AUTOMATIC CALIBRATION OF A MOTION CAPTURE SYSTEM

BASED ON INERTIAL SENSORS FOR TELE-MANIPULATION

org Hoffmann

Institut f

ur Informatik II, University of Bonn, Germany

Bernd Br

uggemann

Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE), Wachtberg, Germany

orn Kr

uger

Institut f

ur Informatik II, University of Bonn, Germany

Keywords:

Telerobotics and teleoperation, Motion capturing, Automatic calibration, Inertial sensors.

Abstract:

We introduce an intuitive way of controlling a manipulator. This control should fulﬁll several constraints like

low room consuming and operable in a short time. With this constraints in mind we chose an inertial sensor

based motion capturing system. Such systems have to be adapted to the user. We present a method to adapt

the system by a fast calibration while it is been used. The calibration does not require speciﬁed given motions.

The soundness of the system is shown in synthetic and real experiments.

1 INTRODUCTION

Interactive manipulation of their environment is an es-

sential task for robots. To provide a good tool for ma-

nipulation in most cases robots are equipped with a

robot arm. Controlling such a manipulator is a chal-

lenging task, especially if it should be remotely con-

trolled. The operator usually has an impression about

the environment by video only. Based on this few in-

formation he has to perform complex motions with

a manipulator. Controlling each joint separately de-

mands long previous training and high concentration.

The operator has to plan a sequence of motions for

each joint in advance. The difﬁculty of these opera-

tions is raised by the fact that it may be necessary to

re-adjust the joints during the motions.

As part of danger defence and disaster response

exploring a potential dangerous area without exposing

persons becomes more and more important. On one

hand, threats by terrorist groups can cause great dam-

age with relatively small effort. On the other hand,

disasters in industrial areas may release dangerous

and not easily detectable chemicals (TICs). If such an

emergency occurs, information about what has hap-

pened and how the situation is developing is crucial

for an effective response. Here robots can help to ac-

quire desired information without exposing personnel

to unknown risks. Such robots can be equipped with

several sensors to identify different threats. But often

it is just as important to manipulate the environment.

This may occur e.g. if a suitcase has to be opened, or

a sample has to be taken for analysis.

Nowadays most robots, which are meant to be

used within a disaster area are at least partly remote

controlled. So the operator has no direct line of sight

to the robot and the area the robot is working in. Be-

ing reliant on only few sensors (like a camera) com-

plex manipulations have to be executed. Often such

tasks are time critical and far away from any available

infrastructure. Therefore the needed control method

for the robot arm has to fulﬁll several constraints: In-

tuitively usable, can be employed quickly, space sav-

ing and easy to transport, and pose only small restric-

tions to the mobility of the user

In this paper we present a method to steer the ma-

nipulator directly by the operator’s movements with-

out the need of an exoskeleton. We use ﬁve iner-

tial sensors to capture the motions of the operator.

The most important part in our system is the auto-

calibration which makes an exact sensor placement

121

Hoffmann J., Brüggemann B. and Krüger B. (2010).

AUTOMATIC CALIBRATION OF A MOTION CAPTURE SYSTEM BASED ON INERTIAL SENSORS FOR TELE-MANIPULATION.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 121-128

DOI: 10.5220/0002880301210128

 SciTePress

unnecessary. In fact we only need to know roughly

where the sensors are positioned.

The remainder of the paper is organized as fol-

lows: In section 2 related works are presented and dif-

ferent ways of remotely controlled manipulators are

shown. In section 3 the problem and its constraints

are described while section 4 shows the preprocess-

ing of the sensor signals. The automatic calibration

for our inertial sensor based motion capturing system

is presented in section 5, followed by the robot arm

control mechanism in section 6. Section 7 presents

results of the calibration as well as its effect on the di-

rect manipulation controls. The paper closes with our

conclusions.

2 RELATED WORK

2.1 Robot Arm Control

Industrial robots are the prime example of the mar-

ket’s demand of manipulators. But also in mobile

robotics the control of manipulators is essential. Here,

in contrast to the industrial robots, the movement is

not predeﬁned and has to be adapted to the situation

and the task. So, in the course of time several dif-

ferent methods for controlling a manipulator were de-

veloped. Roughly those methods can be divided into

remotely controlled and autonomous movements.

Remotely controlled drives have the advantages

to fall back on the decision of the operator. Al-

though there is no need of autonomous decisions the

system has to provide the operator with full control

and information needed for the motion task. Within

the remotely controlled manipulators, methods can be

distinguished by their input devices, e.g. joysticks

(some mentioned in (Laycock and Day, 2003)). From

standard joysticks to those with force feedback, the

boundaries to master-slave control mechanisms are

ﬂuid.

The idea to couple a control mechanism directly

to the manipulator is rather old (e.g. (Goertz, 1954)).

Such master-slave approaches have some advantages

like being very accurate and the operator is always

aware of the motions the manipulator will perform.

Additionally, master-slave controls are able to pro-

vide the operator with haptic feedback. So there can

be more resistance within the master device if the ma-

nipulator will be near to an obstacle or it is possible

to feel the structure of a surface (e.g. (Tachi et al.,

1990; Yokokohji and Yoshikawa, 1992)).

In-between master-slave devices and joysticks,

there are exoskeletons. Such devices are worn by the

operator. Exoskeletons are available for just a limb

(e.g. an arm) or as whole body device (Bergamasco

et al., 1994). Its advantages are a very precise recon-

struction of the human motion and, compared to the

master-slave approach, less space is needed. But due

to the stiff construction, those exoskeletons prevent

the operator from performing movements in a natural

way.

Motion capturing is another approach to control

a manipulator by reconstructing human movements.

(e.g. (Miller et al., 2004; Pollard et al., 2002)). Ap-

proaches range from camera based motion capturing

(with active or passive markers) to motion capturing

with inertial sensors.

2.2 Motion Capturing with Inertial

Sensors

Many techniques for capturing human motions have

been developed in the last decades. Most of these

techniques suffer from several disadvantages: they are

not portable, need complex calibrations, or have dis-

turbing exoskeletons. A good overview over the most

important techniques is given in (Welch and Foxlin,

2002).

In the last years inertial sensor systems have be-

come more popular for many applications. They are

used for action recognition in games (Slyper and Hod-

gins, 2008), and for detection of typical or similar pat-

terns in medical applications (Sabatini et al., 2005;

Chang, 2006). A lot of work has been done on par-

tial motion capturing, where only special parts of the

body, as upper limbs for example, are considered. In

this area applications like home rehabilitation sys-

tems (Zhou et al., 2006; Zhou and Hu, 2007; Tao

et al., 2007) or robot controllers (Miller et al., 2004)

have been developed. For tracking applications sev-

eral systems based on fusion of inertial sensors and a

variety of other systems have been introduced (Foxlin,

2005). For full body motion capturing a portabel

system based on inertial sensors combined with an

acoustic system was designed (Vlasic et al., 2007).

Since Nintendo introduced the Wiimote in 2006 a lot

of applications were developed (Schou and Gardner,

2007; Lee, 2008; Shiratori and Hodgins, 2008), while

low cost sensors are available for mass market appli-

cations.

All these applications have to face similar techni-

cal difﬁculties. The data of inertial sensors are noisy

and may contain a drifting. This problem is tack-

led using some basic techniques in all applications.

Kalman ﬁlters are widely used for an optimal estima-

tion based on data of multiple sensors (Vlasic et al.,

2007; Zhou and Hu, 2007; Tao et al., 2007; Shira-

tori and Hodgins, 2008), especially when different

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Figure 1: Positioning of the ﬁve inertial sensors on the right

arm. Each joint is assigned to one sensor. The sensor on the

shoulder is used as ﬁx point.

types of sensors are combined. A Monte Carlo op-

timization can be used as basis for the estimation of

positions and orientations (Zhou et al., 2006). For

recognition of typical patterns in acceleration data

classical signal processing techniques, like phase de-

tection (Sabatini et al., 2005) or frequence analysis

by Haar wavelet transformations (Slyper and Hod-

gins, 2008) have been employed. To estimate the di-

rection of gravity several heuristics have been devel-

oped (Miller et al., 2004; Foxlin, 2005).

3 PROBLEM DESCRIPTION

To directly control the robot manipulator, the move-

ment of the whole human arm has to be reconstructed.

Therefore, we used four inertial sensors. One for each

part of the arm: Upper arm, lower arm, hand, and ﬁn-

gers. A ﬁfth sensor is needed to be positioned on the

shoulder as a ﬁx point (see Figure 1). The sensors pro-

vide their orientation but no information about their

position in space. If the setup of the human arm and

each sensor positioning on that arm is known, then it

is possible to compute the arm conﬁguration:

Let G

be the missing position for the i-th joint, q

the orientation measured by sensor i and s

the length

of the segment i (from joint i to i + 1). R

(v) is the

rotation of vector v around q

. Then the desired posi-

tions of each joint are:

= [0,0,0]

(1)

= G

i−1

+ R

([s

,0,0]

) (2)

Those equations needs the sensors to be oriented

in direction of the bone it is connected to. Unfortu-

nately the positioning of the sensors in reality is not

as accurate as it is needed. So each sensor has a bias

to the desired positioning. Figure 2 shows such a sit-

uation. Assuming the positioning is accurate, the ori-

entations of the sensors imply a bended arm. When

taken the faulty positioning α and β into account it can

be recognized that the original arm conﬁguration cor-

responds to a stretched arm. If the sensors are placed

without calibration with calibration

Figure 2: Imprecise positioning of sensors will cause false

reconstruction of movements. Assuming the sensors are

placed carefully the orientation implies a bended arm (left).

When knowing that the sensors are placed faulty by an angle

α and β the arm has to be reconstructed as streched (right).

carefully on the human arm, the result will be alright

but not very precise. But for a trustworthy and accu-

rate result it is important to know the difference be-

tween the bone orientation and its corresponding sen-

sor.

When the system is calibrated, the reconstructed

motions of the operator can be used to control the ma-

nipulator. As the used manipulator (and many robot

arms currently available) has not the same morpholo-

gies as the human arm, it is not possible to map the

reconstructed joint angles directly to the robot. So

another mapping has to be found. We used a simple

mapping by directly using the position and orienta-

tion of the operator’s ﬁngers. These informations are

easy to compute with the calibrated arm model. As

the ﬁnger’s position is related to the shoulder, it can

be used to provide a target for the manipulator’s tool

center point (TCP). This means that there will be no

direct correlation between the pose of the human arm

and the manipulator but a correlation between the po-

sition of the ﬁngers and the TCP.

4 SIGNAL PREPROCESSING

We use two different types of sensor data provided

by the inertial sensor system. First, we get orientation

data which are used to perform forward kinematics on

our arm model. Second, we obtain acceleration data

which are used for the calibration. While the orienta-

tion data is reliable over long time periods the accel-

eration data is more noisy and is afﬂicted by drifting.

In this section we show how we proceed with these

artifacts in our sensor data.

4.1 Removing Noise

We have to remove noise on both sensor signals: The

orientation data and the acceleration data. Both data

types have a high frequency noise. To reduce this

noise, we used a standard binomial low pass ﬁlter. As

the velocity signal is a function in the position space,

a ﬁlter with a window w = 20 data points is chosen.

AUTOMATIC CALIBRATION OF A MOTION CAPTURE SYSTEM BASED ON INERTIAL SENSORS FOR

TELE-MANIPULATION

123

0 100 200 300 400 500 600 700 800

−1

x [m/s]

0 100 200 300 400 500 600 700 800

−1

y [m/s]

0 100 200 300 400 500 600 700 800

−1

z [m/s]

Figure 3: Velocity data for the three axis. Without zero

velocity updates the data show a clear drift (red), with zero

velocity updates the drift is visibly reduced (blue).

Since the orientations are represented by quaternions,

we employ a smoothing ﬁlter to the orientation data

like it is described by (Lee and Shin, 2002) in order

to achieve reliable data.

4.2 Removing Drifting

The acceleration measurements include the gravita-

tion. These so-called local accelerations have to be

converted to global acceleration, i.e. accelerations

without gravitation. When using the sensor orienta-

tion to remove acceleration caused by gravity, small

errors in the orientation accumulate to large drifts in

the rotational acceleration.

We can efﬁciently remove drifting by apply-

ing zero velocity updates (ZUPT) introduced by

(Grejner-Brzezinska et al., 2001). Regardless whether

we know the exact positions and orientations of our

sensors: If the orientations do not change neither will

the positions. We computed the velocity of each joint

of our arm model. If these velocities are slower than a

threshold (we use 0.05

) we presume that the arm

is not moving, i.e. its accelerations are zero. The

measured non zero accelerations at these zero veloc-

ity points describe how the data have drifted. We use

these information to correct the acceleration between

two zero velocity points by changing the accelerations

linearly. Some results of this method are given in Fig-

ure 3. Without zero velocity updates the data show a

clear drift away from the zero line, with zero velocity

updates the drift is visibly reduced. It shows that the

sensor drifting can be removed clearly. Note that this

technique is an ofﬂine strategy. However, this is no

drawback, since we need the acceleration data only

for the calibration of the sensors. For reconstruction

we only use the orientation data, which do not have

any drifting at all.

5 SENSOR CALIBRATION

The goal of our calibration is to detect the sensors’

positions and orientations on the operator’s arm auto-

matically. Figure 2 shows the importance of this step,

since a wrong bias may lead to faulty reconstructions

of the arm motions. During our calibration step we

use a model of the human arm. This model consists

of four by spherical joints connected segments corre-

sponding to: Upper arm, lower arm, hand, and ﬁn-

gers. We assume that the ﬁrst joint of the model con-

nects the upper arm to a world coordinate frame. This

model contains also simulated sensors on each seg-

ment. Based on the models movement simulated data

can be acquired. The calibration goes on as follows:

1. Recording of calibration motion

2. Signal preprocessing (see section 4)

3. Apply measured orientations to the model (move

the model)

4. Derive simulated data from model

5. Compare simulated data with measured data

6. Change parameters of the arm model to minimize

the error. Continue with step 3.

Step 3 to 6 are repeated until the resulting error is

small enough. So the calibration can be expressed as a

optimization problem, where the distance between the

measured data and the data derived from the model is

minimized:

min

t,~q,



sensors

model

sensors

t,~q,



, (3)

where:

• D is some distance function

• D

sensors

are data computed from the measured ac-

celerations of the sensors

• D

model

are data computed with the arm model

which depends on:

– O

sensors

are measured orientation data

–

t position of each sensor on its corresponding

segment

– ~q orientation of each sensor with respect to its

corresponding segment

–

s length of each segment

5.1 Optimization Problem

The optimization crucially depends on two different

parameters. First the distance function D. This func-

tion deﬁnes the similarity of two data sets D. As we

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124

have to compare data of different quality (i.e. posi-

tion, orientation and length) different distance func-

tions have to be considered. The actually used func-

tion D may consist of one or any combination of dif-

ferent distance functions. The adequate combination

for our problem will be determined in the results. The

three chosen basic distance functions are:

sensors

− D

model

)

(4)

cos

= arccos



sensors

model



(5)

dir

sensor

− D

model

| (6)

The second important parameter of the optimiza-

tion problem is the kind of data D we use. For the

optimization we only use measured acceleration data

and quantities deduced from the acceleration. Hence

the usable features are:

• aG: local acceleration (measured data from sen-

sors)

• a: global acceleration (without gravitation)

• v: velocity

• p: position

Although all the deduced features are computed from

the local acceleration, there is a difference in qual-

ity. As mentioned above the global acceleration is

computed using the measured orientation. There-

fore, methods like zero velocity updates are applica-

ble. The velocity data v are obtained from integrating

a and are smoother which could result in a more stable

optimization. By additional integration we obtain po-

sition data p, which leads to a further generalization.

So D may be any combination of those features.

The goal is to get D

sensors

, the measured data, and

model

, the results from the model with respect to the

chosen distance function as similar as possible. The

movements of the model are produced by orienta-

tion data O

sensors

. The arm model is conﬁgured by

the segments length, position and the orientation of

each sensor. Depending on this conﬁguration the for-

ward kinematics of the arm induced by O

sensors

results

in a speciﬁc movement. This movement generates the

data D

model

. This constitutes a non-linear optimization

problem which we solved by using the Levenberg-

Marquardt algorithm.

5.2 Calibration Motion

The result of the calibration step depends on the un-

derlying movement. There is no need for speciﬁc

given motion that has to be performed. However,

the calibration motion should cover movements in

Figure 4: The used manipulator. Originally it is mounted

on an EOD robot.

any degree of freedom, if possible. As the calibra-

tion movement is used to determine the arm param-

eters, a wide range of different motions will result

in a well deﬁned solution. Also it is expected that

the optimization process will converge faster. In gen-

eral to provide better results the calibration motion

should include fast movements, pauses (for zero ve-

locity updates) and movements in any possible direc-

tion. Note that the main part of the movements should

be faster than the ZUPT threshold, otherwise the cali-

bration might be unreliable. Experiments show that

calibration movements of less than one minute are

sufﬁcient.

6 ROBOT ARM CONTROL

The manipulator used in our experiments is the robot

arm normally mounted on the teleMAX robot of the

manufacturer Telerob. The conﬁguration is similar

to a human arm, with two differences. First, it is

equipped with a telescope joint. Second and more im-

portant, it is rotated by 90 degrees so that the shoulder

does not turn vertically but horizontally (see Figure

4). The robot arm is equipped with a so called TCP

control. With its help the tip of the gripper can be

moved along and rotate around all axis. Every move-

ment command is related to the point of origin in the

shoulder joint. Using the inertial sensor based recon-

struction of the human arm, the ﬁnger position can

be determined and send to the manipulator. The goal

of the presented control system is to give the opera-

tor a feeling for the movements of the manipulator.

Therefore, the relation of human actions and robot re-

actions must be visible to the operator. If a movement

is performed by the operator the manipulator has to

react and imitate this movement. When designing the

controler, one has to be kept in mind that the used ma-

nipulator is not able to perform movements as fast as

humans.

AUTOMATIC CALIBRATION OF A MOTION CAPTURE SYSTEM BASED ON INERTIAL SENSORS FOR

TELE-MANIPULATION

125

To let the manipulator perform a similar trajec-

tory as the operator, a list of end effector positions

could be implemented. While the human performs

the movement such a list is ﬁlled and the manipulator

will move to the given end effector positions as fast

as possible. We decided not to use such a technique

because of the speed gap between operator and ma-

nipulator. Experiments showed that in our setup there

is no feeling of being directly coupled to the manipu-

lator. The manipulator will be more and more behind

the operator’s movements. Therefore, we decided to

implement a direct transmission of the current opera-

tor’s arm position. This provides the operator with a

feeling of being directly connected to the manipulator

and, in contrast to the list, it is possible to interrupt a

not desired movement. But to deﬁne a trajectory the

TCP has to follow, the operator has to move in the

same speed as the robot does. Otherwise only target

points are given, which the robot tries to reach on a

linear way.

Fully stretched the manipulator has a range of

about 1.50 m, more than a human arm. To achieve the

full length of the manipulator, there must be a scaling

function between the human motion and the manipu-

lator movement. In experiments with different users,

seemed to scaling the up-down direction (the z-axis)

provided an unnatural feeling. Scaling in x- and y-

axis seemed to be no problem to adapt to. So a scal-

ing for x- and y-axis is used to allow the operator to

use the whole operation range of the manipulator. We

decided not to use any scaling for the z-axis.

7 RESULTS

7.1 Calibration of Synthetic Examples

To test the calibration and the used optimizer, we ﬁrst

used synthetic data. The synthetic data were produced

by the arm model. Here for, the model is moved

and the simulated sensors provide us with accelera-

tion and orientation data. As stated in Section 5 the

calibration mechanism has to ﬁnd the correct posi-

tioning of each of the four sensors and the length of

the segment corresponding.

To check whether the optimizer can ﬁnd the cor-

rect conﬁguration we performed several experiments.

On the one hand, we tested situations where only one

of the parameters is erroneous. On the other hand we

have conﬁgurations where all three parameters were

unknown. For example we tested the calibration of

the system with sensors erroneous in orientation. So

the model was build up with the sensors in orientation

not equal to the segment’s orientation. Movements

Figure 5: Some example results for calibration of synthetic

data with all three parameters faulty. E(tr) is the error of the

positioning of the sensors, E(ori) the error of the orientation,

E(len) the error of the segment length.

were simulated and the sensor data computed. These

sensor data were now used by the optimizer. It knows

where the sensors are and how long each segment is.

It does not know in which orientation the sensors are

mounted. The experiment shows that the optimizer

was able to determine the sensors’ orientations. The

described experiment was repeated with each parame-

ter being erroneous and also with all three parameters

erroneous.

As the optimizer itself is also parameterized, the

output depends on the chosen combination of distance

functions and the features chosen (see Section 5). To

check which combination of distance functions and

features is adequate to solve the problem, we tested

every possible combination on the synthetic data. In

ﬁgure 5 some results are given for the experiments

with all three parameters erroneous.

There are some combinations which are not suit-

able to use but it shows that there are several differ-

ent combinations of metrics and features which en-

able the optimizer to ﬁnd the original arm conﬁgura-

tion. This shows that the method is able to ﬁnd the

correct conﬁguration in general. The next step is to

see whether the system is also stable with real data.

7.2 Calibration of Real Sensor Data

To test how the calibration step is effective to the mo-

tion reconstruction on real data a ground truth is nec-

essary. We used an optical motion capturing system

to compare the real movement of the operator with

the uncalibrated and calibrated reconstruction from

our system. In Figure 6 two example movements are

shown. The red crosses show the ﬁnger positions

(used as target position for the TCP of the manipu-

lator) of the uncalibrated system. The green circles

show the calibrated reconstruction. The blue dots rep-

resent the data from the optical system. In the left

picture a wiping motion is shown. The right digram

shows a gripping motion. In both examples the cali-

brated system performs more precise with respect to

the ground truth.

To show the effect of calibration to the motion re-

construction we used a poorly positioned system to

reconstruct a stretched arm. In Figure 7 the wrong

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−1

−0.8

−0.6

−0.4

−0.2

0.2

0.4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

−0.2

0.2

−1

−0.8

−0.6

−0.4

−0.2

0.2

0.40

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

−0.5

0.5

Figure 6: Comparison between an optical MoCap system as ground truth and our inertial sensor based system. The red

crosses: ﬁnger positions of the uncalibrated system, green circles: calibrated reconstruction, blue dots: optical system.

Figure 7: Arm model before and after calibration. The underlying pose is a stretched arm. As the sensors on upper and lower

arm were not well orientated, the uncalibrated model returns a bended arm. After calibration the model shows are pose much

more alike the operator’s actual arm conﬁguration., a stretched arm. Left picture shows view from above, right picture from

the side.

reconstruction can be seen. This ﬂexed position hap-

pens due to the fact that the model expects the sensors

to be aligned to the orientations of the segments. Af-

ter the calibration step the model is very close to the

real pose, but with a small error in the bended elbow.

7.3 Control

To show that the presented method in general is ade-

quate to control the manipulator we had different per-

sons to perform several tasks. Those tasks were:

• Perform an inﬁnity symbol

• Push a small log from the corner of a table

• Grip the log, (whereas the opening of the gripper

is only slightly wider than the logs diameter)

Half of the users had never used this control method

before. They had to perform each of the tasks twice.

Once without any training, and once after some train-

ing. The results can be seen in Table 1 and 2.

Here you can see that, even totally untrained, most

users were able to use the manipulator in the desired

way. In fact the untrained test persons have a lower

failure rate as the trained people. This may result from

the ambitions of the trained people to be faster. This

seems to make them more careless.

Table 1: Performance of untrained users with the control

system.

task successful failure average time [s]

inﬁnity 8 0 37

Pushing 8 0 49

Gripping 7 1 52

Table 2: Performance of fairly trained users with the control

system.

task successful failure average time [s]

inﬁnity 14 0 34

Pushing 13 1 31

Gripping 12 2 35

Often expressed criticism was the gap between the

speed of the manipulator and the human arm. Most

users needed some time to adapt to the much slower

manipulator. So we expect better results when using

a more agile manipulator.

8 CONCLUSIONS

In this paper we presented a control method for a

robot arm. The main requirements to fulﬁll were an

AUTOMATIC CALIBRATION OF A MOTION CAPTURE SYSTEM BASED ON INERTIAL SENSORS FOR

TELE-MANIPULATION

127

intuitive control, simple usage, and little space con-

suming. Our system based on inertial sensors to be

worn on the operator’s arm. With the help of an au-

tomatic calibration function, the exact placement of

these sensors can be found, which makes an exact

placement obsolete. Hence, equipping the system is

rather simple and fast. The system is able to recon-

struct the motion of the operator and therefore, send

them to the manipulator. This results in a direct mo-

tion control where the user steers the TCP of the ma-

nipulator with his own movements. First experiments

showed that even untrained persons can use the con-

trol system to fulﬁll certain tasks.

However, the system showed some points to im-

prove, especially in its intuitive use. As it took a lot

of time to get used to the fact, that the operator only

steers the TCP but not the morphologies of the arm,

a next step in development will be to map the recon-

structed joint angles of the human arm to the joints of

the manipulator to achive a similar manipulator con-

ﬁguration compared to the operator’s pose. Also a

continuous calibration could be useful. The calibra-

tion as it is designed showed no reason not to be used

parallel to the steering task. This enables the system

to react to changes e.g. a loose sensor or changes in

the magnetic ﬁeld. Here experiments are necessary to

show if the system stays stable.

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