Miguel Saez Ph.D.

Researcher

Manufacturing Systems Research Lab

ROBOTIC ASSEMBLY: 

CHALLENGES AND OPPORTUNITIES
INTRODUCTION

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What is Robotic Assembly?

Academia:

• Fixtureless Assembly: Use of robots to place parts in the proper position 

without the need of a dedicated fixture (Hoska, 1988) 

• Vision-guided positioning : Interaction of multiple robots to position and hold 

parts to perform a task (Bone & Capson, 2003) (Novakovic et al. 2017) 

• Coordinated motion: Control joint position and torque of multiple robots 

holding a single parts (Gueaieb et al., 2007) (Uchiyama et al, 1987) 

Industry:

• Clutch insertion using vision and force sensing (Gravel et al., 2008) 

• Robot-to-Robot handoff at GM body shop plants

• “Open-loop” positioning of sheet metal parts using multiple robot arms
KEY TECHNOLOGY FEATURES

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• Perception: Use of sensors to understand the work environment and process requirements:

o Exteroceptive:

- Digital image: One or multiple cameras to identify target features

- Laser: Point or line readings to create a point cloud or line 

o Enteroreceptive:

- Load cells: Measure forces and/or torques in robot arm or end effector

- Motor torque: Motor current feedback

• Robot coordination

o Synchronous motions: Share positional and target information to execute separate tasks

o Asynchronous motions: Share positional and target information to execute the same task

• Robot accuracy: Identify the actual and target pose of the robot arm
ASSEMBLY OPERATIONS

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• Body Assembly: • Vehicle Assembly

• Powertrain assembly • Battery Assembly
ASSEMBLY OPERATIONS

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• Body Assembly: Sheet metal component with 

target features for positioning and alignments

• Battery Assembly: Metal and plastic 

components with locating features

2D and 3D perception systems: Place parts in reference to a target feature (e.g.: holes) or surface matching

media.gm.com
PERCEPTION IN MANUFACTURING

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2D:

- Digital image: Detection of objects positioned in a work place 

(X,Y,R)

- Laser line: Detect object position in a single work plane (X,Z)

- 2D+Laser: Detection of objects positioned in two or more layers 

(X,Y,Z,R)

3D:

- Stereo vision: Detect object position and orientation based 

binocular vision and image processing

- Infrared: Use one image to map a part in 3D

- Laser point cloud: Structured and sparse laser reflection 

measurements to identify objects or features in robot workspace 

Force sensing:

Identify forces and torque in 3DOF
VISION FOR PATH PLANNING

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• Offset from taught points

Relationship between datum and 

key feature is taught and vision 

is used to define an offset

• Direct pose programming

Relationship between datum and 

key feature programmed based 

on the product requirements

A

τTP A

τGAP

Part1/Robot1

Assy position

P2’

S2’

Part2/Robot2
CHALLENGES: IMAGE ACQUISITION AND PROCESSING

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• Field of View: Narrow FOV requires multiple robot movements which increases 

operation cycle time 

• Accuracy: Aggregated error of optical instruments, image processing and feature 

extraction algorithm and robot movements

• Access: Robot mounted sensors require might face access and constraints to get 

proper image
CHALLENGES: IMAGE ACQUISITION AND PROCESSING

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• Part properties: Poor contrast or reflection. 

Images are required to have good contrast 

between the work environment, background, 

target feature 

• Environmental conditions: Changes in ambient 

lighting can produce inconsistent performance. 
CHALLENGES: INTEGRATION AND ROBOT ACCURACY

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• Integration: Additional hardware and software 

requirements that increate system latency

• Robot accuracy: Inherent errors in robot 

mastering and calibration along with thermal 

expansion/contraction of robot arm affect 

accuracy of vision system and commanded 

positions
OPPORTUNITIES: PRODUCTIVITY 

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- Reduce robot movement: Larger field of view 

would reduce the need to collect multiple 

images to command an absolute or relative 

position to one or multiple robots

- Faster image acquisition: Solutions robust to 

changes in part reflection, glare, or ambient 

light will prevent the system of having to take 

multiple images of a single feature

media.gm.com
OPPORTUNITIES: QUALITY

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- Accurate imaging: Use solutions often used for metrology for accurate robot 

guidance

- Accurate positioning: Reduce the errors in robot positioning for both image 

acquisition and assembly

- Post-process inspection: Use vision system for robot guidance and inspection to 

enable “Built-in-quality” where no bad parts leave the cell
RESEARCH AND DEVELOPMENT REQUIREMENTS

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• Extended Field of View: Increase the working distance to the observable 

world from a single or multiple camera locations 

• Increase accuracy: Reduce or estimate error or vision system and robot arm

• Robust to different parts and work environment: Use of different 

wavelengths for perception

• Fast image acquisition: reduce time to acquire image and identify features
STANDARDIZATION REQUIREMENTS

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• Define a common testing setup

• How to measure accuracy?

• How to differentiate between sensor, algorithm, and robot error?
QUESTIONS?

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THANK YOU

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