is performed using an industrial conveyor belt or a robotic arm optimized using a numerical model of the defected test sample. During the path planning, the directional emissivity is considered for the complex surface and an optimal experimental setup is found. The results show that in-line quality evaluation of complex shaped structures is improved with a minimal amount of inspection time.

The traditional literature on camera network design focuses on constructing automated algorithms. These require problem-specific input from experts in order to produce their output. The nature of the required input is highly unintuitive, leading to an impractical workflow for human operators. In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner. From real world practical examples we conclude that the camera networks designed using this interface are highly competitive with, or sometimes even superior to, those generated by automated algorithms, but the associated workflow is more intuitive and simple. The competitiveness of the human-generated camera networks is remarkable because the structure of the optimization problem is a well known combinatorial NP-hard problem. These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems, given an intuitive visualization of the problem.

In-line inspection of advanced components remains a challenging task in industry. The authors will describe an automated methodology that uses numerical simulations to automatically determine the best set of experimental parameters to inspect the structure on defects using active thermography. The inspection is performed using a robotic arm and advanced path-planning tools to determine the optimal positions of the measurement points and excitation points. During the path planning, the directional emissivity is considered for the complex surface, and a minimization of the amount of measurement points is performed. The numerical simulation optimization used a genetic algorithm and spline regression model to optimize the heat power, robot speed, camera frame rate, and excitation timing to fulfill the automatic inspection.