Advanced Robotics 23 (2009) 1327–1358

www.brill.nl/ar

Full paper
iSoRA: Humanoid Robot Arm for Intelligent Haptic
Interaction with the Environment
Dzmitry Tsetserukou ∗ , Naoki Kawakami and Susumu Tachi
Department of Information Physics and Computing, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku,
Tokyo 113-8656, Japan
Received 5 November 2008; accepted 7 February 2009

Abstract
The paper concentrates on the development and control of the humanoid robot arm iSoRA, intended for
operation in a dynamic unstructured environment. Optical torque sensors integrated into each joint enablemeasurement of contacting forces along the entire manipulator surface. A variable admittance control strategy was elaborated to increase the robot functionality and to achieve the human-like dynamics of interaction.
The experimental results show that the proposed approach not only provides safe interaction of the robot
arm with a person, but also improves the effectiveness of contact task performance. The paper also presents
a novel concept of avoidance of an obstacle of unknown shape. The tactile sensory ability of the developed
manipulator allows robot links to follow the object contour and to perform motion planning in the dynamic
environment. The information on the applied normal force vector, object shape and target point coordinates
is supplied to the motion planning system. The algorithms for contact point detection, object geometry
recognition, and estimation of contacting object stiffness are detailed. The numerical simulation elicits acapability of the proposed method to approximate various object shapes precisely. The experimental results
showed that the local admittance control and motion planner allowed the end-effector to follow the object
contour in a very smooth, consistent manner while reaching the target point.
© Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009
Keywords
Humanoid robot arm, variable admittance control, human–robot interaction, obstacle avoidance, opticaltorque sensor

1. Introduction
Service robotics aimed at assisting humans in everyday life environments is nowadays gaining increased interest from researchers and industry. Such robots areprogrammed to perform continuously changing tasks in unstructured human envi* To whom correspondence should be addressed. E-mail: dima_teterukov@ipc.i.u-tokyo.ac.jp© Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2009

DOI:10.1163/156855309X462619

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ronments. Both coexistence, when the human and robot share the same workspace,and cooperation, when the human and robot work on the same task, impose strict
requirements on manipulator behavior and control in order to ensure safe interaction with the environment, effectiveness of target task execution and ergonomic
cooperation.
Classical motion planning algorithms for general manipulators in cluttered environments first construct the robot configuration space with a known set of stationary
obstacles and then generate collision-free motions in the configuration space [1]. In
the case of real indoor environments, known obstacles (e.g., chairs, tables, etc.) andunknown obstacles (persons approaching the robot, unmapped environment, etc.)
change their location dynamically. Moreover, the object shape, speed and position
can also alter in an unpredictable way, leading to obstruction of the planned robot
motion.
Thus, the robot control system faces three main issues: (1) establishing safe physical contact with environment, (2) planning collision-free motion and recognition of
obstacle shapes, and (3) directing the robot to its goal position. To meet these challenges, sensor-based planning making use of real-time sensor information about thesurroundings is needed.
For an autonomous mobile robot moving in an unknown environment, non-tactile
sensors are normally used. A variety of algorithms for shape recognition from camera images has been developed (e.g., stereo vision, optical flow). However, thesetechniques usually require a lot of computation and, therefore, are not suitable forreal-time use [2].
When it comes to a multi-d.o.f. robot manipulator working in an unknown environment, the issues of obstacle avoidance become much more complicated becauseof the high risk of collision of each joint and each link of the robot arm with an obstacle. The most effective solution for preventing collisions is to endow the whole
robot arm with the ability to safely contact and interact with environment in realtime. Lumelsky [3] pioneered the idea to cover a manipulator with a sensitive skincapable of detecting nearby objects. An array of infrared proximity sensors delivers
the information about any obstacles obstructing the arm motion to the control system. The algorithm of the motion planner maneuvers the robot arm, thus avoiding
contact [4, 5]. Recently, a high-speed vision system attached to the robot manipulator for collision avoidance was developed [6].
We argue that the robot arm should be controlled not to avoid physical contact,
but rather to ensure safe tactile interaction with the environment during obstacle
avoidance. The benefits of such interaction are obvious. The robot can acquire
plenty of fundamental information about objects by touch (shape, stiffness, texture, location, fixation, etc.) and plan its motion in the most optimal fashion. Itshould be noted that tasks under human supervision, such as transporting the object, leading the robot tip via force-following and performing the assembling tasks,
require the processing algorithm of the contact state. Conventional approaches tohandle the physical interaction between a manipulator and environment are based

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on the impedance control of a robot arm tip [7]. However, the other parts of the robot body (forearm, elbow, upper arm, shoulder and torso) are insensitive to contact
and present significant danger not only to humans, but also to the robot structure
itself. Generally, two techniques of safe interaction with the entire robot arm arewell recognized, i.e., passive compliance control and active force control.
Whereas mechanical leaf springs [8] and variable stiffness actuators [9] integrated into each joint allow achieving a fast response to an external disturbance,
they also cause vibrations destabilizing the dynamic behavior. Additionally, therobot control in such cases is complicated by many unknown parameters (e.g., actuator stiffness and damping). Another approach to improve the intrinsic safety of
the robot is to reduce the weight of components. The main drawback of this method
is the occurrence of undamped structural vibrations.
Active force control implies that the robot structure is stiff enough to provide
high position accuracy and contact ability with environment is achieved by excessive sensory system feedback [10]. However, due to the time lag induced by the
sampling process, such robots pose dangers at the moment of impact. Mitsunaga et
al. [11] progressively improved the tactile ability of the robot through covering its
entire body with piezoelectric film-based tactile sensors. Since this device integrates
a huge amount of small sensors incorporated into soft layer, and requires complicated wiring and signal processing hardware, it has high cost and reliability issues.
2. Robot Arm and Sensory System
To realize the safe physical contact of the entire robot arm structure with humans and to guarantee obstacle avoidance, we developed a whole-sensitive robot
arm iSoRA (intelligent Soft Robot Arm) (by using distributed torque sensors ineach joint). When contact with the environment occurs, the manipulator automatically generates compliant motion according to the measured external force (active
control). Thus, the whole structure of the manipulator can safely interact with an
unstructured environment. The torque sensors introduce compliances, which soften
the impact forces on the initial state of the contact transient (passive control).
The developed robot arm iSoRA (Fig. 1) has 4 d.o.f.: roll, pitch and yaw jointsof a shoulder, and a pitch joint of an elbow. Such orthogonal disposition of the axes
simplifies the installation of the torque sensors and motor drives into the joints.
Coordinate systems based on the Denavit–Hartenberg convention are represented
in Fig. 2. The 8-d.o.f. robot hand allows performing dexterous manipulations.
Each robot joint is equipped with an optical torque sensor. The sizes and appearance of the arm were chosen so that any sense of incongruity during interaction witha human is avoided. We kept the arm proportions the same as in an average height
human, aged 25: upper arm length L1 0.308 m, upper arm circumference 0.251 m,
forearm length L2 0.241 m and forearm circumference 0.189 m.
The specific application (i.e., a robot arm interacting with human beings in asafe manner) introduces special requirements for the design of the torque sensor.

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Figure 1. Robot arm iSoRA.

Figure 2. Coordinate system.

In order to facilitate the realization of torque measurement in each arm joint, wedeveloped new optical torque sensors based on results presented in Refs [12, 13].
The novelty of our method is the application of the ultra-small-size photointerrupter(PI) RPI-121 as a sensitive element to measure the relative motion of sensor components. The dimensions of the PI (3.6 mm × 2.6 mm × 3.3 mm) and its weight of
0.05 g allow realization of a compact design. The spring components were manufactured from one piece of AISI 4135 steel using a wire electrical discharge machining.
The optical torque sensor is set between the driving shaft of the harmonic transmission and driven shaft of the joint (Fig. 3). When the load is applied to the robot
joint, the magnitude of the output signal from PI corresponds to the exerted load.

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Figure 3. Torque sensor of the elbow joint and calibration results. (a) Optical torque sensor. (b) Calibration results.

The sensors attached to the first, second and third/fourth joints were designed tomeasure torques of ±12.5, ±10.5 and ±4.5 Nm, respectively. A non-linearity of
2.5% FS was calculated using the maximum deviated value from the best-fit line.
The developed optical torque sensors are characterized by high dependability, good
accuracy (even in electrically noisy environments), low price, compact size, lightweight, and easy mounting procedure.
3. Intelligent Variable Admittance Control
3.1. Joint Impedance Control
The dynamic equation of an n-d.o.f. manipulator in joint space coordinates (during
interaction with the environment) is given by:M(θ )θ̈ + C(θ, θ̇)θ̇ + τf (θ̇ ) + G(θ ) = τ + τEXT ,(1)

where θ , θ̇ and θ̈ are the joint angle, the joint angular velocity and the joint angle
acceleration, respectively, M(θ ) ∈ R n×n is the symmetric positive definite inertia
matrix, C(θ, θ̇) ∈ R n is the vector of Coriolis and centrifugal torques, τf (θ̇ ) ∈ R n is
the vector of actuator joint friction torques, G(θ ) ∈ R n is the vector of gravitational
torques, τ ∈ R n is the vector of actuator joint torques, and τEXT ∈ R n is the vectorof external disturbance joint torques.
People can perform dexterous contact tasks in daily activities, regulating theirown dynamics according to a time-varying environment. To achieve skillful humanlike behavior, the humanoid robot has to be able to change its dynamic characteristics depending on time-varying interaction forces. The most efficient method ofcontrolling the interaction between a manipulator and an environment is impedancecontrol [14]. This approach enables us to regulate response properties of the robot to external forces through modifying the mechanical impedance parameters.
A schematic representation of joint impedance control is given in Fig. 4.
The desired impedance properties of the ith joint of manipulator can be expressed as:
Jdi θ̈i + Ddi θ̇i + Kdi θi = τEXTi ;θi = θci − θdi ,

(2)

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Figure 4. Concept of the local impedance control: when external force FEXT is applied to the robot
link i + 1, assigned values of desired inertia Jd(i+1) , damping Dd(i+1) and stiffness Kd(i+1) of thei + 1 joint define the dynamic properties of this robot joint in response to the exerted external torqueτEXT(i+1) .

where Jdi , Ddi and Kdi are the desired inertia, damping and stiffness of the ith joint,respectively, τEXTi is the torque applied to ith joint and caused by external forces,and θi is the difference between the current position θci and desired position θdi .
The state-space presentation of the equation of local impedance control is written
as:

 
  

θ̇i
0
1
θi
0
=
+
τ
(t),
(3)
v̇i
vi
−Kdi /Jdi −Ddi /Jdi
1/Jdi EXTi
or:


 
θ̇i
θ
= A i + BτEXTi (t),
(4)
v̇i
vi
where the state variable is defined as vi = θ̇i , and A and B are matrices. After
integration of (4), the discrete time presentation of the impedance equation is expressed as:




θk+1
θk
(5)
= Ad
+ Bd TEXT(k) .
θ̇k+1θ̇k
To achieve the fastest possible non-oscillatory response to the external force, weassigned the eigenvalues λ1 and λ2 of matrix A as real and equal to λ1 = λ2 = λ,λ > 0. By using the Cayley–Hamilton method for matrix exponential determination,
we have:
1 − λTT,(6)
Ad = eλT−Kd T /Jd 1 − λT − Dd T /Jd λT1e (1 − λT ) − 1
Bd = (Ad − I )A−1 B = −,(7)
Kd −(Dd /(2Jd ))2 T eλT
where T is the sampling time and I is the identity matrix.

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There are several conflicting requirements on the choice of dynamics parameters of the impedance model to provide effectiveness and functionality of a service
robot in tasks of physical interactions fulfilled in cooperation with humans and toensure the collision avoidance. For example, while accomplishing service tasks forhumans in the autonomous mode, it is required to provide high stiffness (to ensuresmall position error during object handling) and high damping (for good velocity tracking). Realization of human-following motion, mainly used in performing
cooperative tasks, also imposes specific requirements on the desired impedance parameter selection. In this approach, by applying force to the robot arm, it is possible
to intuitively operate the humanoid robot motion along the force and speed direction without considering any command signals. In the case of a collision, the verysmall stiffness is obligatory to reduce the impact forces.
Basically, there are two types of solution of this problem: adaptive and functional adjustments of impedance parameters. In adaptive control, the damping andstiffness of the system are gradually adjusted according to sensed dynamics andcontact forces [15]. However, due to parametric uncertainties of the robot dynamics
model, it is difficult to obtain the complete description of the dynamics. Therefore,
model-based adaptive impedance control must rely on either repeated motions ortime for adaptation to achieve convergence to the desired system parameters. For
systems with more than 1 d.o.f., such an approach can hardly be applied. In thefunctional approaches, current impedance parameters have predetermined relations
to the current sensed variables. Generally, these methods presume determining thecurrent stiffness and damping matrices functionally dependent on sensed variables.
The main idea of Ref. [16] is that the contact between the slave robot and the environment can be classified according to the angle between the commanded velocity
and the contact force. It is supposed that force and velocity vectors are usually
parallel when the impact occurs or when the object is being pushed. Thus, the functional dependency of stiffness and damping on the angle between sensed force andvelocity vectors was proposed. In real cooperation tasks and, especially, collisions,these vectors can not only be parallel, but also have independent arbitrary directions
according to the direction of the external force.
Another line of variable impedance research is directed to the estimation ofhuman arm stiffness [17]. The proposed variable impedance controller varies a
damping parameter of target impedance in proportion to an estimated value of thehuman arm stiffness. Despite the fact that humanoid robot arm can effectively follow the human arm motion, it cannot perform tasks autonomously. Besides, only
impedance parameters of the robot end-effector can be adjusted. We elaborated a
new methodology for impedance parameter adjustment based on the physical interaction mode and providing the dynamic stability of the system.
3.2. Intelligent Variable Joint Admittance Control
It was found that humans adjust their joint stiffness to accommodate changes insurface stiffness [18]. Furthermore, the research on impedance characteristics of a

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human arm shows that, while pushing or pulling the object naturally, the humanarm stiffness and damping behavior can be approximated by exponential curves[19]. The first essential peculiarity of the new control method is that we introduce
the exponential functional dependency between the sensed force and stiffness to
impart the human-like damping and stiffness behavior to the robot arm interactingwith the environment. The second main feature originates from the fundamental
conflict in impedance selection with regard to current working conditions. We consider the threshold of the external disturbance torque value τEXTth to distinguishthe service task (with high stiffness and damping of joints) from human-following
motion tasks requiring low stiffness. This value can be chosen depending on theforce necessary to accomplish the service task (e.g., lifting a box and physicallysupporting a disabled human require individual values of forces generated by the
robot arm). We assigned the specific magnitude of τEXTth to each joint of the robot
arm. The third distinctive contribution is the recognition of the collision based on
the time derivative of the joint torque.
3.2.1. Control for Accomplishment of Service Tasks and Human-Following Motion
The procedure of impedance parameters selection is illustrated by the example ofthe elbow joint as follows. In the first stage, the parameters of desirable impedancemodel of the robot are computed for the case of service task accomplishment and theaverage level of contact force of the human–robot interaction. The desired stiffness
Kd1 (for the static equilibrium case) is calculated from (8) based on the maximum
deflection value of the joint angle θmax caused by external torque τEXT while
performing the service task:
Kd1 =

τEXT
.
θmax

(8)

It was defined that a external torque of 1 Nm results in θi max of 0.1 rad giving
Kd1 of 10 (Nm/rad). The desired damping is expressed as:(9)
Dd1 = 2ζ Kd1 Jd1 .
To realize a fast non-oscillatory response on the external torque, we defined thedamping coefficient ζ as 1.05. The value of desired inertia Jd1 of 0.1 (kg·m2 ) was
assigned to realize fast response tracking. Thus, the value Dd1 of 2.1 (Nm·s/rad)was derived from (9). These parameters are valid till the interaction force does notcause any overload of robot arm. When the sensed value of the torque is higher
than the threshold level, the robot recognizes this condition as the human-following
motion mode and adjusts its dynamics parameters (stiffness and damping) in thesame way as a human in order to provide smooth natural interaction. To realize
such continuous change of dynamics, we use an exponential relation between theexternal disturbance torque and desired stiffness:
Kd2 = Kd1 eμ(τEXT −τEXTth ) ,(10)

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where Kd2 is the desired stiffness on the second stage of the interaction, and μ is
the coefficient defining the level of decreasing the arm joint stiffness in response toincreasing the difference between external torque and torque threshold.
The desired damping is adjusted to prevent force responses from being too sluggish while changing stiffness values and to ensure contact stability:(11)
Dd2 = 2.1 Kd2 Jd1 = 2.1e0.5μ(τEXT −τEXTth ) Kd1 Jd1 .
Then, the variable joint impedance controller is described by:
Jdi θ̈i + 2.1e0.5μ(τEXTi −τEXTth ) Kd1 Jd1 θ̇i + Kd1 eμ(τEXTi −τEXTth ) θi= τEXTi ; θi = θci − θdi .

(12)

To verify the theory and to evaluate the feasibility and performance of the
proposed impedance controller, experiments with the iSoRA robot arm were conducted. To ensure the effectiveness of service task accomplishment, we decided to
implement position-based impedance control (admittance control) (Fig. 5, Kp , Kv ,TG and TZ denote proportional gain, derivative gain of PD controller, torque causedby gravity forces, and torque measured by torque sensor, respectively). In this algorithm, the compliant trajectory generated by the impedance controller is tracked by
the PD control loop.
During the experiment, the interaction with arm was performed to exceed thejoint torque threshold level (τEXTth of 0.6 Nm was assigned, μ = −1.155). The
experimental results for the elbow joint — applied torque, impedance trajectories
with constant (conventional approach) and variable coefficients (proposed intelligent variable joint admittance control), variable stiffness plot, variable damping

Figure 5. Block diagram of intelligent variable admittance control.

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Figure 6. External torque.

Figure 7. Impedance trajectories.

Figure 8. Variable stiffness Kd2 .

plot, applied torque with different magnitudes and corresponding impedance trajectories with constant and variable coefficients — are presented in Figs 6–11,
respectively.
The experimental results show the successful realization of the variable joint
impedance control. While contacting with the human, the robot arm generates compliant soft motion according to the sensed force. The plot presented in Fig. 7 showsthat the variable impedance control provides a softer trajectory to accomplish the

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Figure 9. Variable damping Dd2 .

Figure 10. External torque.

Figure 11. Impedance trajectories.

human-following motion than the impedance control with constant coefficients. As
we assigned the critically damped response of the impedance model to the disturbance force, the output angle (θk+1 ) has an ascending–descending exponential
trajectory. As seen from the experimental results shown in Figs 10 and 11, theproposed variable impedance control comprehensively distinguishes and processes
the service tasks (torques under threshold level) and cooperative human-following
tasks (torques above threshold level). As the result of the experiments with the vari-1338

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able impedance-controlled arm, tactile sensation of soft friendly interaction wasachieved.
3.2.2. Intelligent Impact Control
In daily life, a person frequently contacts with an environment. The impact between a limb and the environment is inevitable during the interaction. In order towork in human daily environments, the service robot has to recognize the collisionconditions, and its control system should be able to guide the manipulator quickly
and smoothly so to avoid excessively large impact forces. Different impact control
algorithms have been proposed. Basically, they focus on preparing for an impact
in advance [20] and coping with impact during contact transience [21]. However,
due to the unexpected nature of collisions and the lack of a robust control strategy
of impact effect minimization, the application area of such approaches is greatly
limited.
We believe that realization of robust impact control should originate from thehuman reflex system. A reflex action is an automatic involuntary neuromuscularaction elicited by a defined stimulus and it can respond to external stimuli with asmall reaction time retracting the limbs away from the object. In our control algorithm, impact is processed as follows. When an unexpected collision is detected,
the impact control algorithm provides the pre-programmed reflex action of the robot arm. After accomplishing a safe and smooth collision, the control system isreturned to the original mode aimed at distinguishing the service and cooperative
tasks.
While analyzing the results of collision experiments, we came to conclusion thatthe large contact forces, mainly used as the criteria of collisions, do not indicate theimpact, while the time derivative of the force value does. In the developed impact
control system, the value of the time derivative of torque τ̇EXTth exceeding the assigned threshold is interpreted as a collision state. To realize reflexive human-likebehavior, the stiffness of the impedance model of the robot arm in the first stage ofcontact transient has to be reduced drastically. Hence, an exponential relation between the time derivative of the external torque and the desired stiffness coefficient
Kd3 was defined with a large coefficient μ of 0.7:
Kd3 = Kd1 e−μτ̇EXTth .

(13)

To attenuate the oscillations of the robot arm, which are inherent in collisions,the larger constant damping coefficient ζ of 1.25 was assigned. The value of thedesired damping coefficient is calculated from (9). In the second stage of the contact
transient, while the time derivative of torque is negative, a high damping value
reducing arm inertia effect is desired. For this case, the following relation between
desired damping Dd3 and time derivative of torque was specified:
(14)
Dd3 = 2.5 Kd1 Jd1 − αd τ̇ ,where αd is the weighting factor for damping (equal to 1.2).

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Figure 12. Torque derivation.

Figure 13. Variable stiffness Kd3 .

Figure 14. Variable damping Dd3 .

The threshold value of the time derivative of torque τ̇EXTth was 2.6 Nm/s. The
experimental results for the elbow joint — time derivative of applied torque, desired
collision stiffness Kd3 , desired collision damping Dd3 plot, impedance trajectories
with constant and collision variable coefficients, time derivative of torque for two
different contact states and corresponding impedance trajectories — are presented
in Figs 12–17, respectively.
From the plots of experimental results, it is apparent that, when the impact isrecognized (the time derivative of torque becomes larger than the threshold value),

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Figure 15. Impedance trajectories.

Figure 16. Torque derivation.

Figure 17. Impedance trajectories.

the stiffness and damping are decreasing drastically (Figs 13 and 14), allowing
avoidance of large impact forces. In the second stage of the interaction, when
the impact force is decreasing, the damping coefficient of the impedance model(Fig. 14) is increased to suppress the dynamic oscillations. Figure 16 indicates thatthe nature of cooperation tasks (graph of the time derivative of torque has a smallvalue of local extremum and stretched in time-scale function) completely differsfrom the impact state (graph of the time derivative of torque has a large spike andD. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

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contracted in time-scale function). The developed control takes advantage of thisfeature to generate reflex action-based motion.
4. Novel Concept of Obstacle Avoidance and Object Exploration ThroughTactile Interaction
4.1. Introduction and Problem Statement
Avoidance of an obstacle with an unknown shape in an unstructured environment is
the fundamental and significant problem in service robotics, which has not yet beensolved in a satisfactory way.
A fairly small amount of work has been done on tactile interaction of the whole
robot arm with the environment. Bauer [22] conducted very practical and useful research on haptic exploration of the environment. The idea underlying the algorithm
is to identify the free space, object location and object shape by several attempts
made by the robot arm in order to pass through an obstacle. It should be noted, however, that the proposed system cannot detect the contact point coordinates, requires
a time-consuming obstacle identification process and, as the authors reported, theexploration algorithm fails when several collisions occur simultaneously.
Substantially more research has been done in the field of object exploration andobject recognition by the humanoid robot hand. Okamura et al. [23] introduced
an approach for haptic exploration of an unknown object through traversing theobject surface by robot fingers. The process consists of a sequence of phases, inwhich some fingers are responsible for grasping, and others roll and slide over theobject surface. Exploratory strategies for the determination of the object geometry
with haptic sensing have been developed [24, 25]. Instead of mounting a distributed tactile sensor on the finger link, the method described in Ref. [26] roughly
detects a contact and then initiates the motion of the fingertip along the contour.
An approach to real-time shape recognition and grasping of unknown objects by arobot hand with soft fingers and an omnidirectional camera is proposed in Ref. [2].
The polygon circumscribing the horizontal cross-section of the object is calculatedusing the volume intersection method.
However, the approaches mentioned above are mostly intended for grasping optimization, not for obstacle avoidance. To cope with the spatial uncertainty of an
unknown, unstructured environment, we elaborated a novel concept of obstacle
avoidance through physical interaction. When the robot arm is obstructed by an
obstacle, the controller directs the manipulator to the target point in such a way thatthe robot arm surface follows the contour of the object. For object shape recognition, the method based on B-spline interpolation is proposed.
4.2. Algorithm of Obstacle Avoidance Through Tactile Sensing
The procedure of obstacle avoidance is as follows:(i) The contact state is recognized once the actual torque in the robot joint exceeds
the assigned threshold value.

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358(ii) The control system computes the normal force vector Fn based on measured
joint torque values and the contact point detection algorithm.
(iii) Local admittance control generates compliant motion according to the applied
normal force vector and maintains the contact force value to be nearly 1 N.
(iv) The object shape is approximated by the cubic B-spline. The next position of
the robot arm is defined by the direction of the tangent vector to the curve at
the contact point and the cross product of vectors locating the end-effector Pt
and target point Pf (Fig. 18).
(v) If the value of cross product equals zero, the vectors Pt and Pf become
collinear. That means that the robot end-effector has reached the target point.
Figure 18 graphically illustrates the obstacle avoidance procedure through haptic
interaction. Point A presents the position of the end-effector when the contact stateis detected. The robot arm moves to the target point B (the place where the object
needs to be grasped is located), establishing continuously physical contact with the
obstacle.
The methods for contact state recognition and computation of contact point coordinates are given below.

Figure 18. Layout of the obstacle avoidance procedure.

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4.3. Contact State Recognition
During the first stage of control the robot links rotate until one of them contacts theobject. In addition to contact force, torque sensors continuously measure the gravity
and inertial load. Since the robot arm moves with low angular speed, the inertial
load component can be disregarded. The Newton–Euler dynamics formulation was
adopted to calculate the reference values of the gravity torques. The application of
the algorithm for robot arm iSoRA results in the equation of the gravitational torquevector:
⎡
⎤
τg1
⎢τ ⎥
G(θ ) = ⎣ g2 ⎦
τg3
τg4
⎤⎡ ⎤
⎡
g
LM2 m2 (s1 c2 c4 + c1 c3 s4 + s1 s2 s3 s4 ) + (LM1 m1 + L1 m2 )s1 c2
LM2 m2 (c1 s2 c4 − c1 c2 s3 s4 ) + (LM1 m1 + L1 m2 )c1 s2
⎥⎢g⎥
⎢
=⎣
⎦⎣ ⎦,
g
LM2 m2 (−c1 s2 c3 s4 − s1 s3 s4 )g
LM2 m2 (−c1 s2 s3 c4 + s1 c3 c4 + c1 c2 s4 )(15)
where τgi is the gravitational torque in the ith joint, m1 and m2 are the point massesof the first and second link, respectively, LM1 and LM2 are the distances from thefirst and second link origins to the centers of mass, respectively, L1 is the upper arm
length, and c1 , c2 , c3 , c4 , s1 , s2 , s3 and s4 are abbreviations for cos(θ1 ), cos(θ2 ),cos(θ3 ), cos(θ4 ), sin(θ1 ), sin(θ2 ), sin(θ3 ) and sin(θ4 ), respectively.
In the case when the robot arm performs planar motion in the X0 Z0 plane, only
the first and fourth joints operate. The gravity torques acting at the first τg1 andfourth joints τg4 are derived from:τg1 = m2 g(LM2 sin(θ1 + θ4 ) + L1 sin(θ1 )) + m1 gLM1 sin(θ1 );τg4 = m2 gLM4 sin(θ1 + θ4 ).

(16)

The experiment with the fourth joint of the robot arm was conducted in order tomeasure the gravity torque (Fig. 19a) and to estimate the error by comparison witha reference model (Fig. 19b).
As can be seen from Fig. 19b, the peak values of the gravity torque estimation
error arise at the start and stop stages of the joint rotation. The reason for this is
the high inertial loading that provokes the vibrations during acceleration and deceleration transients. This disturbance can be evaluated by using accelerometers andexcluded from further consideration. Observing the measurement error plot, we canassign the relevant threshold that triggers control of the constraint motion.
4.4. Contact Point Determination
The information about the contact point coordinates is necessary for the calculationof the object stiffness, applied force vector and object shape reconstruction. The

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Figure 19. Experimental results of gravity measurement.

tactile sensor can be useful in this case. However, our goal is to perform tactileinteraction with the environment without any complicated sensory system of therobot. The method we present here results from the assumption that the coordinatesof intersection of the subsequent profiles of the robot arm equal the coordinatesof the contact points during tactile obstacle avoidance. This assumption holds truesince the increment of joint angles is small enough because of the fast sampling rateof 1700 Hz and slow collision avoidance motion. Let us consider the case when theforearm is contacting with an object. The line representing the robot forearm islocated by the following position vectors:
Ptx
L2 (s1 s2 s3 s4 + c1 c3 s4 + s1 c2 c4 ) + L1 s1 c2
Pt = Pty =(17)−L2 (c2 s3 s4 − s2 c4 ) + L1 s2Ptz−L2 (c1 s2 s3 s4 − s1 c3 s4 + c1 c2 c4 ) − L1 c1 c2
Pex
L1 s1 c2
Pe = Pey =
,
(18)
L1 s2
Pez
−L1 c1 c2
where L1 and L2 are the lengths of the upper arm and forearm, respectively, and Pt
and Pe are the vectors locating the tip of robot arm and elbow joint, respectively.
During surface following, the subsequent position of the link is defined by joint
angle increments θi + θi . New coordinates of the end points of forearm Pt andPe are derived through substitution θi + θi into (17) and (18), respectively. Theintersection point of two consequent lines defines the coordinates of the contact
point. In the case of planar motion (when θ2 = θ3 = 0), the intersection coordinates(PCx ; PCz ) can be defined using determinants:
Pex Pez
Pex − Ptx
Ptx Ptz
PCx =Pex Pez
Ptx PtzPex − Ptx
Pex − Ptx

Pex − Ptx
Pez − Ptz
Pez − Ptz=

−L1 (s14 s4 − s14 s4 )
s14

(19)

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Figure 20. Shape recognition of the round and rectangular object.

Pez
Ptz

Pez − Ptz

Pez
Ptz
Pex − Ptx
Pex − Ptx

Pez − Ptz

Pex
Ptx
PCz =

Pex
Ptx

Pez − Ptz
Pez − Ptz=

−L1 (c14 s4 − c14 s4 )
,
s14

(20)

where s14 , s14 , s14 , s4 , c14 and c14 stand for sin(θ1 + θ4 ), sin(θ1 + θ1 + θ4 +θ4 ), sin(θ1 + θ4 ), sin(θ4 + θ4 ), cos(θ1 + θ4 ) and cos(θ1 + θ1 + θ4 + θ4 ),respectively.
4.5. Shape Recognition
Studies on human tactile perception show that edge or contour following is one of
the common exploratory procedures that people use for the determination of object
geometry [27]. We propose the tactile object shape recognition method based on theacquisition and formulation of information as image primitives. Tactile images are
generated when the robot arm registers a series of contact points with an object. The
node points obtained by employing the contact point detection algorithm (17)–(20)are interpolated by B-splines. Thus, complex shapes of obstacles can be detected
accurately. Graphical modeling of the shape recognition during avoidance of roundand rectangular obstacle is shown in Fig. 20.
The B-spline curve is expressed as a convex combination of polygon vertex position vectors. The spline method solving the connection problem that exists withother techniques features superior controllability and desired continuity [28]. To
obtain C2 continuity, we employ a cubic uniform B-spline. The segment i of spline
curve Pi (t) is a cubic parametric polynomial described by:⎡⎤⎡⎤−1 3 −3 1
Pi−1
1⎢ 3 −6 3 0 ⎥ ⎢ Pi ⎥(21)
Pi (t) = [ t 3 t 2 t 1 ] ⎣
⎦⎣
⎦,
−3 0
3 0
Pi+1
6
Pi+2
1
4
1 0

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

Figure 21. Method of object shape approximation.

where Pi−1 , Pi , Pi+1 and Pi+2 are the control points (contact points preliminary
calculated from (19) and (20)); i = 1, 2, . . . , n − 2; n + 1 is the number of given
control points; and t ∈ [0, 1] is the global parameter.
An actual spline curve is made of these curve segments Pi (t). Let us estimate theerror of object shape approximation by using the proposed approach. We assume
that the object has a circular contour of radius R and the contact points PCi defined by intersections of subsequent robot link positions li and li+1 are equidistant(Fig. 21). The radius of circle RP , which the contact point is placed on, is expressedfrom the right triangle OAPC0 :
RP =

R
,
cos(π/m)where m is the number of control points (equal to n + 1).
The coordinates of contact points PCi are defined by:





π
π
2πi
2πi
−
, RP sin−
PCi = RP cos
m
m
m
m

2πi
π
2πi
π 
R cos( m − m ) R sin( m − m )=,.
cos(π/m)cos(π/m)

(22)

(23)

For the sake of simplification of further computations, we assume the radius R of
one unit and rotate the contact points PCi in the clockwise direction on angle π/m.
The transformed control points Pi are placed on the circle with a different graphical

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

scale for geometrical visualization (numerical value of RP is equal to RP ). Cartesian
coordinates of control points can be calculated from:sin( 2πicos( 2πi
m )
m )
,
,
(24)
Pi =
cos(π/m) cos(π/m)
where i = 0, 1, . . . , m − 1.
The worst approximation is obtained midway between the control points, i.e.,
Pi (0.5). The midpoint of a cubic segment, however, is easily derived from (21) as:1(Pm−1 + 23P0 + 23P1 + P2 ).
(25)
48
After substitution of the control point coordinates from (24) to (25) we have:(1 + cos θ )(11 + cos θ ) sin θ (11 + cos θ )
PM =
,
,
(26)
24 cos(θ/2)
24 cos(θ/2)
PM = P1 (0.5) =where θ = 2π/m.
The deviation E (%) from a true circle is expressed as: 
2 × 100 = 1 − cos(2π/m) × 100.
E = 1 − PM(27)
12The deviations from a true circle for different numbers of control points are listed
in Table 1.
The proposed approach based on the B-spline can, therefore, provide an excellent approximation to a circle. Since the robot angular iterations are fairly small,the number of control points is huge, which results in a very precise contour recognition. The curvature of the curve segment i is calculated as:Pi = P  − P  P 
Pix
iy
iy ix
2 + P 2 )3/2
(Pix
iy

(28)

.

The algorithm of contour recognition of the rectangular-shaped object is asfollows. In the presence of a vertex, the lines intersect in one point. When thecoordinates of the intersection point change drastically, the subsequent vertex isrecognized. The line connecting two vertices define the edge of the object (Fig. 20).
More complicated shapes can be detected by a combination of B-spline interpolation, vertex and edges recognition. If the curve-defining vectors Pi−1 , Pi , Pi+1 andTable 1.
Deviation from a true circle
m

E (%)

12

16

20

24

28

32

36

40

44

1.117

0.634

0.408

0.284

0.209

0.16

0.127

0.103

0.085

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Pi+2 lie collinear, the curve segment defined by those four position vectors degenerates to a line segment. This line segment connects continuously to the neighboring
curve segments with continuity up to the curvature vector. This feature is extremely
useful in the case of complex shape recognition.
4.6. Estimation of Contacting Object Stiffness
During the first stage of control the robot links rotate until one of them contacts theobject. The control system detects the contact state using the reference and actual
torque values in the joints.
During contact transition we can acquire information about the collision dangerof contacting the object through its stiffness estimation and represent this information to the operator. This can be done by establishing stiff contact through PD
control of the robot arm with high P-gain until the joint torque exceeds the threshold value of 0.05 Nm. The robot was commanded to follow the trajectory in freespace with a constant angular velocity. An object was placed on this path so thatthe second link would contact it. The joint torque was recorded for the fourth joint
while contacting with object. Figure 22a–e shows experimental results when thelink comes into contact with objects having different stiffness varying from a very
low rate to a very high, i.e., a piece of sponge, rubber sponge, rubber, chemicalwood and aluminum, respectively. The time derivative of the torque during impact
is given in Fig. 22f.
It is apparent from the plots presented in Fig. 22 that the stiffer the contacting
object, the smaller the angle of robot joint rotation. The elastic deformation of theobject and inherent compliance of robot joints lead to rotation of the robot arm by
angle θi during contact transience (Fig. 23).
The proposed method of estimation of contacting object stiffness is based on
the principles of solid mechanics for an elastic linear material model. It includes
the following stages: (i) calculation of the total elastic deformation of robot linkwith object δi , (ii) estimation of the total stiffness of a robot link–object pair ki ,and (iii) computation of the object stiffness ko from the given values of the robot
link stiffness and total stiffness. The given data are contact point coordinates PCi(derived from (19) and (20)), the changes in joint angle θi and joint torque valueτi while colliding (θi and τi are derived from Fig. 22).
The distance that contact point C on the robot link surface 1 moves perpendicularly to the radius ri under torque τi , is equal to ri θi (since the angle θi
is fairly small). The unknown angle ϕi can be found by taking into account that
 C CB =  OCA = ϕi :




hi
OA
= arctan,(29)
ϕi = arctanCA
PCi
where hi equals half of the robot link thickness.
The total elastic deformation is calculated from the right triangle CBC as:
δi = θi ri cos(ϕi ).

(30)

D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

(a)

(b)

(c)

(d)

(e)

(f)

1349

Figure 22. Experimental results of stiffness estimation.

The radius of the contact point trajectory ri is found as ri =consideration of the right triangle CAO.



PC2i + h2i through1350

D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

Figure 23. Scheme for elastic deformation calculation.
Table 2.
Findings from the experimental diagrams
Parameter

SpongeRubber sponge

RubberWood

θi 10−4 (rad)τi 10−2 (Nm)

39.53
2.98

10.82
3.0

4.12
2.79

3.84
2.92

Now, we can easily calculate the total stiffness:
τi
Fni
τi

=
ki =
=
. (31)
δi
PCi θi ri cos(ϕi ) P θ P 2 + h2 cos(arctan(h /P ))
Ci
i
i
C
i
i
Ci
By linear approximation, we take into account only two values of θi for τi nearest
to 0.02 and 0.05 Nm. Then, from the experimental diagrams (Fig. 22a–e) we obtain
θi , and τi and list them in Table 2.
The total elastic deformation δi is made up of elastic deformation caused by
object compliance δo and that generated by joint flexibility δri . That is, we canwrite:Fni
Rni Fni= δo + δri =
+
,
(32)
δi =
ki
ko
kri
where Rni is the reaction, the absolute value of which equals Fni ; ko and kri are thestiffness of the object and the stiffness of the robot link, respectively.
The coefficient kri is mainly defined by torque sensor stiffness, harmonic drive
stiffness, structural flexibility and P-gain magnitude. Detailed examination has
showed that a complex theoretical model of robot link stiffness can hardly provide
accurate estimation of kri . Therefore, we can set the value of robot link stiffness

D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

1351

Table 3.
Total stiffness and object stiffness
Parameter

SpongeRubber sponge

RubberWoodki (N/m)ko (N/m)

260.84282.04959.3
1325.92

2343.787223.94

2469.04
8562.90

close to the total stiffness in the hardest contact case. This assumption is valid
because during impact with a hard environment, such as the aluminum plate, thecontact deformation of the object is too small to be accounted for (ko ≈ ∞). Thus,
using (29)–(31) and the data presented in Fig. 22a–e we derive the unknown valueof kri :0.0293 Nm
N= 3468.6 .
−42m
0.175 · 2.758 · 10 · 0.1776 · 0.986 m
The stiffness of the objects is calculated from:
kri =

ko =

kri · ki,
kri − ki(33)

where ki is defined by (31). Derived values of the total stiffness ki and object stiffness ko are given in Table 3.
The obtained results demonstrate a strong correspondence of correlation among
the calculated object stiffness with that of real objects. Naturally, the actual stiffness of the colliding environment differs from the calculated one with finite error.
To achieve high accuracy, specific equipment is needed. However, our aim was only
assessment of the danger level of robot arm collision with an object. Specifically,
we can define that sponge and rubber sponge material are safe for interaction, butrubber, wood and metal pose a threat while striking the robot arm. Consequently,
we have succeeded in solving the main task — object classification by getting information about stiffness during impact.
In the case when the collision must be detected robustly without considerationof object properties, the value of the time derivative of torque can be used to judge
the impact value (Fig. 22f).
4.7. Experimental Results of Obstacle Avoidance Through Haptic Interaction andAccomplishment of Human Motion Following
The still images of the robot arm motion video are given in Fig. 24.
The robot arm is controlled by the program on a Dell Precision computer (CPU:Intel Pentium, 3.2 GHz, memory 2 GB). The results show that the robot arm slides
over the obstacle surface (black object on the table) smoothly and maintains constant physical contact with it till the end-effector reaches the target point. The speedof the obstacle avoidance can be adjusted online depending on the object properties

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

Figure 24. Experiment of obstacle avoidance through whole-arm tactile interaction.

Figure 25. Experiment of human-following motion.

and shape. For example, a shape composed of edges and vertices can be handled
faster than one with curve contour.
The remarkable opportunity of ensuring safe robot–environment interaction isthat we can establish different dynamic parameters of the robot arm contacting withthe environment according to the calculated object stiffness (by using the method
described in Section 4.6). This is an inherent capability of humans, since when wecollide with stiff objects we tend to soften our muscles as much as we can.
In order to verify the ability of the developed robot arm iSoRA to safely interact with human beings along the entire surface and to perform cooperative tasks
through human-following motion, the experiment was conducted. In the first stageof interaction, force was applied to the upper arm of the humanoid robot arm. The
robot arm generates compliant motion according to the applied force value andcomes to the position shown in Fig. 25 (left). Then, an additional force was appliedD. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

1353

to the forearm of iSoRA. Since the net torque has a counter-clockwise direction at
the pitch joint of the shoulder, the robot arm generates continuous compliant motion
upward (Fig. 25, right).
It can be seen from Fig. 25 that the elaborated intelligent joint admittance control
enables the human cooperating with the service robot to control the position andorientation of the robot arm through haptic interaction along the entire robot arm
surface. The robot successfully recognizes the human-following task and generates
compliant motion in a safe manner.
4.8. Application of a Robot with a Tactile-Sensitive Robot Arm
The proposed concept opens a wide range of potential applications in service robotics and teleoperation. Here, we discuss other possible applications of the robot
arm with whole-arm tactile sensing ability.
The obstacle avoidance presents significant issues for urban search and rescue
robots. Such robots aim at assisting rescue workers in the investigation of unreachable areas, such as small cracks and pipes, or unsafe places by delivering visual
information from cameras at the end-effector. Controllable compliance of the robot
arm joint can be extremely useful in this application, since it provides whole-arm
tactile interaction with the environment. Therefore, such type of manipulator has
significant capabilities for performance improvement in comparison with conventional manipulators in the field of obstacle avoidance. We summarize possible casesof application of the robot enabling intelligent tactile obstacle avoidance as follows:(i) A robot arm finds the path through interaction with obstacles in extremely
cramped environments (Fig. 18).
(ii) A robot reaches for a visible object located inside a narrow space. The robot
arm contacts and follows the surface of the obstacle represented by the sidesof the box to fulfill the task (Fig. 26).
(iii) A robot reaches for an object located beyond the robot camera visibility range.
In such cases the robot arm needs to be equipped with additional cameras inorder to process the collision softly on its own (Fig. 27).

Figure 26. Robot reaches the object located in narrow space.

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

Figure 27. Robot reaches the object located beyond visibility range.

Figure 28. Robot investigates the indoor environment.

(iv) A robot investigates the indoor area to achieve situational awareness without
the necessity of its bulky body entering the room. When the situation is assessed, the robot body can elude the jamb and enter into the room carefully(Fig. 28).
(v) A robot structures the environment. A vision system of conventional robots
cannot determine the distance to the object in three-dimensional space. By tactile interaction, the robot can precisely define the object location and reachable
working area.
The developed robot and proposed algorithms can enable a service robot not only
to work in a dynamic unstructured environment, but also to cooperate with humans
in a safe manner and to assist rescue workers in the investigation of unreachable
places.

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1355

5. Conclusions and Future WorkA new whole-sensitive robot arm, iSoRA, was developed to provide human-like capabilities of performing contact tasks in a broad variety of environments. Each joint
was facilitated with a high-performance optical torque sensor. Intelligent variable
admittance control and reflex action-based impact control were elaborated to realize
safe, smooth and natural human–robot interaction. We introduced the exponentialfunctional dependency between the sensed force and stiffness to impart human-like
damping and stiffness behavior to the robot arm. Experimental results of impact revealed that large contact forces, mainly used as criteria of collisions, do not indicate
the impact state, while the time derivative of the force value does. The magnitude oftime derivative of torque was used as an indicator of the collision state. To realize reflexive human-like behavior, the stiffness of the impedance model of the robot arm
was reduced drastically during the collision transient. This was achieved by meansof the reciprocal functional dependency of stiffness on the value of the time derivative of the torque. The effectiveness of the controllers was experimentally illustrated
on the whole-sensitive robot arm. The conventionally impedance-controlled robot
can provide contacting task only at the tip of the end-effector with predetermined
dynamics. By contrast, approaches developed by us provide delicate continuous
safe interaction of all surface of the robot arm with the environment.
The proposed concept of obstacle avoidance through whole-arm tactile interaction enables the manipulator to adapt the planned motion to the obstacle shape. Suchan algorithm is especially valuable for service robots working in real indoor environments. Tactile interaction gains the essential information about the contacting
object (shape, stiffness, location, etc.). Numerical simulation revealed that the described method for object shape recognition based on the B-spline approximation
provides high accuracy of contour approximation. The method of object stiffness
estimation is presented and verified. The experimental results presented show thevalidity and robustness of the proposed concept. Our approach can also be extended
for object shape recognition and motion planning in three-dimensional space.
The subject of ongoing work is to adapt the dynamic parameters of the robot
to the surface properties (stiffness, friction coefficient) of the contacting object.
Object mobility and object fixation will be examined by observing the external
force pattern during physical interaction.
Acknowledgements
The research is supported in part by a Japan Society for the Promotion of Science
Postdoctoral Fellowship for Foreign Scholars.
References
1. T. Lozano-Perez, A simple motion-planning algorithm for general robot manipulators, IEEE J.
Robotics Automat. 3, 224–238 (1987).

1356

D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

2. T. Yoshikawa, M. Koeda and H. Fujimoto, Shape recognition and grasping by robotic hands with
soft fingers and omnidirectional camera, in: Proc. IEEE Int. Conf. on Robotics and Automation,
Pasadena, CA, pp. 299–304 (2008).
3. V. J. Lumelsky, M. S. Shur and S. Wagner, Sensitive skin, IEEE Sensors J. 1, 41–51 (2001).
4. V. Lumelsky and E. Cheung, Towards safe real-time robot teleoperation: automatic wholesensitive arm collision avoidance frees the operator for global control, in: Proc. IEEE Int. Conf.
on Robotics and Automation, Sacramento, pp. 797–802 (1991).
5. J. T. Feddema and J. L. Novak, Whole arm obstacle avoidance for teleoperated robot, in: Proc.
IEEE Int. Conf. on Robotics and Automation, San Diego, CA, pp. 3303–3309 (1994).
6. S. Morikawa, T. Senoo, A. Namiki and M. Ishikawa, Real-time collision avoidance using a robot manipulator with light-weight small high-speed vision system, in: Proc. IEEE Int. Conf. on
Robotics and Automation, Rome, pp. 794–799 (2007).
7. F. Caccavael, C. Natale, B. Siciliano and L. Villani, Six-DOF impedance control based on angle/axis representation, IEEE Trans. Robotics Automat. 15, 289–300 (1999).
8. H. Iwata, S. Kobashi, T. Aono and S. Sugano, Design of anthropomorphic 4-DOF tactile interaction manipulator with passive joints, in: Proc. IEEE/RSJ Int. Conf. on Intelligent Robots andSystems, Edmonton, pp. 1785–1790 (2005).
9. A. Bicchi and G. Tonietti, Fast and soft arm tactics: dealing with the safety-performance trade-off
in robot arms design and control, IEEE Robotics Automat. Mag. 11, 22–33 (2004).
10. G. Hirzinger, A. Albu-Schaffer, M. Hahnle, I. Schaefer and N. Sporer, On a new generation of
torque controlled light-weight robots, in: Proc. IEEE Int. Conf. on Robotics and Automation,
Seoul, pp. 3356–3363 (2001).
11. N. Mitsunaga, T. Miyashita, H. Ishiguro, K. Kogure and N. Hagita, Robovie IV: a communication
robot interacting with people daily in an office, in: Proc. IEEE/RSJ Int. Conf. Intelligent Robots
and Systems, Beijing, pp. 5066–5072 (2006).
12. D. Tsetserukou, R. Tadakuma, H. Kajimoto and S. Tachi, Optical torque sensors for local impedance control realization of an anthropomorphic robot arm, J. Robotics Mechatron. 18, 121–130(2006).
13. D. Tsetserukou and S. Tachi, Torque sensors for robot joint control, in: Sensors, Focus on Tactile,
Force and Stress Sensors, J. G. Rocha and S. Lanceros-Mendes (Eds), pp. 15–36. Austria (2008).
14. N. Hogan, Impedance control: an approach to manipulation, Part I–III, ASME J. of Dyn. Syst.
Meas. Control. 107, 1–23 (1985).
15. R. Carelli and R. Kelly, Adaptive impedance/force controller for robot manipulators, IEEE Trans.
Automatic Control. 36, 967–972 (1991).
16. R. V. Dubey, T. F. Chang and S. E. Everett, Variable damping impedance control of a bilateral
telerobotic system, IEEE Control Syst. Mag. 17, 37–44 (1997).
17. T. Tsumugiwa, R. Yokogawa and K. Hara, Variable impedance control based on estimation ofhuman arm stiffness for human–robot cooperative calligraphic task, in: Proc. IEEE Int. Conf.
Robotics and Automation, Washington, DC, pp. 644–650 (2002).
18. D. P. Ferris and C. T. Farley, Interaction of leg stiffness and surface stiffness during human hopping, J. Appl. Physiol. 82, 15–22 (1997).
19. M. M. Rahman, R. Ikeura and K. Muzutani, Investigating the impedance characteristics of humanarm for development of robot to cooperate with human operators, in: Proc. IEEE Int. Conf. on
Systems, Man and Cybernetics, Tokyo, pp. 676–681 (1999).
20. I. D. Walker, Impact configuration and measures for kinematically redundant and multiple armed
robot system, IEEE Trans. Robotics Automat. 10, 670–683 (1994).

D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

1357

21. R. Volpe and P. Khosla, A theoretical and experimental investigation of impact control for manipulators, Int. J. Robotics Res. 12, 351–365 (1993).
22. J. Bauer, Mobile robot with tactile arm used for exploration and pushing tasks, in: Proc. IEEE/RSJ
Int. Conf. on Intelligent Robots and Systems, Osaka, pp. 560–567 (1996).
23. A. M. Okamura, M. L. Turner and M. R. Cutkosky, Haptic exploration of objects with rollingand sliding, in: Proc. IEEE Int. Conf. on Robotics and Automation, Albuquerque, pp. 2485–2490(1997).
24. S. Caselli, C. Magnaninni, F. Zanichelli and E. Caraffi, Efficient exploration and recognition of
convex objects based on haptic perception, in: Proc. IEEE Int. Conf. on Robotics and Automation,
Minneapolis, MN, pp. 3508–3515 (1996).
25. A. S. Rao and K. Y. Goldberg, Shape from diameter: recognizing polygonal parts with a paralleljaw gripper, Int. J. Robotics Res. 13, 16–37 (1994).
26. M. Kaneko, H. Maekawa and K. Tanie, Active tactile sensing by robotic fingers based onminimum-external-sensor-realization, in: Proc. IEEE Int. Conf. on Robotics and Automation,
Nice, pp. 1289–1294 (1992).
27. R. L. Klatzky and S. Lederman, Intelligent exploration by the human hand, in: Dexterous Robot Manipulation, S. T. Venkataraman and T. Iberall (Eds), pp. 66–81. Springer, New York, NY(1990).
28. F. Yamaguchi, Curves and Surfaces in Computer Aided Geometric Design. Springer-Verlag,
Berlin (1988).

About the Authors
Dzmitry Tsetserukou received the BS degree with honors in Electrical Engineering from the Mogilev Machine Building Institute, Belarus, in 1999, the MS degree
in Mechanical Engineering from the National Academy of Sciences of Belarus, in
2002, and the PhD degree in Information Science and Technology from the University of Tokyo, Japan, in 2007. From 2002 to 2004, he was a Research Associate
in the scientific research group at the National Academy of Sciences of Belarus.
He is currently a JSPS Post-Doctoral Fellow in the Department of Information
Physics and Computing at the University of Tokyo, Japan. He is a member of theIEEE and the author of over 50 technical publications, three patents, a book chapter and a book. His
research interests include humanoid robots, force/torque sensors, impedance and admittance control,
teleoperation, haptic interfaces and displays, and affective haptics.
Naoki Kawakami received the MS and PhD degrees from the University ofTokyo, Japan, in 1996 and 1999, respectively. He is currently a Lecturer in the Department of Information Physics and Computing at the University of Tokyo. His
research interests include virtual reality, real-time remote robotics, telexistence
and retro-reflective projection technology. He is the author of over 150 technicalpublications.

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D. Tsetserukou et al. / Advanced Robotics 23 (2009) 1327–1358

Susumu Tachi received his BS, MS and PhD degrees from the University ofTokyo, in 1968, 1970 and 1973, respectively. From 1975 to 1989, he was a Director of the Biorobotics Division at the Mechanical Engineering Laboratory, Ministry of International Trade and Industry, Tsukuba Science City, Japan. From 1989
to 2009, he was a Professor in the Department of Information Physics and Computing, University of Tokyo. He is currently a Professor in the Graduate School ofMedia Design, Keio University, Japan. He is a Founding Director of the Robotic
Society of Japan and a Founding President of the Virtual Reality Society of Japan.
From 1988, he has served as a Chairman of the IMEKO (International Measurement Confederation)
Technical Committee on Measurement in Robotics. He is a member of IEEE VR Steering Committee,
and has served as a General Chair of IEEE VR 2001 and 2002. He was responsible for the following
research projects: Guide Dog Robot (1976–1983), National Large Scale Project on Advanced Robotics in Hazardous Environment (1983–1990), National Large Scale Project on Humanoids and Human
Friendly Robotics (1998–2003), and CREST Project on Telexistence Communication Systems (2000–
2005). He is the author of over 500 technical publications. His research interests include virtual reality,
real-time remote robotics, telexistence and retro-reflective projection technology.