A. Sánchez García

A machine vision system for defect characterization on transparent parts with non-plane surfaces

This contribution presents a machine vision system capable of revealing, detecting and characterizing defects on non-plane transparent surfaces. Because in this kind of surface, transparent and opaque defects can be found, special lighting conditions are required. Therefore, the cornerstone of this machine vision is the innovative lighting system developed. Thanks to this, the defect segmentation is straightforward and with a very low computational burden, allowing real-time inspection. To aid in the conception of the imaging conditions, the lighting system is completely described and also compared with other commercial lighting systems. In addition, for the defect segmentation, a new adaptive threshold selection algorithm is proposed. Finally, the system performance is assessed by conducting a series of tests using a commercial model of headlamp lens.This contribution presents a machine vision system capable of revealing, detecting and characterizing defects on non-plane transparent surfaces. Because in this kind of surface, transparent and opaque defects can be found, special lighting conditions are required. Therefore, the cornerstone of this machine vision is the innovative lighting system developed. Thanks to this, the defect segmentation is straightforward and with a very low computational burden, allowing real-time inspection. To aid in the conception of the imaging conditions, the lighting system is completely described and also compared with other commercial lighting systems. In addition, for the defect segmentation, a new adaptive threshold selection algorithm is proposed. Finally, the system performance is assessed by conducting a series of tests using a commercial model of headlamp lens.

A sensor planning system for automated headlamp lens inspection

Usually, the automated systems for quality control based on computer vision have been centered on the design of algorithms for detecting different types of defects. Nevertheless, the issues related to planning suitable sensor poses for the inspection task have received less attention. In addition, the applications where a vision sensor can only sample a portion of a part from a single viewpoint, the sensor planning problem becomes critically important. This is the case of the automated inspection of vehicle headlamp lens, that due to its geometry and dimensions, requires multiple sensor poses to observe the whole part. Moreover, the customer requirements that define the maximum defect size should also be taken into account in the inspection process. This paper presents a vision sensor planning system applied to the quality control of headlamp lenses. The system uses the lens CAD, a vision sensor model and the customer requirements, included through a fuzzy approach, to achieve an optimal set of viewpoints. To compute the number and distribution of the viewpoints, a genetic algorithm is used. Experimental results demonstrate the effectiveness of the sensor planning system on commercial lenses.

A dynamic lighting system for automated visual inspection of headlamp lenses

This paper presents a novel lighting system to reveal surface defects on transparent parts with non-planar surface. Thanks to this system, the defect segmentation is straightforward and fast to compute and so a real-time inspection of these parts is possible. To aid in the conception of the imaging conditions, the lighting system is completely described and also a comparative study with others commercial lighting systems is done. Then, a new adaptive threshold selection algorithm for defect segmentation is proposed. Finally, the system performance is assessed using a commercial model of headlamp lens.

Model predictive position/force control of an anthropomorphic robotic arm

When a robotic manipulator executes a task in restricted and unknown environments, it is necessary to implement a position/force control in free space and constrained space. In classic controllers, position/force control may be carried out by means a controller switching, once the contact point has been reached. In this paper, a new position/force predictive controller for an anthropomorphic robotic arm is proposed. This work proposes an unique control law for controlling both variables (position or force). In a model predictive framework, where several goals have to be reached, it is known that cost function includes several parameters (weights) whose modification allows to prioritize goals to achieve. This fact has been taken into account to set up the controller. The proposed control law is validated by experiments involving a 7-dof anthropomorphic robotic arm executing a trajectory tracking task in a restricted environment. As experimental results show, it is possible to control position and force by adjusting controller weights.

Visual Predictive Control of Robot Manipulators Using a 3D ToF Camera

This paper presents a visual servo strategy for an anthropomorphic manipulator with eye-to-hand configuration. As novelty, a time-of-flight (ToF) camera, together with a model-based predictive control (MPC), have been used to execute the manipulator tasks in an unstructured environment. The ToF camera provides 3-D data of a target object and the MPC cost function achieves convergence of robot motion. With this configuration, there is no requirement to estimate a 3-D feature point position from a 2-D image and the camera model. A procedure for the target segmentation on the depth data, which is based on a previous training, is also presented. Experimental results, obtained in a robotic platform, show that the proposed approach is well suited for visual servoing purposes.

High-accuracy automatic system to assemble vehicle headlamps

In this work, the conception, design and development of a prototype for the automatic assembly and dimensional correction of vehicle headlamps is presented. Firstly, we analyze different measurement systems that can be used to determine the position of the elements that compounds the headlamp; secondly, we show the main conclusions obtained from the application of different technologies of actuators used to control the position of the components during the assembly. Finally, it is described the experimental results which validate the proposed system. They were carried out in both the lab and the assembly line in the factory. The new prototype solve the problem of assembling vehicle headlamps obtaining a final product with minimum dimensional errors.

An automatic procedure to identify the areas of interest for the automated inspection of headlamp lenses

Many machine vision applications require manipulation of the acquired image to prepare it for processing. One type of pre-processing operation consists in identifying the areas of interest—or regions of interest (ROIs)—for the image analysis. But in occasions, the image areas to be analyzed may vary from one part to another. This is the case of the automated inspection of headlamp lenses that, due to the part positioning mechanism, the image areas to be processed do not always stand in the same image site. This paper presents an automatic procedure to adjust the region of interest in the images acquired for the inspection of a commercial model of headlamp lens. This proposal is included in a machine vision prototype for the automated inspection of such lenses. The results herein presented are achieved with this prototype.

An automatic procedure to code the inspection guideline for vehicle headlamp lenses

Nowadays the quality control of headlamp lenses for vehicles is visually made by expert operators, known as inspectors. To establish a standard in this manual process, the inspectors must follow the inspection guideline. This guideline is a document, defined by experts in the inspection of lenses and validated by the customer, that presents the criterions of acceptance and rejection for the flaws that may appear on the lens surface. This paper proposes a methodology to include the inspection guideline in an automated headlamp lens inspection system based on computer vision. As the way in which the guideline includes the knowledge of an expert in the inspection of lenses is inherently qualitative and vague, a Fuzzy Rule-Based System is developed to model this information.

Expert system based controller for the high-accuracy automatic assembly of vehicle headlamps

A vehicle headlamp is a high-sophisticated device basically composed of three elements: housing, lighting system and lens. Nowadays, they are assembled with no dimensional control, obtaining a final product with high physical deviations. Since the car manufacturer is requiring more and more precision, the number of faulty headlamps is increasing, and thus the cost to the headlamp producer. To solve this problem, a new automatic assembly machine was built. It integrated different technologies such as sensor systems, linear actuators and computational capacities. This work presents the conception, design and development of a fuzzy control algorithm for an automatic machine capable of correcting the dimensional tolerances of vehicle headlamps during its assembly process. Particularly, this paper describes the problem of modeling and control an industrial system, with a high complex dynamics and unknown model, by a rule-based model constructed from measured data. To validate the proposed control system, different experimental tests were carried out in both the lab and the assembly line in the factory. The obtained results show how the prototype, together with the proposed fuzzy software, solve the problem of assembling vehicle headlamps obtaining a final product with minimum dimensional errors.

An adaptable vision system for the automatic inspection of surface defects in automotive headlamp lenses

As a great part of the industrial inspection problems require specific, expensive, and highly complex solutions, there is the need to build flexible systems capable to be easily adapted to different models of a part. In this context, this paper proposes the use of a component-based methodology for designing and building flexible inspection systems for automotive headlamp lenses. Through computer vision, and taking into account the different elements that can be utilized in an industrial environment, we propose the use of a component based architecture for easily designing and building industrial inspection systems for this kind of parts. Within this architecture, each software, or hardware element, is encapsulated in a component with a predefined input/ouput layout and, later, it can be used considering the task inspection requirements. The main advantages of the proposed working philosophy are its flexibility, that the resulting machine vision can be easily adapted to any kind of headlamp lens, and that it is fully open to any new component (lighting system, process algorithm, industrial conveyor, etc.). This proposition has been successfully validated building an industrial prototype that was tested in both, the lab, and in a production line.