A Robotic Bone Drilling Methodology Based on Position Measurements

Marcos Louredo, Inaki Dıaz and Jorge Juan Gil

Abstract— Over the past decades many studies have dealtwith the development of robotic tools to improve the processof bone drilling. The main difficulty of the operation resides inthe ability to detect bone layer transitions and/or protrusionsduring the procedure so that damage to surrounding tissue isminimized. The present paper set up a test bench in order tostudy some of the most relevant drilling methodologies in theliterature. The study illustrates some of the drawbacks, and itproposes a new drilling methodology that provides improvedresults.

Index Terms— Assisted Surgery, Bone Drilling

I. INTRODUCTION

The present work focuses on bone-machining surgical

procedures such as drilling, reaming and sawing, all very

common in different surgical interventions. Specifically, our

work focuses on bone-drilling procedures.

In any hospital around the world, bone drilling procedures

are performed many times per day, and in very different

surgical specialties such as orthopedic surgery, ear surgery,

maxillofacial surgery, neurosurgery, among many others.

During the drilling procedure, the surgeon uses a drill or

a similar device to make a hole through the bone. In many

cases this operation is extremely delicate and requires great

precision and accuracy in order to drill only the desired

depth. Even the slightest deviation in the drilling path can

damage the tissue surrounding the bone (veins, arteries, brain

tissue, spinal cord, etc.) causing irreversible damage to the

patient.

Currently, drilling processes are usually carried out by em-

ploying manual electric or pneumatic drilling tools (Fig. 1).

The operation mode of these tools is very simple and similar

to the drilling tools used at home to hang a picture. The

surgeon can control the rotation speed of the drill bit (with a

pedal, button, etc.) while he/she exerts certain force against

the bone to make the hole.

The main disadvantage of such drilling tools is that there

is no way to guess when the hole is done or the desired

depth is reached. Moreover, a breakthrough can push the

drill bit further along the drilling axis due to the inertia

of the drilling force. While this undesired effect may not

be very important when drilling a wall at home, it can be

of critical relevance when drilling a bone since surrounding

tissue can be seriously damaged. Currently, the only way of

efficiently stopping the drilling procedure at the desired depth

is the experience and intuition of the surgeon. Therefore,

The authors are with the Applied Mechanics Department, CEIT, PaseoManuel Lardizabal 15, E-20018 San Sebastian (Guipuzcoa), Spain (e-mails: {mlouredo,idiaz,jjgil}@ceit.es) and with TECNUN,University of Navarra, Paseo Manuel Lardizabal 13, E-20018 San Sebastian(Guipuzcoa), Spain.

Fig. 1. Bone drilling tool.

any means of assisting the surgeon during the operation can

decrease the potential for error or mishap.

Over the last decade many solutions have been proposed

to improve the current art of surgical drilling. A number

of solutions rely on image-based trajectory control, which

involves the surgeon using X-ray images from time to time

to “see” the penetration depth. This is the current approach

taken by surgeons when depth control is critical.

Developments in the drilling tool have included mecha-

tronic systems that control either the linear or the rotational

movements of the drill bit, or both movements simultane-

ously, such that the surgeon only has to place the system

at the correct position and orientation. These semi-automatic

and automatic solutions vary depending on the methodology

they follow to control the penetration depth into the bone:

(i) by using predefined penetration depth values [1], or (ii)

by using control algorithms that analyze the measurements

of different sensors coupled to the drill bit [2], [3], [4].

The present work focuses on the automatic drilling tools

group. Both the rotation and the linear movement of the

drill bit are automatic; that is, the surgeon places the drill

bit at the desired position and orientation and pushes the

start button. Afterward, the system carries out the drilling

procedure automatically and stops the drill bit either at the

layer transition or at bone breakthrough, according to the

surgeon’s requirements. An optimal control methodology

should be able to move the drill bit along its trajectory in

order to achieve a minimum level of protrusion of the drill

bit beyond the desired point.

This paper provides a review of mechatronic drilling

methodologies found in the literature. Moreover, it analyzes

some of the most relevant algorithms in a test bench specif-

The Fourth IEEE RAS/EMBS International Conferenceon Biomedical Robotics and BiomechatronicsRoma, Italy. June 24-27, 2012

978-1-4577-1198-5/12/$26.00 ©2012 IEEE 1155

ically designed for such a purpose. Finally, it proposes and

validates a new drilling methodology that improves previous

methods.

II. RELATED WORK

A typical bone structure is comprised of a dense outer

layer (the cortical bone), and a less dense inner portion (the

trabecular bone). Depending on the surgical procedure, the

bone drilling process can consist of either boring into the

two cortical walls (from one side of the bone to the other)

or only into one cortical wall (without necessarily passing

through the trabecular bone).

In general, control methods for detecting bone layer tran-

sitions while drilling are based on the penetration force and

cutting torque measured by sensors attached to the drilling

tool (i.e force/torque sensors, accelerometers, etc.). Fig. 2

shows an example of such signals during the bone drilling

process.

Drilling of the first cortical wall

Abrupt decrease inthe penetration force

Time (s)

Abrupt increase inthe cutting torque

Force

Torque

Fig. 2. Force and torque signals measured while drilling a bone with amechatronic tool at CEIT’s laboratories.

Note that both the force and torque signals present abrupt

variations at the initial and final drilling stages. Although the

shapes of the signals vary for different types of bones, the

abrupt variations are always present in layer transitions. In

fact, the control methods and systems previously proposed

in the literature differ in the way they try to detect these

variations. Most of them implement detection algorithms by

predefining threshold values for these variations, and when

these threshold values are reached, the system assumes that

the drill bit has arrived at a bone layer transition.

In 1995, Brett et al. [5] were the first authors to provide

a solution for an automatic drilling methodology. They

proposed a control strategy for the precise drilling of flexible

bone tissues during ear surgery. To detect the moment of

the drill bit’s complete breakthrough, the system identified

a persistent increase of the cutting torque simultaneous with

a persistent decrease of the penetration force. In subsequent

studies [6], [7], aspects of the tool design were examined.

At the same time, Allota et al. [8] devised a technique

for detecting breakthroughs for use with a mechatronic

tool designed for orthopedic surgery. They also proposed

a theoretical model to obtain the penetration force and

cutting torque parameters and to detect a breakthrough by

imposing an upper limit threshold of the first derivative of

the penetration force.

Ong and Bouazza-Marouf [9] devised a robust detection

method for drill bit breakthrough when drilling into long

bones. Their work looked into the fluctuation in the drilling

force profiles, drilling between successive samples and drill

bit rotational speeds. The method proposed by the authors,

based on a modified Kalman filter, was able to convert the

profiles of differences in drilling force between successive

samples and/or the drill bit rotational speed into easily

recognizable and more consistent profiles, allowing a robust

and repeatable detection of drill bit breakthrough.

In later work, Brett et al. [10], [11] described a robotic

system for microdrilling during a stapedotomy. Information

on the state of the drilling process was derived from feed

force and torque sensory data with respect to time and dis-

placement. The system was automatically able to determine

the unknowns of tissue thickness, hardness and flexibility.

Detection of the onset of breakthrough, which is key to

establishing thickness, was via the identification of features

in the multiple sensory data that characterize this condition.

Lee and Shih [12], [13] developed a robotic bone drilling

system for applications in orthopedic surgery. The proposed

robotic bone drilling system consisted of an inner loop fuzzy

controller for robot position control, and an outer loop PD

controller for feed unit force control. Moreover, breakthrough

detection was a function of thrust force threshold information

and trended in drill torque and feed rate.

In 2008, Coulson et al. [14] presented an autonomous

surgical robot system that was able to carry out the critical

process of penetrating the bone tissue of the wall of the

cochlea without penetrating the endosteal membrane located

immediately inside the cochlea.

Recently, Taylor et al. [15] presented a surgical robotic

device that is able to discriminate tissue interfaces and other

controlling parameters in the space in front of the drill tip.

A smart tool detects the area just in front of the tool tip and

is able to control the interaction with respect to the flexing

tissue in order to avoid penetration or to control the extent

of protrusion with respect to the position of the tissue. To

interpret the drilling conditions and the conditions leading

up to breakthrough at a tissue interface, a sensing scheme

that discriminates between the variety of conditions posed in

the drilling environment is used.

An alternative detection methodology based on wavelets

was presented by Colla and Allota [16]. They investigated

the application of a wavelet-based controller to a mechatronic

drill for orthopedic surgery. The penetration velocity of the

drill was generated on the basis of a wavelet analysis of the

thrust force signal.

Yet another approach found in the literature is based

on fuzzy logic and neuronal networks. A novel hand-held

drilling tool devoted to orthopedic surgery was presented in

[17]. The drilling tool used a fuzzy logic controller to control

the penetration velocity and identify the time of incipient

breakthrough.

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Kaburlasos [18], [19] reported on the successful applica-

tion of learning, classification and feature extraction tech-

niques to the stapedotomy surgical procedure. The authors

used force and torque data during drilling to estimate the

thickness of the stapes bone by learning a linear mapping of

force features to torque features. This learning was attained

by employing the two level fuzzy lattice (2L-FL) scheme

for supervised clustering.

III. EVALUATION OF PRIOR METHODOLOGIES

This section analyzes some of the most relevant drilling

methodologies in the previous section by testing them on a

specifically designed test bench.

A. Test Bench Set-Up

The test bench (Fig. 3) was designed to drive a rotating

drill bit in an axial direction so that both the linear movement

and the rotation of the drill bit are controlled and measurable

for automatic drilling procedures. Two main parts can be

differentiated: (i) the drill bit and motor set used to drive the

rotation of the tool, and (ii) the feed mechanism, responsible

for driving the drill bit and motor set in the axial direction.

Encoder

Motor Linear guide

Cable-driventransmission

Drill bit and motor set

Fig. 3. Components of the test bench.

The drill bit and motor set consists of a surgical inter-

changeable drill bit, attached to an electric DC motor (maxon

RE40 148877) that drives it at the desired speed. Both

elements are attached to the drilling guide, which provides

the axial movement to the set.

The feed mechanism drives the drill bit and motor set

in the axial drilling direction. Movement is actuated by an

electric DC motor (maxon RE40 148877) and a cable-driven

transmission [20]. The cable-driven transmission allows con-

trolled movement of a linear guide (igus R© DryLin R© Tk-

0.4-15-1,150), which holds the drill bit and motor set.

Additionally, there is an optical encoder (Quantum Devices

QD145-05, 5000 ppr) coupled to the motor to measure the

movement of the drill bit in the axial direction.

The last component is the body of the device. It fixes all

components together and incorporates an ATI Mini40 force

sensor to measure the penetrating force (Fig. 4). The different

elements have been manufactured by a Rapid Prototype

Object EDEN 3300 machine, and are made of Fullcure R© 720

material. Table I summarizes the main specifications of the

system.

Sensor support

Sensor

Encoder

Drill bit

Motor

Fig. 4. Components of the drilling tool.

TABLE I

MAIN SPECIFICATIONS OF THE TEST BENCH.

Parameter ValueFeed workspace (axial direction) 1018 mmFeed displacement resolution (axial direction) 15 μmMax. penetration force (continuous) 40 NMax. penetration force (peak) 160 NMax. cutting torque 184 mNmRotational speed 0 − 7500 rev/min

The control unit that drives the test bench consists of a

control box and a display. All the electronics are placed

in this module. It receives sensor measurements from the

drilling guide, analyzes them and controls the drilling pro-

cess. Specifically, for this prototype, a dSpace DS1104

control board is used, and control algorithms run at a fixed

sampling rate of 1 kHz.

B. Experiments

Drilling experiments were performed on cortical bones

along the middle section of cooked bovine femoral shafts.

The whole femur, which was stripped of all soft tissue, was

clamped rigidly onto a bone holder in order to ensure that

there was no system compliance. The input feed rate for the

axial movement was set to 12 mm/min, and the rotational

speed of the drill bit was 7500 rev/min. A 6 mm diameter

surgical drill bit was used for the experiments.

Notice that some limitations, which do not come into play

in real drilling processes, are taken into consideration: (i) the

bone is rigidly attached to a structure (rigid bone holder), (ii)

no refrigeration is applied, and (iii) cooked animal bones

are used. Although these considerations limit application

to a real environment, they still allow the basic working

principle of the detection algorithm to be validated and allow

a comparison with prior state of the art that also considered

similar testing conditions [8], [9], [12].

Fig. 5 shows a picture of the final test bench set-up. Three

representative drilling methodologies of the related work

were implemented on it, and results are described next. We

refer the reader to the references cited for each methodology

for further details about the algorithms applied.

Fig. 6 shows the data obtained when drilling with the

detection methodology presented by Ong and Bouazza-

Marouf [9]. The authors used the Force Difference between

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Fig. 5. Test bench set-up and bone holder.

Successive Samples (FDSS), filtered by a modified Kalman

estimator (K-FDSS), to detect bone layer transitions. This

detection occurs when the K-FDSS signal passes through

a certain threshold value (0.001 N in the experiment). The

Force signal plotted in the figure is the penetrating force

measured by the sensor in the axial direction.

Fig. 6. Drilling procedure on two cortical bone layers using the methodby Ong and Bouazza-Marouf [9]. Dots 1 and 3 represent the imminent drillbit breakthrough, while squares 2 and 4 show the detection points providedby the method. Triangles show false detection points.

It can be seen from the figure that the implementation of

this methodology for detecting drill bit breakthrough results

in late detection. Moreover, a sensitive threshold value (such

as the one applied in the experiment) generates many false

detection points before the drilling begins (t = 0 − 14 s),

while less sensitive values imply higher delays in the detec-

tion point.

Fig. 7 shows the data obtained when drilling with the

wavelet-based detection methodology presented by Colla and

Allota [16]. Using a tree-structured wavelet decomposition

stage, the authors obtained three useful output sequences: the

approximation coefficients at the first decomposition level

and two detail coefficients (d1 and d2). These latter two

sequences present a peak corresponding to each breakdown

in the original signal. These peaks can be easily detected by

a simple threshold comparator (γ2 and γ13).

By using a threshold value of γ13, protrusion at the first

Fig. 7. Drilling procedure of two cortical layers using the method by Collaand Allota [16]. Dots represent the imminent drill bit breakthrough, whilesquares show the detection points provided by the method.

cortical wall is detected on time, however, the detection for

the second cortical wall occurs when the breakthrough is

almost done. Again, tuning the threshold value improves

some precision, but it leads to other undesired effects such

as false detection points.

The last method analyzed at the test bench is the one

proposed by Kaburlasos et al. [18] (Fig. 8). The authors

used neural network theory and fuzzy logic to identify four

drilling stages (Sa, Sb, Sc, Sd). Transition from one stage to

another is held at a representative drilling point that allows

the procedure to be controlled: 1) bone drilling starts, 2)

drilling is stabilized (penetration is greater than half the

radius of the drill bit), 3) protrusion starts, and 4) complete

breakthrough occurs.

Fig. 8. Drilling procedure with one cortical layer using the method byKaburlasos et al. [18]. Red squares represent the transition points fromstage to stage, and the yellow dot shows the beginning of the protrusion.

The method performs well if the drilled bone has similar

thickness and homogeneity to the bones used to implement

the neural network. But again, the method lacks precision

when bone type changes or the threshold value is modified.

To sum up, the methods analyzed have some elements

in common: (i) the use of a velocity control approach for

the axial movement of the drill bit, (ii) the use of a force

sensor to measure penetration force and to feed the detection

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control algorithm, and (iii) the use of threshold values to

determine layer transitions. As a result, these methods have

similar limitations in detecting layer transitions just before

protrusion: (i) the signal of the force sensor is very noisy

for control purposes, and certain delay is introduced after

filtering this signal, (ii) the use of threshold values decreases

the effectiveness of the algorithms among different bone

types; moreover, depending on the sensitivity of the threshold

value it results in false detections with conservative values

and a lack of detection with wider margins.

IV. PROPOSED BONE DRILLING METHODOLOGY

The present work proposes a new control methodology for

detecting layer transitions to improve the point of detection

[21]. Our focus is on finding a method that allows fast

detection (just before protrusion), and whose effectiveness is

not conditioned by the sensitivity of using threshold values

(low false detection rate).

The proposed methodology uses a different approach to

control the linear movement along the penetration axis com-

pared to traditional velocity control. The diagram in Fig. 9

shows the axial movement control law applied to the linear

guide through the DC motor and the cable transmission. The

input to the system is a ramp-style position signal, with the

slope being equal to the desired translational drilling speed.

Then, a proportional controller is applied to the difference

between the reference position and the X position measured

by the optical encoder attached to the motor.

Fig. 9. Control diagram for the axial displacement along the guide.

Before reaching the bone, the system moves smoothly

following the speed imposed by the slope of the ramp. Once

drilling begins, the position error greatly increases due to the

resistance encountered when the drilling movement comes up

against the bone’s stiffness, and also because the controller

saturates the actuation to a proper motor torque level. Note

that once the motor is saturated, the control scheme works

as a constant force input to the system.

Simultaneously, a detection algorithm is running at the

control unit. Its duty is to predict the exact moment just

prior to the protrusion in order to stop the axial movement

of the drill. To that end, the detection algorithm monitors the

same position error signal fed to the controller in Fig. 9.

Fig. 10 shows the position reference signal, the X position

measured by the encoder, and the error signal between both

of them when drilling a bone with this control scheme. At

time t = 0 s, the drill comes into contact with the bone

and starts the process. Due to the stiffness of the bone and

the saturation of the motor, the X position lags behind the

position reference, and the error signal increases as drilling

time goes on. This situation remains unaltered until the bone

protrusion is about to happen (t ≈ 125 s).

Fig. 10. Position and error signals measured during the drilling process(at t = 0 s bone drilling starts, at t ≈ 125 s bone protrusion starts).

Just before protrusion the remaining width of the bone

layer becomes very tiny and bone stiffness is considerably

reduced. At that moment, and according to the implemented

control movement law, the system will respond by acceler-

ating while trying to follow the reference position, thereby

minimizing the position error. The implemented detection

method targets this sudden acceleration to discriminate a

bone layer transition.

In terms of the error signal, this variation is seen as a

change in the sign of its slope, from positive to negative.

This condition is implemented in the detection algorithm,

imposing the detention of the axial movement of the drill as

a response to a sign change in the slope of the error signal.

A very important feature of the proposed algorithm is that

at detection time a very thin bone layer still remains, which

the surgeon can easily break manually. This condition allows

safer drilling in critical places since the system stops the drill

just before any surrounding tissue that is different to bone is

reached. In contrast, the prior state of the art, mostly detect

layer transitions once bone protrusion occurs.

The detection algorithm successfully detects the bone

protrusion in time by employing only the measurement of

the optical encoder that is attached to the feeding motor.

This measurement, unlike a force sensor or an accelerom-

eter, has the advantage of having very low noise. Another

condition for the successful implementation of the algorithm

is the cable transmission used to feed the carriage. Its

high reversibility allows the control scheme in Fig. 9 to

be implemented, and it also allows the system to accelerate

before protrusion (Fig. 10).

20 consecutive drilling experiments were performed using

the proposed method at the test bench, and a comparison with

a detection methodology based on the force measure was also

carried out. All of them performed very similar results. The

proposed new method detected protrusion on cortical walls

earlier than force-based methods. Fig. 11 shows data from

one of the experiments (the system was commanded to detect

the protrusion but to go on drilling in order to get the data).

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Time (s)

X(m

m)

err

X(m

m)

X(m

m)

ref

Forc

e(N

)

Fig. 11. Drilling experiment using our proposed method. Yellow dotsrepresent the detection points found by force-signal-based methodologies,and red dots the detection points obtained by the proposed methodology.

Fig. 12 shows the holes made with the new detection

methodology (left) and with the force signal-based algo-

rithms (right) when commanding the system to stop at bone

protrusion. It can be seen how the new method is able to

stop the procedure just before the breakthrough whilst other

methods are not so accurate.

Fig. 12. Holes made with force-signal-based (left) and with the proposedmethodology (right).

V. CONCLUSIONS

This work reviewed previous related work on mechatronic

bone drilling systems and methodologies. Moreover, a test

bench was used to show some common drawbacks present

in such methods. Afterward, we described and tested a new

bone drilling methodology that improves the previous art.

The proposed drilling method avoids the use of force

sensors or accelerometers, and the detection algorithm relies

uniquely on the measure of the linear movement of the drill

bit. By using the position signal of the drill bit, better results

can be obtained than with previous systems. The position

signal is less affected by electromagnetic noise, and the

control methodology used provides earlier detection of the

layer transitions and breakthroughs.Future work will focus on an approach close to the

real conditions found in an operating theatre, taking into

account the limitations considered in this paper, and on the

development of a hand-held mechatronic drilling tool that

can take advantage of the drilling methodology proposed.

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