Dynamic Alignment Control Using Depth Imagery for Automated Wheel Assembly

Abstract

This paper presents a novel method for dynamic alignment control using infrared light depth imagery to enable automated wheel loading operation for the trim and final automotive assembly line. A key requirement for automated wheel loading is to track the motion of the wheel hub and simultaneously identify the spatial positions and angular orientations of its alignment features in real-time on a moving vehicle body. This requirement is met in this work, where low-cost infrared depth-imaging devices like Microsoft Kinect™ and Asus Xtion™, vastly popular in the gaming industry, are used to track a moving wheel hub and recognise alignment features on both the wheel hub and the wheel in real time in a laboratory environment. Accurate control instructions are then computed to instruct the automation system to rotate the wheel to achieve precise alignment with the wheel hub and load the wheel at the right time. Experimental results demonstrate that the reproducibility error in alignment control satisfies the assembly tolerance of 2mm for the wheel loading operation, and thus the proposed method can be applied to automate wheel assembly on the trim and final automotive assembly line. The novelty of this work lies in its use of depth imaging for dynamic alignment control, which provides real-time spatial data in all 3 axes simultaneously as against the popularly reported RGB imaging techniques that are computationally more demanding, sensitive to ambient lighting and require the use of additional force sensors to obtain depth axis control data. This paper demonstrates the concept of a light-controlled factory where depth imaging using infrared light and depth image analysis is used to enable intelligent control in automation.

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

“…In this work, the automotive wheel loading operation is selected as a use case and DSS framework and tool is developed and tested. Prabhu et al 8 have proposed an automation solution in which depth imaging sensors are used to capture relevant data from the assembly shopfloor, such as the motion characteristics of the vehicle body moving on the conveyor line and the alignment features of both the wheel hub, on which the wheel is installed, and the wheel for successful loading. It was demonstrated in that work that the critical work done by human senses in a manual operation could be accomplished by low-cost sensors, thereby taking a big step towards automating the operation.…”

“…Another example is the use of depth imagery for dynamic alignment control for an automated wheel loading operation. 8 This article reports the use of Microsoft Kinect, 9 a consumer-grade, low-cost sensor to replace the more expensive RGB vision systems and force sensors. In all previous research attempts, the live data captured by vision systems, depth imaging and force sensors are used as an alternative to human senses in the automated operation to make ad-hoc, situation-specific decisions without the real-time flexibility and adaptation of human decision-making that caters for dynamic assembly adjustments.…”

Digitisation of a moving assembly operation using multiple depth imaging sensors

“…The operation targeted is that of wheel loading on the trim and final assembly line of automotive production with the vision to automate that operation in the future. One such automation of using a depth imaging sensor to capture live shopfloor data for dynamic alignment of components to be assembled on a moving line [13]. This paper reports an extension of that work.…”

“…Prabhu et al have previously proposed a depth sensorbased method to determine the misalignment between the wheel and the wheel hub mounted on a constantly moving vehicle body and to compute the optimum alignment manoeuver [13]. They investigated the use of a single depth imaging sensor to recognise alignment features on both the wheel and the wheel hub over a short stretch of 400 mm of a 2.5-m long typical wheel loading workstation.…”

“…The algorithms used to track the moving wheel hub and recognise the angular positions of the alignment features of both the moving wheel hub and the stationary wheel are based on the comparison of depth values of the pixels that belong to the object with those of the background as reported by Prabhu et al [13]. To track the moving wheel hub, the depth images provided by the far sensor are continuously monitored for depth values of pixels belonging to the wheel hub motion plane, represented by the far motion sensing zone, to be in the range of 800 and 900 mm, indicating the presence of the wheel hub.…”