Flexible robotic assembly in dynamic environments

Shi, Jane; Menassa, Roland

doi:10.1145/2377576.2377626

Cited by 9 publications

(12 citation statements)

References 10 publications

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“…Although sometimes considered primitives for force-based control (Marvel and Falco 2012), surface and contour matching are also integral for assembly (Shi and Menassa 2010). Surface and contour matching refer to the processes of aligning and pressing components along flat or curved surfaces, respectively.…”

Section: Assembly Types and Search Strategiesmentioning

confidence: 99%

Multi-Robot Assembly Strategies and Metrics

2018

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We present a survey of multi-robot assembly applications and methods and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.

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Section: Assembly Types and Search Strategiesmentioning

confidence: 99%

Multi-Robot Assembly Strategies and Metrics

2018

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“…Misalignment between the wheel and the wheel hub is also checked by the visual servoing system, and transformation is applied to correct it. Shi has reported a preliminary analysis of dynamic conveyor motion and presented the typical motion characteristics of industrial conveyors such as speed, acceleration and multi-axis deviations in motion [8]. Based on that study, Shi and Menassa have proposed a method in which a coarse vision camera tracks the general motion characteristics of the moving vehicle body with lower accuracy and a fine vision camera to track the deviations in vehicle body motion just before loading is performed [9].…”

Section: Related Workmentioning

confidence: 99%

Digitisation of a moving assembly operation using multiple depth imaging sensors

Prabhu

Song

Thrower

et al. 2015

Int J Adv Manuf Technol

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Several manufacturing operations continue to be manual even in today's highly automated industry because the complexity of such operations makes them heavily reliant on human skills, intellect and experience. This work aims to aid the automation of one such operation, the wheel loading operation on the trim and final moving assembly line in automotive production. It proposes a new method that uses multiple low-cost depth imaging sensors, commonly used in gaming, to acquire and digitise key shopfloor data associated with the operation, such as motion characteristics of the vehicle body on the moving conveyor line and the angular positions of alignment features of the parts to be assembled, in order to inform an intelligent automation solution. Experiments are conducted to test the performance of the proposed method across various assembly conditions, and the results are validated against an industry standard method using laser tracking. Some disadvantages of the method are discussed, and suggestions for improvements are suggested. The proposed method has the potential to be adopted to enable the automation of a wide range of moving assembly operations in multiple sectors of the manufacturing industry.

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“…Alignment is achieved by computing the transformation required to rotate the tool, holding the wheel, to match the wheel hub orientation recognised by the vision system. Shi and Menassa have proposed a coarse vision system to track the macro motion of the moving vehicle body, a fine vision system to track the small deviations in vehicle body motion and an end-of-arm tool with a vision camera to locate the wheel hub studs for alignment [1]. Lange et al have proposed coarse and fine sensing systems and a compliant force-torque sensor in the end-effector to control the loading step to compensate for small-scale oscillations of the wheel hub [3].…”

Section: Related Workmentioning

confidence: 99%

“…While body construction and paint shops are automated, the trim and final assembly line, where the vehicle gets its instrumentation, seats, doors, trims, and wheels among other things, is the least automated [1,2]. This is because it is not possible to use pre-programmed industrial robots to assemble parts on a constantly moving vehicle body.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Alignment Control Using Depth Imagery for Automated Wheel Assembly

et al. 2014

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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.

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Flexible robotic assembly in dynamic environments

Cited by 9 publications

References 10 publications

Multi-Robot Assembly Strategies and Metrics

Multi-Robot Assembly Strategies and Metrics

Digitisation of a moving assembly operation using multiple depth imaging sensors

Dynamic Alignment Control Using Depth Imagery for Automated Wheel Assembly

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