force controlled robotic assembly - aminer · flexible robotic assembly for powertrain applications...

Flexible Robotic Assembly for Powertrain ApplicationsProject Review 04/28/00

1

Force Controlled Robotic Assembly

David P. GravelSenior Technical Specialist

Ford Motor Company Advanced Manufacturing Technology Development Center

Robot Force Control Partnersn Kawasaki Heavy Industriesn FANUC Roboticsn Robotics Research Corp.n Case Western Reserve University, Cleveland,OHn National Center for Manufacturing Sciencesn Sandia National Labn ComauPICOn MicroDexterity Systemsn Marquette University in Milwaukee, WIn National Center for Standards and Technology

(NIST)


2

Inspection1%

Assembly3%

Arc Welding13%

Painting18%

Spot Welding31%

MaterialRemoving

4%

Material Handling27%

Dispensing3%

Current Robotic Applications

Source: Robotic Industries Association

Current Robot Paradigm

n Assembly with position-servo robot control cannot occur where the assembly tolerance is less than the positional uncertainty.

n Position controlled robots do not have the compliance required for accommodating alignment errors in the inherent in the assembly process.

n Even devices such as a RCC device are not sufficient to allow conventional robots to tackle complex assembly tasks.


3

Business Drivers

n Thousands of components in powertrain and vehicle assembly can now only be manually assembled

n Many heavy parts can cause Repetitive Stress Injuries, and require relief workers

n Quality Issues can arise from tired workers assembling heavy parts for long periods of time

n Cycle time improvements can boost productivity and capacity

n Worker safety in improved if the forces robots can impart to their environment is limited

Example


4

Two Human Attributes Needed

Humans have natural attributes used to successfully manipulate delicate objects with purposeful intent and excellently integrated tactile and visual senses:

1. Force-control: Provides the robot with a “soft touch” to react to contact forces for assembly, insertion, or for meshing of gears

2. Learning: Humans have an innate learning capacity not found yet in industrial machines

Project Goals

n Develop a new robot control paradigm that allows robots to control the forces they impart to an environment

n Develop human-like assembly strategies to direct robot forces and moments in an intelligent fashion

n Implement force controlled robots on ergonomic problem applications that can have a quality impact


5

History of Force Controln Passive Compliant Device research from

Marquette Universityn Trellis / Kawasaki force control with CWRUn Robotic Research Corp. application developmentn NIST ATP for Flexible Robotic Assembly for

Powertrain Applications ($3.5M)n Kawasaki Force Control Joint Development

Projectn FANUC Robotics Force Control Development

Project

How Force Control Works

New method for robotic control, not a position servo but a motor torque control scheme that limits and directs contact forces during assembly.


6

Force Control Test Plan

n Setup a graduated difficulty applications where each phase builds on skills acquired in previous phases.n 4R70W Clutch Hubn AX4N Intermediate Clutch Hubn AX4N Triple Clutch

Initial Force Control Application

n 4R70W forward clutch hubn Locate the top clutch ring surface and control

impact force and vertical insertion forcen Impose a search pattern that threads through

the clutch platesn Repeat until done


7

Second Applicationn AX4N Intermediate Clutch Hub - More

difficult since plates have less internal friction, making relative motion between the hub and the clutch plate difficult

n Need high speed accel/decel with force controln More “agility” required for this task, a high

performance version of the forward clutch algorithm.

Hardest Application

n AX4N Triple Clutchn Delicate sun gear to insert into a

planetary gear setn Internal spline gear mates

approx. ¼” after initial sun gear insertion

n Heavy part, ~10kg


8

RRC Test Results

n Robotics Research robot was able to do all three tasks but lacked a good robot controller system and has not established industrial reliability

KHI Test Resultsn Excellent FC hub insertion cycle timesn Superb reliability for AX4N triple clutch,

total cycles times close to the 24 sec. line rate


9

FANUC Test Resultsn Excellent results

with all force control efforts

n FANUC is installing a intermediate clutch application at the Van Dyke Transmission Plant

n Excellent AX4N triple clutch cycle times.

FRAPA NIST ATP

n Flexible Robotic Assembly for Powertrain Applications (FRAPA)

n $3.5M NIST ATP

n Paradex I results from Case Western Reserve University

n Dramaticlly lower and more repeatable forces


10

Machine Learning

n Humans are able to learn complex assembly tasks relatively quickly. Machines, however, are notoriously difficult to “teach”.

n Work at CWRU on machine learning has focused on four areas. n How do people perceive edges and boundaries when mating two

parts together. n Strategies people invoke under various force feedback conditions.

n Create a model based system that uses signal feedback to direct robot motion.

n Implement the knowledge gained in steps 1-3 into a neural net robot controller.

CWRU Experiments

n Moments exist near a hole

n Experiments using a 6 DOF joystick people deprived of moment info have dramatically more trouble performing a peg in hole assembly


11

Edge and Moment Data Presentation

n Even heavily filtered, or impoverished data, presented to humans was sufficient for quick convergence of the peg into the hole.

Importance of Training

n Paths taken by trained and untrained operators to accomplish the peg in hole task with filtered moment and edge data.


12

Neural Net Results

n Near the location of the hole the neural network was able to pick up signals out of the noise of dragging the peg on the surface. The results are plotted to the right.

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Actual Sensor Moment Data

n Moment information as a slightly tilted 9.8 mm peg is dragged across 10mm hole in various locations


13

Genetic Algorithms

n A self learning system that self optimizes system parametersn Removes burden of

training from plant floor personnel

n Adapts to changing conditions (stack up)

n Less plant floor expertise needed to maintain system

PUBLICATIONS ORDER FORM

Date: ID#: internal use only ITEM QTY. COST TOTAL: SHIPPING INFORMATION

Name

Title

Company

Address

City State Zip

Telephone Fax

Email Address

PAYMENT INFORMATION

o Check Enclosed o Invoice me (publications will not be shipped until payment is received) o Credit Card

q American Express q Visa q MasterCard

Account #: Expiration Date:

Name on Card:

Please complete this form and mail to: Automation Technologies Council 900 Victors Way, P.O. Box 3724

Ann Arbor, MI 48106

For faster service, fax this form to: (734) 994-3338

Phone: (734) 994-6088.

force controlled robotic assembly - aminer · flexible robotic assembly for powertrain applications...

Documents