Object Slippage Prevention using Different Control Strategies

Pavel Dzitac

, Abdul Md Mazid

Guy Littlefair

and Ashwin Polishetty

School of Engineering, Deakin University, Geelong, Australia

Department of Engineering, Macquarie University, Sydney, Australia

Keywords: Robotic Grasping, Slippage Sensing, Tangential Force Sensing, Slippage Detection, Slippage Prevention,

Incipient Slippage, Grasp Force Estimation, Grasp Force Control.

Abstract: This paper presents experimental results obtained using a friction-based slippage and tangential force

sensing device that has been developed for the purpose of reliable object slippage prevention during robotic

manipulation. The experimental results obtained demonstrate that the developed slippage sensing strategy is

rugged and reliable even in its current “rough prototype” state of development. This work has the potential

to yield a low cost and highly customisable slippage and tangential force sensing device for a variety of

robotic object grasping and manipulation applications. It is envisaged that the work presented here will be

beneficial to researchers in the area of object slippage prevention.

1 INTRODUCTION

Reliable grasping and manipulation incorporates

control decisions that perform better than the basic

“grasp and hold tight” strategies. Adequate grasp

force control is essential to reliable and safe

manipulation; it improves robot’s chances to

manipulate objects autonomously close to optimum

conditions on the first attempt.

Slippage takes place in two stages: incipient

slippage and bulk or gross slippage (Tremblay and

Cutkosky, 1993). It is characterised by a mechanical

behaviour known as “stick-slip” that results in

vibration and is present during relative motion

between two surfaces in contact (Bowden and

Tabor, 1986). In order to prevent slippage it is

necessary to predict what researchers call pre-slip

(Petchartee and Monkman, 2007) or incipient slip

(Bowden and Tabor, 1986), (Mingrino et. al, 1994,

Canepa et al., 1994, Pelossof et al., 2004), the tell-

tale sign that slippage is about to occur. Predictive

measures such as incipient slip detection have been

used by researchers to control slippage during object

manipulation (Canepa et al., 1994, Pelossof et al.,

2004, Dubey and Crowder, 2006, Watanabe and

Obinata, 2007).

The stick-slip vibration frequency and amplitude

are dependent on many factors and are not a constant

behaviour during slippage under all conditions;

vibration frequency and amplitude vary with object

materials, different surface conditions and speed

(Bowden and Tabor, 1986, Abdo et al., 2009). Stick-

slip can be reduced under certain conditions by

applying a particular range of vibration frequencies

at specific amplitudes to the system that produces

the stick-slip induced vibration (Abdo et al., 2009).

It may also be challenging to distinguish stick-slip

vibration from background noise. Therefore, it could

be concluded that stick-slip is not an ideal source for

generic incipient slippage detection, but it is a

mechanical behaviour by which most slippage

events can be detected using vibration detection

techniques.

Researchers have developed many tactile sensors

intended for grasp force and slippage control

(Russel, 1990, Choi et al., 2005, Rossiter and Mukai,

2005, Dahiya et al., 2010). Industrial robotics

applications require sensors that can handle

significant loads; a load of 50 N is towards the low

end on the load scale. Therefore load bearing

capacity and the ability to maintain sensitivity at

maximum load are important criteria that determine

the suitability of a sensing technology for industrial

robotics applications.

This paper presents experimental results obtained

with the latest friction-based slippage and tangential

force sensing prototype, in conjunction with various

slippage detection and prevention strategies.

The working principle of the friction-based

slippage sensing device is described in (Dzitac and

Dzitac, P., Mazid, A., Littlefair, G. and Polishetty, A.

Object Slippage Prevention using Different Control Strategies.

DOI: 10.5220/0006003505590566

In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2016) - Volume 1, pages 559-566

ISBN: 978-989-758-198-4

559

Mazid, 2012), and is briefly summarised here to

facilitate understanding.

2 FRICTION-BASED SLIPPAGE

SENSING

The slippage sensing device relies on friction

between two sets of friction surfaces: one at the

roller-shaft interface, and the other at the roller-

object interface (Figure 1).

Figure 1: Friction-based parallel jaw gripper concept with

rollers on support shaft.

When the applied grasp force to the object is

insufficient, the roller slips on its support shaft, but

still rolls on the surface of the manipulated object.

This allows the static coefficient of friction to be

maintained at the roller-object interface. Therefore,

this design incorporates the following benefits.

• Slippage starts at the roller-shaft interface well

before slippage at the gripper-object interface,

which facilitates object slippage prevention;

• The static and dynamic coefficients of friction at

the roller-shaft interface are known in advance,

which allows adequate grasp force application

when object mass is known.

In Figure 1 the friction force between the roller and

its support shaft is given by





= µ



(1)

Where, µ



is the coefficient of friction at the roller-

shaft interface and F is the grasp force.

The friction force between the roller and the

object prevents roller slippage on the object surface

and is given by





= µ



(2)

Where, µ



is the coefficient of friction at the roller-

object interface.

The net torque at the roller-shaft interface is given





=





−





,



≤

(3)

Where, 



represents the tangential force during

grasping. Slippage at the shaft-roller interface will

begin when the net torque 





When both friction rollers are holding the object,

the weight of the object will be shared between the

two rollers such that each roller will support





.

In general, a smaller net torque 



will require a

smaller grasp force to prevent slippage at the roller-

shaft interface. This simple design concept allows

slippage between the roller and the manipulated

object to be prevented reliably.

3 TORQUE (TANGENTIAL

FORCE) SENSOR DESIGN

The prototype parallel gripper incorporates two

sensing elements, one for tangential force sensing

and one for slippage sensing, each fitted to a parallel

gripper jaw. The torque sensing element (Figure 2)

is fitted with a full Wheatstone bridge torque sensor

that senses the tangential force developed on the

shaft by the weight of the object held in the gripper.

The roller rotates on the shaft, which in turn is

supported on roller bearings at its two ends to

minimise friction and allow the full torque that is

developed on the roller by the object to be sensed by

the torque pin that is bolted to the support.

Figure 2: Cross-section through the torque sensing

element assembly.

The TML QFCT-2 strain gauges used for the

torque pin’s full Wheatstone bridge have the

following parameters:

• Gage factor GF = 2.12 +/-1 %

• Grid length = 2 mm

• Allowable strain = 3 %

The 3 % strain allows a strain gauge elongation of

2mm∗0.03=0.06mm=60μm

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560

Due to the very small values, strain is also

expressed in “microstrain” (strain * 10

). In this case

the 3 % strain limit of the strain gauge can be

expressed as

0.03∗10



=30,000microstrain

The torque pin (Figure 3) was designed to

maximise its torsional strain when a 300 gram load

is held in the gripper. It is made of 6061-T6

aluminium, has a diameter of 4 mm and a length of

20 mm. The design intent was to make the pin

diameter as small as possible to maximise its strain

under the given load (and therefore the torque

resolution) but still allow the strain gauges to be

fitted on its circumference without overlap.

Figure 3: Torque pin assembly fitted with a full

Wheatstone bridge.

The maximum torque strain for a round shaft is

given as





∗E∗R





(

1+υ

)

(4)

Where,



- maximum shaft torsional strain

T - Applied torque to shaft

E – Elastic modulus of shaft material

R – Shaft (pin) outer radius

υ – Poison ratio

The design parameters for the torque pin are as

follows:

T=3N*0.013m=0.039Nm

E=68.9*109N/mm



R=0.002m;

υ=0.33

Substituting the parameters in equation 4 gives



∗.

∗.∗



∗.



∗

(

.

)

∗





The 120 microstrain range is well within the

allowable 30,000 microstrain that can be applied to

the strain gauge without causing it damage.

The theoretical full bridge output, not accounting

for losses is estimated as





GF∗



(5)

Where,

-bridgeoutput(mV)

-bridgeexcitation(V)

GF–thegaugefactorofthestraingauge

Substituting in equation 5 gives a bridge output of





2.12∗120∗10



=0.127

At full strain and an excitation voltage of 5 V DC

the theoretical bridge output is





@5V=0.127

5V=0.635mV

The bridge output is expected to vary from the

theoretical value due to variations in excitation

voltage, variations in strain gauge extension wire

resistance and because the strain gauge is not

matched to aluminium for temperature

compensation. These variations from the theoretical

output will be relatively small (within 5 %),

therefore acceptable for measuring the tangential

force developed by the object on the robot gripper.

4 SLIPPAGE SENSOR DESIGN

The second jaw of the slippage sensing device was

fitted with an incremental rotary encoder designed to

sense roller rotation when the object slips in the

gripper. In this design the steel roller support shaft is

press-fitted into the aluminium roller such that they

rotate together. The shaft can rotate in the steel plain

bearings (Figure 4). The encoder is attached to the

support using a screw, and its shaft is coupled to the

roller support shaft via a silicone rubber disc. This

allows the encoder to sense relative motion between

the roller support shaft and the steel plain bearings,

therefore allowing slippage to be detected by the

same principle as described in section 2 above.

Figure 4: Cross-section through the slippage sensing

element assembly.

Object Slippage Prevention using Different Control Strategies

561

5 EXPERIMENTAL SETUP

The major components of the experimental setup are

shown in Figure 5 to Figure 9. The schematic

representation of the experimental setup is shown in

Figure 10.

Figure 5: Friction-based tangential force sensing device

fitted with torque sensor.

Figure 6: Nemicon 18S-500-2MC-2-15-00E incremental

encoder, used as slippage sensor.

Friction-based slippage sensing roller assembly

is fitted with the Nemicon incremental encoder. The

rubber O-rings are used to increase the coefficient of

friction and provide mechanical compliance between

the gripper and the grasped object. The O-rings also

increase resistance to object rotation in the gripper

due to the discrete contact points formed with the

object (Dzitac et al., 2014).

Figure 7: Encoder fitted to the roller assembly.

The Windows 7 operating system, running on an

Asus notebook and executing custom vision

software, processes the image received from the

Basler vision camera to determine object shape, size,

location and orientation. Object data is sent to the

Moacon controller via an RS232 communication

link. The Moacon C programmable controller (with

digital inputs, relay outputs, analog inputs,

quadrature encoder inputs, pulse width modulation

PWM outputs and four position controllers for the

XYZ and R stepper motor drivers) runs custom

software that performs signal processing, motion

planning, instinctive control, motion control and

robot control at a basic level.

Figure 8: Moacon controller.

XYZ+R Cartesian robot (“R” stands for gripper

rotation around Z axis) fitted with a modified

Schunk RH701 electric gripper to allow higher grasp

forces to be applied. This allows a 300 g object to be

held safely without overstressing the gripper.

Figure 9: Cartesian robot fitted with gripper, slippage

sensing element and tangential force sensing element.

Other experimental setup components include the:

• Full bridge amplifier model TWL-9R92 for

torque sensor strain gauges;

• G251X digital stepper motor driver for XYZ and

R axes;

• MD10C brush motor PWM drive for grasp force

control.

Figure 10: Schematic representation of the experimental

setup using the final friction-based gripper design – the

vision camera is used to find the object.

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562

Three sets of experiments were conducted to assess

different slippage control strategies:

• Experiment 1 – Simple slippage control;

• Experiment 2 – Sensor fusion based slippage

control;

• Experiment 3 – Proportional based slippage

control.

A 300 gram aluminium object was used for the

experiment (Figure 11). The robot was instructed to

execute the following tasks autonomously:

• Grasp the object with approximately 10 % of the

available grasp force;

• While lifting the object, adjust the grasp force to

stop slippage;

• When object lifting is completed accelerate the

gripper downward then upward and

monitor/control slippage;

• If slippage becomes large and potentially

uncontrollable (i.e. there is not enough grasp

force available to control slippage), stop robot

motion and revert to manual robot control by a

human to prevent damage;

• Else move gripper down and release the object.

Constraints and assumptions:

• Object shapes were limited to rectangular and

cylindrical;

• Object mass was limited to approximately 300

grams to avoid overstressing the gripper;

• The object was assumed to be capable of

sustaining 100 % grasp force without damage;

• The roller was assumed to always rotate when object

slippage occurred. The term “object slippage” is used

here to mean slippage at the roller-shaft interface not

at the roller-object interface;

Initial grasp force creates sufficient contact with

the object to cause the roller to rotate when object

lifting begins.

Figure 11: Object grasping, lifting and releasing sequence

using the prototype slippage and tangential force sensing

device.

5.1 Experiment 1 Results – Simple

Slippage Control

The simple slippage control strategy requires the

robot to apply an initial grasp force of 10 % and then

increment the grasp force by 5 % when slippage is

detected. Grasp force is estimated from the

percentage of the pulse width modulated (PWM)

current applied to the gripper motor. Figure 12

shows the grasp force, tangential force and slippage

sensing encoder pulses recorded during the grasp

and manipulation cycle using this control strategy.

Figure 12: Initial grasp force set at 10 % of range. Grasp

force increments are in steps of 10 %. The oscilloscope

horizontal time scale is 2 s/division. Slip during

acceleration is about 0.25 mm (i.e. roller rotates 1.44 ° on

its Ø20 mm support shaft).

Unpredicted slippages were recorded in each of

the ten object manipulation attempts. However, all

unpredicted slippages were successfully resolved by

increasing the grasp force and stopping the slippage,

partially due to the medium manipulation velocity of

approximately 1 m/s.

The results for the simple slippage control

strategy are summarised in Table 1.

Table 1: Results for simple slippage control strategy.

Total object

lifts

Unpredicted

slippage

Slippage stopped

successfully

10 10 10

5.2 Experiment 2 Results – Sensor

Fusion Slippage Control

This experiment was conducted using the same

hardware, methodology and constraints as in

Experiment 1 above, except that the “sensor fusion”

slippage control strategy (Dzitac et al., 1014) was used

here. The diagram in Figure 13 illustrates the grasp

force control algorithm used; sensor fusion determines

whether slippage is “potential”, “controllable” or

Object Slippage Prevention using Different Control Strategies

563

“potentially uncontrollable”. The recorded grasping

and manipulation data is shown in Fig. 14.

Figure 13: Grasp force control state diagram including

sensor fusion based slippage detection.

Figure 14: Unpredicted slippage occurred during lifting

but was stopped by increasing grasp force.

Table 2 summarises the experimental results. One

unpredicted slippage occurred during the initial lift of

the first attempt. Two unpredicted slippages also

occurred due to a “creep” effect noticed during

experimentation. Because the encoder tracks the roller

displacement over time, small slippages accumulate

until it is sufficient to trigger a slippage event, which

is defined as > 4 encoder counts. This behaves like a

slippage integrator; however when a slippage event is

triggered as a result of slippage creep, the grasp force

increase stops slippage, but the sensor fusion strategy

in its current form cannot predict the slippage because

the changes in tangential force are too small to trigger

a controller reaction.

Table 2: Results for sensor fusion slippage control

strategy.

Total

object

lifts

Unpredicte

d slippage

Slippage due

to “creep”

Slippage

prevented

successfully

10 1 2 7

5.3 Experiment 3

Results – Proportional Slippage

Control

This experiment was conducted using the same

hardware, methodology and constraints as in

Experiments 1 and 2 above, except that the

performance of a modified version of the slippage

control strategy was compared to the performance of

the sensor fusion grasp force control strategy.

This strategy uses the ratio of the static to the

dynamic coefficient of friction









as a scaling factor

for grasp force correction that depends on the values

of the two coefficients of friction at the roller-shaft

interface where slippage occurs first. The reasoning

behind this strategy is that the additional grasp force

required to stop “object slippage” (i.e. slippage at

the roller-shaft interface) will be proportional to the









ratio at the roller-shaft interface and can be

illustrated as follows.

Let μ





in Figure 15 be the grasp force at which

slippage stops during object lifting and μ





the

static friction force when slippage stops. If the grasp

force is then reduced to the point of incipient

slippage (i.e. the natural grasp force safety margin



=0), the reduced static friction becomes μ





and results in the following relationship





=μ





(6)

Re-arranging gives













(7)

Therefore, at the instant when slippage starts F



(i.e.

the grasp force) has to be increased by at least the









ratio to stop slippage successfully.

Figure 15: Grasp force reduction to the point of roller

slippage on shaft.

The proposed grasp force control strategy in this

case (when no slippage takes place) can be

expressed as follows

F=F



1+



−F





,F



(8)

Where,

• F is the total grasp force applied to the object;

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564

• F



is the grasp force recorded at the point where

slippage stopped while lifting the object; it is

the static component of the total grasp force;

• F



is the tangential force recorded at the point

where slippage stopped while lifting the object;

• F



is the current tangential force developed by

the grasped object on the roller;

•



−F



is the absolute value of the tangential

force change;

• F

















is the dynamic component of the

total grasp force; its value changes in

proportion to changes in F



relative to F



When slippage signals are detected, the static

component F



is assigned a new value that has been

incremented by a factor









, which allocates a grasp

force safety margin proportional to the friction

characteristics at the roller-shaft interface. This

increases the static components of the grasp force

safety margin to prevent future slippage and can be

expressed as follows





=F











(9)

The grasp force control in equation 8 can also be

expressed in terms of PWM duty as follows

PWM=P



1+



−F





,F



(10)

Where,

• PWMisthepulsewidthmodulationduty

appliedtothegrippermotor;itcontrolsthe

graspforceofthegripper;

• PWM



isthepulsewidthmodulationrecorded

atthepointwhereslippagestoppedwhile

liftingtheobject;

• PWM

















isthedynamiccomponentof

thetotalgraspforce;itsvaluechangesin

proportiontochangesinF



relativetoF



.

When slippage is detected, PWM



will be assigned a

new value that has been increased by the









factor as

follows

PWM





=PWM











(11)

Equation 11 is useful because most controllers are

equipped with PWM outputs that can be used to

control the grasp force of a robot gripper.

To improve the initial grasp-lift time and the

overall reaction to slippage, a rate-based grasp force

safety margin control strategy was used as a

replacement for equation 11. When slippage is

detected PWM



is increased based on the rate of

received encoder pulses (i.e. rate of slippage).

PWM





=PWM



+ks

(12)

Where,

• k is a gain constant and s is the slippage rate

within a control loop cycle (e.g. number of

encoder pulses in 100 ms).

This rate-based slippage control is possible because

the rate of slippage is readily available from the rate

of the encoder pulses generated during slippage.

The recorded grasping and manipulation data is

shown in Figure 16.

Figure 16: Slip-rate based control during initial lifting and

“proportional” control after initial lifting.

Table 3 summarises the experimental results for

the rate-based grasping and manipulation

experiment.

Table 3: Results for proportional slippage control strategy.

Total

object

lifts

Unpredicte

d slippage

Slippage due

to “creep”

Slippage

prevented

successfully

10 2 2 6

6 CONCLUSION AND FUTURE

WORK

The addition of torque sensing to one gripper roller

made it possible to use preventive slippage control

strategies such as sensor fusion and proportional

control, which is not possible when using a slippage

sensor alone.

The slippage prevention strategies based on

sensor fusion and proportional control used in these

experiments perform better that basic slippage

control strategy that increases grasp force after

slippage is detected. However, unpredicted slippage

still occurred due to the “slippage creep” effect

inherent in the current design. An unpredicted

Object Slippage Prevention using Different Control Strategies

565

slippage is considered to be a slippage event to

which the control strategy did not react before the

actual slippage was detected.

The current tangential force sensing prototype is

affected by hysteresis due to stray friction in the

mechanical assembly. Future work could be done to

reduce or eliminate stray friction.

A derivative term could be added to the

proportional term of the proportional controller to

facilitate reaction to tangential force rate of change,

and as a result improve slippage prevention.

The developed slippage detection and control

strategy can sense slippage and tangential force in

one axis only. Further work can be done to add

slippage sensing in other axes.

The slippage control strategy presented here is

not a generic solution for slippage control in robotic

object manipulation, but is rugged, reliable,

repeatable and highly customizable.

REFERENCES

Tremblay M. R., Cutkosky M. R., 1993. “Estimating

Friction Using Incipient Slip Sensing During a

Manipulation Task,” In Proceedings of IEEE

International Conference on Robotics and

Automation, Atlanta (GA).

Bowden F. P., Tabor D., 1986. The Friction and

Lubrication of Solids, UK: Oxford Science Press.

Petchartee S., Monkman G., 2007. “Pre-slip detection

based Tactile Sensing,” In Proceedings of

International Conference on Sensors, Sensor

Networks and Information, Toulouse.

Mingrino A., Bucci A., Magni R., Dario P., 1994.

“Slippage Control in Hand Prostheses by sensing

Grasping Forces and Sliding Motion,” In Proceedings

of the IEEE/RSJ International Conference on

Intelligent Robots and Systems.

Canepa G., Campanella M., Rossi D., 1994. “Slip

Detection by a Tactile Neural Network,” In

Proceedings of IEEE/RSJ Internationl Conference on

Intelligent Robots and Systems.

Pelossof R., Miller A., Allen P., Jebara T., 2004. “An

SVM Learning Approach to Robotic Grasping,” In

Proceedings of International Conference on Robotics

and Automation.

Dubey V. N., Crowder R. M., 2006. “A dynamic tactile

sensor on photoelastic effect,” Sensors and Actuators,

no. 128, pp. 217-224.

Watanabe N., Obinata G., 2007. “Grip Force Control

Based on the Degree of Slippage Using Optical Tactile

Sensor,” In International Symposium on Micro-

NanoMechatronics and Human Science.

Abdo J., Tahat M., Abouelsoud A. A., 2009. “The effect

of excitation frequencies on stick-slip amplitude,” In

Proceedings of International Conference on Integrity,

Reliability and Failure, Porto (Portugal).

Russel R. A., 1990. Robot Tactile Sensing, New York:

Prentice Hall.

Choi B., Cho H. Kang R., S., 2005. “Development of

tactile sensor for detecting contact force and slip,” In

Proceedings of IEEE/RSJ International Conference on

Intelligent Robots and Systems, Edmonton.

Rossiter J., Mukai T., 2005. “A Novel Tactile Sensor

Using a Matrix of LEDs Operating in Both

Photoemitter and Photodetector Modes,” In

Proceedings of 2005 IEEE Conference on Sensor.

Dahiya R. S., Metta G., Valle M., Sandini G., 2010.

“Tactile Sensing - From Humans to Humanoids,”

IEEE Transactions on Robotics, vol. 26, no. 1.

Dzitac P., Mazid A. M., 2012. “A method to control grip

force and slippage for robotic object grasping and

manipulation”, In MED 2012 Proceedings of the

Control and Automation Mediterranean conference,

Barcelona, Spain. pp. 116-121.

Dzitac P., Mazid A. M., Littlefair G., Polishetty A., 2014.

“The Effective Radius and Resistance to Slippage”, In

Proceedings of the 11th International Conference on

Informatics in Control, Automation and Robotics

(ICINCO 2014), Vienna, Austria.

Dzitac P., Mazid A. M., Littlefair G., Polishetty A., 2014.

“Robotic Grasping and Manipulation Controller

Framework - Architecture Redevelopment”, In

Proceedings of the 11th International Conference on

Informatics in Control, Automation and Robotics

(ICINCO 2014), Vienna, Austria.

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