IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 20181727

High-Speed and Lightweight Humanoid Robot Arm
for a Skillful Badminton Robot
Shotaro Mori

, Kazutoshi Tanaka, Satoshi Nishikawa, Ryuma Niiyama, and Yasuo Kuniyoshi

Abstract—Sports, especially badminton, require participants toperform dynamic and skillful motions. Previous robots have had
difficulty in performing like a human because of their severe limitations of low operating speed, heavy bodies, and simplistic mechanisms. In this letter, we propose a new robot design that consistsof a structure integrated with pneumatic actuators and noninterfering many-degree-of-freedom joints, for the realization of ahigh-speed and lightweight humanoid robot. We made a fourdegree-of-freedom robot arm for badminton, which is an especiallydynamic sport, aiming for maximum speed while meeting geometric requirements. The robot swung with a racket-head speed of
21 m/s, which is a value higher than speeds achieved by previous robotic arms. The robot also realized a skillful shot, namely
the spin net shot, which cannot be performed by previous badminton robots having simple mechanisms. A pneumatic robot is
considered difficult to control, especially in terms of feedback control. We found that the reproducibility of the robot was as fine as
10–40 mm at the racket head for four kinds of strong swings. Using
feedforward control, we also conducted an experiment in which
the robot hits a flying shuttle, and achieved a high hitting rate of
69.7% for powerful swings. We believe that this research expands
the possibilities of the pneumatic robot and is the first step toward
developing a skillful humanoid badminton robot.
Index Terms—Hydraulic/pneumatic actuators, mechanism
design, tendon/wire mechanism, humanoid robots, motion control.

I. INTRODUCTIONOBOTS have been developed for various sports, such assoccer [1], table tennis [2], and badminton [3]–[6]. Suchrobots can entertain people through their play and are a subjectof important research themes, such as artificial intelligence,robotics, and machine learning. With technological advances,robots will become useful in events and as practice partners for
professional players. However, conventional sports robots arestill not comparable to humans, because the emphasis of their

R

Manuscript received September 10, 2017; accepted January 14, 2018. Dateof publication February 7, 2018; date of current version February 27, 2018. Thisletter was recommended for publication by Associate Editor S. Briot and Editor
P. Rocco upon evaluation of the reviewers’ comments. This work was supportedby the JSPS under a Grant-in-Aid for Scientific Research (A) 26240039 anda Grant-in-Aid for Young Scientists (A) 15H05320. (Corresponding author:
Shotaro Mori.)
The authors are with the Department of Mechano-Informatics, Graduate
School of Information Science and Technology, The University of Tokyo, Tokyo
113-8656, Japan (e-mail: s-mori@isi.imi.i.u-tokyo.ac.jp; tanaka@isi.imi.i.
u-tokyo.ac.jp; nisikawa@isi.imi.i.u-tokyo.ac.jp; niiyama@isi.imi.i.u-tokyo.ac.
jp; kuniyosh@isi.imi.i.u-tokyo.ac.jp).
This letter has supplementary downloadable material available at
http://ieeexplore.ieee.org, provided by the authors. The Supplementary Materials contain a video in which the brief explanation of our paper and the movementof our robot are shown. This material is 9.19 MB in size.
Digital Object Identifier 10.1109/LRA.2018.2803207

Fig. 1.

Overview of the developed robot.

development has mostly been on ensuring the game proceeds
even though played clumsily. However, human sports in general
require high-speed operation and dexterous action, such as thatwhen hitting a ball in a certain direction in a ball sport, making
a fast and skillful attack or avoiding an attack in a fighting sport,
or executing a feint.
The present study focuses on badminton as an especiallychallenging sport for robots demanding dynamic and skillful
motions. Badminton is known to be a high-speed game. For
example, the initial velocity of the smash is the highest velocity
among all ball sports [7]. In addition, badminton has skillfulshots that use unique characteristics, such as the lightweightbody and complex shape, of the shuttle [8], [9]. Badminton
robots have already been developed; e.g., wheeled robots thathave participated in a university competition [3], [5], [6] anda railed robot [4]. These robots realized continuous rallies but
had only a simple mechanism with few degrees of freedom(DoFs), and they therefore could not perform the varied andskillful shots that humans use. Such shots are effective in termsof deceiving opponents, often determine who wins a match, andexcite spectators.
A humanoid sports robot has advantages in that the opponent’s motion is important in interpersonal sports; e.g., execut-

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1728

ing feints and predicting the opponents next move are important
aspects of sport. The high number of DoFs of the humanoid isalso effective for the execution of skillful shots as mentioned
above.
We therefore consider three requirements of next-generation
badminton robots: high speed, low weight, and a humanoid
form. Naturally, these requirements relate also to other sports.
However, there are many difficulties in realizing all theserequirements.
High-speed manipulation is an important theme for general
robotic arms. Kawamura et al. developed FALCON [10], which
realized acceleration of 43 [G] at the end effector (320 [g])via a parallel-link-wire driving mechanism. However, the maximum speed was no more than 13 [m/s]. Its size is about
1.45 [m] × 1.45 [m] × 1.25 [m]. As a humanoid robot arm,
WAM Arm is a well-known high-speed serial-link-type robot,
having actuators at the base of a link and employing a wire driving system. Senoo et al. realized high-speed batting using WAM
Arm [11] with a reach of 1000 [mm], but the maximum speed
was no more than 6 [m/s] (and is officially limited to 3 [m/s]).
Büchler et al. developed Lightweight Robotic Arm [12]. They
placed all pneumatic artificial muscles on the base and made thearm itself lightweight (700 [g]), and realized a speed of 12 [m/s]at the end effector for pneumatic pressure of 0.3 [MPa] and angular velocity of 1500 [deg/s] (26.18 [rad/s]). Fast robotic arms
have thus been developed using light arms. However, the overallbodies are heavy because the robots have been developed to bestationary; e.g., WAM Arm has a mass of 27 [kg] [13]. Future
sports robots should be able not only to swing quickly but also tomake fast whole-body movements. In addition, developed robots
do not have anthropomorphic joints such as a high-DoF wrist.
Against the above background, as a high-speed andlightweight humanoid robot that can play badminton, we
propose a humanoid robot that is actuated by a wire-driven
pneumatic cylinder integrated in the structure. As an example,
we make a robot with four DoFs from the elbow to hand. Wethen evaluate the robot and test its ability to return a badminton
shuttle.
II. PROPOSED HIGH-SPEED AND LIGHTWEIGHT HUMANOIDROBOT SYSTEM
A. Requirements of the Robot
According to the above discussion, the requirements of ahumanoid badminton robot are summarized as the high-speed
swing of a human, the anatomical shape of a human, and agile
whole-body motion. However, as a minimum setup in the firststep of development of such a robot, we made and evaluateda simple four-DoF robot arm. The robot arm comprises only
the arm below of the elbow but includes important components
for future extension: a three-DoF joint and linking. We focusedon the high-speed wrist, which is the fastest-moving joint in thehuman body and said to be important in a badminton smash [14].
The requirements of the robot arm are then described as follows.
r High-speed wrist as for humans (26.3 [rad/s] [14]);
r The anatomical shape of a human, having DoFs, link
lengths, and link diameters similar to those of a human;

IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Fig. 2.

DoF diagram of the developed robot.

r Low overall weight and volume for extensibility to an agile
whole-body robot.
B. Overview of the Robot
The selection of actuators is most important in realizing sucha robot. We employed pneumatic actuators. Among other commonly used actuators, an electromagnetic actuator has a quick
response and high controllability. However, its speed is strictly
limited by its back electromotive force and its power-to-weightratio is low because of its heavy coil and magnet. A hydraulicactuator has a high power-to-weight ratio for an industrial machine, but it needs other heavy apparatus such as pumps andmotors. Moreover, its speed is limited because of high oil viscosity. Meanwhile, a pneumatic actuator has a much higher
power-to-weight ratio than other actuators on the whole, andcan realize high-speed motion. One disadvantage of the pneumatic actuator is the requirement of an air compressor. However,there are lightweight compressors available, such as MAX-JET
manufactured by NIKKEN (5 [kg], 31 [L/min] at 0.8 [MPa]).
Otherwise, it is also possible for the sports robot to be equippedwith tanks and for compressed air to be supplied from outside
the court through a thin tube. Additionally, a slow response
can be a serious problem for the feedback control, but we willpropose a feedforward control method that is suitable for thehigh-speed motion of a pneumatic robot.
The robot (Fig. 1) has a one-DoF elbow and a three-DoF
wrist. The robot reproduces the anatomical shape and DoFs ofa human. In this letter, we refer to the DoFs of medial rotation/external rotation (elbow joint), pronation/supination (wrist
PS joint), palmar flexion/dorsal flexion (wrist PD joint), andulnar flexion/radial flexion (wrist UR joint). These are shownin Fig. 2. The ranges of joint angles are approximately simulated as those of a human; the UR joint is at 90 [deg] while
the other joints are at 180 [deg]. The masses of different parts,
including proportional-pressure-regulating valves (Tecno basic,
Hoerbiger Corp.) and air tanks (550 [ml]), are low as shown in
Table I. The air compressor is placed outside and continuously
supplies air to the robot through a tube. Each joint is equippedwith a potentiometer to measure the joint angle. The workspaceof the robot is shown in Fig. 4.
C. Mechanical Design
The lightweight links of arms are essential for a high-speed
swing. Inspired by the Structure Integrated Pneumatic Cable

MORI et al.: HIGH-SPEED AND LIGHTWEIGHT HUMANOID ROBOT ARM FOR A SKILLFUL BADMINTON ROBOT

Fig. 3.

1729

Wiring diagram of the three-DoF wrist.
TABLE IMASS AND LENGTH OF EACH PART OF THE ROBOT

Section

Mass

Note

Upper arm
Forearm
Hand 1
Hand 2
Hand 3Air valve
Air tank

453 [g]1063 [g]187 [g]
54 [g]
254 [g]144 [g] × 9
113 [g] × 4

link#0, Fixed on the baselink#1, 363 [mm]link#2, 63 [mm]link#3, 44 [mm]link#4, 584 [mm], incl. a racket(96 [g])Fixed on the base
Fixed on the base, 550 [ml]Fig. 6.

Configuration of the robot arm.

using a conventional SIPC cylinder as in Fig. 5. We will propose
a method of configuring an anthropomorphic robot by placing
a plurality of SIPC cylinders on one link and construct multiDoF joints (Fig. 6). As a precaution when designing multi-DoF
joints, it is necessary to devise wire routes so that the wires do notslacken for each posture. We routed wires through relay points
on each joint axis to avoid such slackening, and successfully
realized a three-DoF wrist (Fig. 3). A link length and diameter
similar to those of humans were realized by this design. An
entire arm with a shoulder, or a whole-body robot having legs,
can be constructed by connecting a plurality of the structures.
D. Determination of Design Parameters

Fig. 4. Workspace of the robot. The workspace is approximated as a thin
elliptical surface, although part of the space behind the robot is not covered. The
lengths of the major axes are approximately x = 1342 [mm], y = 1242 [mm],and z = 1116 [mm].

Fig. 5. Simplified robot having an SIPC cylinder and five important design
parameters.

(SIPC) cylinder [15], we integrated pneumatic cylinders into
links as shown in Fig. 5. Because actuators are integrated in
the structure and employ cables instead of the heavy rods usedin normal air cylinders, the robot has a lightweight arm andoperates with high acceleration and high speed. However, it isdifficult to construct an anthropomorphic high-DoF robot simply

There are trade-off relationships of, for example, the weight,
obtained velocity, and specifications of the servo valve. Here,the angular velocity at each joint is especially important, but theangular velocity cannot be simply calculated for a pneumatic
actuator. We will show how to calculate the angular velocity
at each joint through modeling and simulation and determine
design parameters.
There are five important design parameters as shown in Fig. 5,
namely the cross-sectional area of the inner cylinder A[m2 ], thepulley radius of the joint r[m], the cylinder stroke L [m], themaximum angle of motion θm ax [rad], and the reduction ratiok[−]. The reducer is not implemented here but can be helpfulin adjusting parameters to satisfy geometric constraints, such as
an anthropomorphic arm length.
First, krθ̇(t) = ẋ(t) and krθm ax = L should be satisfied,
where θ(t) [rad] is a joint angle and x(t) [m] is a piston position. According to the equation of motion, θ(t), x(t), and theforce produced by the actuator f (t) [N] areI θ̈(t) = μkrF (t) − τc sgn θ̇(t) ,(1)mẍ(t) = f (t) − F (t) − (fp sgn (ẋ(t)) + fpv ẋ(t)) ,(2)

f (t) = (P+ (t) − P− (t)) A.

(3)

For our robot, (P+ (t) − P− (t))A  fp sgn(ẋ(t)) + fv ẋ(t) andI  μmk 2 r2 are satisfied when a robot swings under full power.

1730

IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Then, approximately speaking,μkrA {P+ (t) − P− (t)} − τc sgn θ̇(t)(4)θ̈(t) ≈
I
is satisfied from (1)–(3), where P+ [Pa] and P− [Pa] denote thepressure of each chamber in a cylinder, τc [N·m] is the static
friction torque, fp [N] is the static friction in a piston, fpv [N·s/m] is the coefficient of viscous friction in the piston, m[kg]is the mass of the piston, and I[kg · m/s] is the moment ofinertia around the joint. Equation (4) suggests that the robot canincrease speed if k, r, and A are set large. However, when arobot operates at high speed, the air pressure decreases owing
to an insufficient air flow rate and the obtained speed decreases.
This can be considered by modeling the air. By differentiating
the state equation of gas, we obtainṖ =ρQRT − krAθ̇(t)P
krAθ̇(t) + V0,

(5)where P [Pa] is the pressure in a chamber, ρ[kg/m3 ] is the densityof air, R[J/kg · K] is the gas constant, T [K] is the temperatureof the gas, and V0 [m3 ] is the initial volume of the chamber. The
air flow rate Q[m3 /s] is described as

⎧
κ+1
2
⎪
κ
S Pu p⎪Plow κ
Plow
2κ 1
⎪
−
⎪
ρ
κ−1 R T
Pu p
Pu p
⎪
⎪
⎪
⎪
⎪

⎪
κ
⎪
κ −1
⎪
Plow
2
⎪
<
<
1
⎨
κ+1
Pu p
Q=
, (6)

⎪
κ+1
⎪
⎪
κ
−1
S
P
up
κ
2
⎪
⎪
⎪
ρ
RT
κ+1
⎪
⎪
⎪

⎪
κ
⎪
⎪
κ −1
⎪
Plow
2
⎩
P u p ≤ κ+1
where κ[−] is the heat capacity ratio of the air and S[m2 ] is theeffective sectional area in the flow channel [16]. By reference
to the commanded pressure Pd [Pa] and pressure in the chamber
P , we define an upstream pressure Pup [ Pa] and a downstreampressure Plow [ Pa] asPs (P ≤ Pd )P (P ≤ Pd )Pup =, Plow =, (7)P (P > Pd )PA (P > Pd )where Ps [Pa] is the pressure of a source of air and PA [ Pa] isthe atmospheric pressure. S[m2 ] is controlled by a valve. We
conducted an experiment to estimate the control law:⎧
Sm ax
(Pth ≤ Pd − P )⎪⎪⎪⎪⎨ Sm ax P d −P
(0 ≤ Pd − P < Pth )
Pth
S=
, (8)
P
−P
⎪
−Sm axout P t h d (−Pth ≤ Pd − P < 0)⎪⎪⎪⎩
(Pd − P < 0)
−Sm axout
which means S is basically a constant value Sm ax [m2 ], and whenthe error in P and Pd becomes less than a threshold Pth [ Pa],
the valve employs proportional control such that P converges
to Pd .
The numerical calculation of (1)–(3) and (5)–(8) employing
the Runge–Kutta fourth-order method allows simulation of theswing speed of each joint. In the approximate equation of motion

Fig. 7. Relationship of joint angles and angular velocities in swing simulations
for different values of the reduction rate parameter (k).

(4) and (5)–(8), all five design parameters emerge as krA. It istherefore enough to adjust only krA in designing the swing
speed. An example of the simulation results is shown in Fig. 7
when changing only k in krA and swinging by commanding a
step input of 0.7 [MPa]. It is found that when krA is excessive,
the speed decreases and the size and weight naturally increase.
A valve specification is also important for the maximum velocity, corresponding to the maximum flow rate Sm ax . A higher
value of Sm ax results in a higher maximum velocity at large
krA. A valve specification is difficult to design freely, and it
would thus be better to first fix a valve and determine krA.
Another valve needs to be considered if the speed is excessive
or insufficient.
We designed the parameters of wrist PS and PD joints according to the results of simulation; these joints are important to thehigh-speed swinging of the robot. We decided the parameterssuch that the joint angular velocity was 40 [rad/s] for the wrist
PS joint and 30 [rad/s] for the wrist PD joint at a joint angle ofπ/2 [rad] (where the limit of motion is defined as 0 rad). The
joint angle was based on the posture at the moment of impactof the smash of the shuttle by a human player, and the velocity
was set such that the angular velocity of the wrist joint was
26.3 [rad/s] [14].
III. EVALUATION EXPERIMENTS
A. Evaluation of the Swing Speed
We conducted experiments to confirm the speed generated by
the developed robot. We first commanded 0.7 [MPa] at each joint
and measured the angular velocity. Results are given in Table II.
The speeds of the wrist PD and elbow joints well matchedcalculated values, but those of the wrist PS and UR joint did not.
We believe that friction within a joint having a dry bearing isgreater than expected. Nevertheless, the angular velocities of thewrist PS and PD joints, which contribute most to the badmintonsmash, exceed those of a human, being 26.3 [rad/s] [14]. We
next similarly commanded 0.7 [MPa] at the elbow joint andthe wrist PS joint with appropriate timing, and attempted tomake the robot swing as quickly as possible. A motion capture

MORI et al.: HIGH-SPEED AND LIGHTWEIGHT HUMANOID ROBOT ARM FOR A SKILLFUL BADMINTON ROBOT1731

TABLE IISWING SPEEDS OF EACH JOINT FOR A STEP PRESSURE COMMAND (0.7 [MPA])
WHEN THE JOINT ANGLE IS π/2 [rad] (WHERE CIRCLED NUMBERS ARE
EXPLAINED BY REFERENCE TO FIG. 2)
Joint
Medial rotation (1 , elbow joint)
Ulnar flexion (2 , wrist UR joint)
Palmar flexion (3 , wrist PD joint)
Pronation (4 , wrist PS joint)

Angular velocity [rad/s]
15.7
24.2
29.0
32.4

Fig. 8. Sequential photographs of a normal net shot (left) and a spin net shot(right).

system (VICON624) was used for the measurement. When the
joint angle of the PS joint was approximately 90 [deg], thevelocity of the center of the racket head was about 21 [m/s].
This is much higher than velocities for previously developed fast
robotic arms with the same scale described in Section I, such as
WAM Arm [13] (8 [m/s] [11]), Lightweight Robotic Arm [12]
(12 [m/s], 26.18 [rad/s]), and FALCON [10] (13 [m/s]).
B. Example of a Skillful Shot—Spin Net Shot
Humans can execute skillful shots using a multi-DoF arm,
while a robot can also make such shots when designed as ahumanoid. As an example, we had the robot execute a spin
net shot. This shot is a slicing motion of the racket surface
across the shuttle performed near the net. The shot makes the
shuttle spin and tumble, which in turn makes it difficult for the
opponent to return the shuttle, especially from a high position.
The execution of the shot is a difficult skill even for an expert
player, but the shot is often effective and even a winning shot.
For the robot to make the shot, we first made the robot adopt an
initial posture through proportional-integral-differential (PID)
position control. A shuttle was then dropped from above by avacuum holder, and the robot executed a command preparedin advance. The wrist UR joint was mainly used for slicing,but the wrist PD joint was also needed to return the shuttle
forward. Video imagery showed that the shuttle was spinningand tumbling, and it took longer for its cork to face down than in
the case of the normal net shot. The robot therefore succeededin executing the spin net shot (Fig. 8).
C. Reproducibility
In general, feedback position control is employed for robotics
control, but such control is difficult for pneumatic robots owing to the slow response or high friction. Pneumatic robots
are therefore often said to have poor controllability. However,Fig. 9. Four types of swing used in the reproducibility evaluation and hitting experiment. (a) Initial posture in the reproducibility experiment. (b) Initial
posture in the hitting experiment. (c) Four types of swing.

feedforward control by learning or preliminary simulation canbe a good alternative. To realize feedforward control by a robot,
it is necessary to reproduce the motion using the same input. We
evaluated reproducibility by executing a certain feedforward
command and measured the variance.
The initial posture corresponded to the maximum joint angle
and four types of swing were performed by manual programming (Fig. 9). Each swing was strong; the maximum pressurecommand was 0.65 [MPa], the swing was complete in about
0.10 [s], and the maximum speed of the racket head during theswing was about 11 [m/s] for lower swings and 17 [m/s] for
upper swings. We also had the robot first perform a preparatory motion (i.e., a backswing); the total swing time was about
0.70 [s]. Commands were given to all joints so that no joint
was at its maximum joint angle during the swing. Calculated
standard deviations are presented in Fig. 10.
It was found that, despite the high swing speeds, standard deviations of the racket position were only about 10–40 [mm].
These values are smaller than the racket diameter of about
220 [mm], and we can therefore say that feedforward control isavailable. The standard deviation of the forehand upper swing
was larger than the standard deviations of other swings. Although we do not clearly explain this, the result suggests thatsome motions are relatively sensitive and the reproducibility
differs depending on the command.
IV. HITTING EXPERIMENT
We argued in Section III-C that feedforward control is available for our robot with low controllability. We will show the1732

Fig. 10. Time series of the standard deviation of the center position of theracket head for four types of swing in evaluating reproducibility. The timingof the backswing command was 0.00–0.50 [s] while that of the swinging command was 0.50–0.70 [s]; only the swinging time is shown. The position was
calculated from each joint angle obtained by the potentiometers approximately
every 0.008 [s] and linearly interpolated. There were 12 trials per type of swing.

effectiveness of the control method in a practical experiment of
the hitting of an incoming shuttle. Naturally, it is essential forthe badminton robot to return a shuttle and so the experimentalso shows the extent to which our robot can play a real gameof badminton.
A. Shuttle Trajectory Recognition and Prediction
We used a motion capture system (Prime 13 W x8, Opti
Track, 240 fps) to recognize a flying shuttle. Reflective tape wasput on the cork of the shuttle but had little effect on the flightcharacteristics because the tape was thin and light. Following
previous studies [4], [5], the shuttle position and velocity were
estimated from a measurement time series and the trajectory
was predicted using an aerodynamics model.
B. Motion Training and Selection
We tried the most basic method of feedforward control togenerate hitting motions in the present study. We had the robot
perform various swings via feedforward pressure commands,
and memorize the motions and corresponding commands preliminarily. When a shuttle was incoming, the robot selected an
appropriate command for hitting the shuttle. The motion selection algorithm is presented as Algorithm 1, and the overall
hitting algorithm as Algorithm 2.
We first generated four patterns of swings by manual programming similar to that in the reproducibility experiment(Section III-C, as shown in Fig. 9). For each swing, we first
had the robot take the same initial posture as in the reproducibility experiment according to the motion limit (Fig. 9(a)).
However, to start the swings as early as possible, the robot thenadopted an intermediate initial posture by executing a certain
command for the elbow joint (Fig. 9(b)). We confirmed therobot could well reproduce the initial posture. We generated
37 patterns of swings by slightly changing the commands. For
each swing, an appropriate time range for hitting was extracted,
and the positions of the center of the racket head and the timestamps were recorded (Fig. 11). To return the shuttle forward andIEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Algorithm 1: Command Selecting Algorithm.
Load pretrained swings//Each swing has measurement points//Each point has position Pracket and time tfunction Select Swingtnow
Errors=∅for Each memorized swing do
for Each memorized point do
Pracket ⇐ the racket position at the pointt ⇐ tnow + the time at the point
for Δt in [0.0, Δtm ax ] do//Adjusting swing timing
Errors ⇐ ||Pshuttle (t + Δt) − Pracket ||if Δt = 0.0 at argmin(Errors) thenreturn Command at argmin(Errors), min(Errors)
else
return Command = None, Error = ∞Algorithm 2: Overall Hitting Algorithm.
loop
Taking the initial posturerepeattnow ⇐ current time
Cexe , Error ⇐ SELECT SWINGtnow
if Error < Eth thenExecute the command Cexe
until Command is executed
 Swing start timet0 ⇐ current time
Repeat Command change loop
Cnew ⇐ SELECT SWINGt0 , Δtm ax = 0.0
t1 ⇐ current time
if (Cexe = Cnew ) in [t0 , t1 ] thenChange the command to Cnew
Cexe ⇐ Cnew
until The swing finishes

dynamically, we chose the time range in which the velocity of
the racket head was in the forward direction and the speed wasnear its maximum, which is effective in making a good shot.
The executing command was chosen so that the racket headand shuttle had the closest approach. In other words, when ashuttle was incoming, the position of the shuttle at the time foreach recorded racket head point was predicted, and distanceerrors were calculated. The swing was executed if the minimum
error was less than a certain threshold. The threshold was set
to 100 [mm] with reference to the radius of the racket head.
If there was no appropriate swing, the algorithm proceeded to
the next loop and repeated the same calculation for the new
shuttle position. Additional processes were implemented forbetter performance; the execution of the command was delayed
if the error was expected to be smaller (“Adjusting swing timing”
in Algorithm 1), or the command was changed after the start ofthe swing if there was a better command (“Command changeloop” in Algorithm 2). The trajectory prediction and selecting

MORI et al.: HIGH-SPEED AND LIGHTWEIGHT HUMANOID ROBOT ARM FOR A SKILLFUL BADMINTON ROBOT

Fig. 11. Trajectories of racket heads in trained swings and trajectories ofshuttles served by humans in the hitting experiment. The shuttle trajectories areshown only when the robot swings.
TABLE IIIRESULTS OF THE HITTING EXPERIMENT (WHERE DATA WERE RECORDED ONLY
WHEN THE ROBOT EXECUTED A SWING
Swing type

Hit

Just touching

No hitTotal

Upper forehandUpper backhand
Lower forehand
Lower backhand

46
17
39
65

11
5
7
19

12
9
4
6

69
31
50
90

167(69.7%)42(17.5%)31(12.9%)

240

Total

command were processed by an external computer, and thecommand was then sent to the microcomputer mounted on the
robot by socket communication.
C. Results and Analysis
For the above setup, a human served a shuttle from the same
position, about 5 [m] away, toward the robot and the robot attempted to return the shuttle. Statistical results are presented
in Table III. Although results depended on the type of swing,
the hitting success rate was 69.7% over all trials and complete misses accounted for only 12.9% of trials. Sequential photographs of a strike are shown in Fig. 12. The serves made by
the human varied (Fig. 11), but the result was recorded only
when the robot decide to swing. The results of trials with a large
shuttle position error due to the human were therefore removed.
The human-serving setup of the experiment also shows that the
robot can cope with a certain variety of shots.
Analysis of the results revealed three major reasons why it
was not certain that the robot hit the shuttle: shuttle trajectory
prediction error, measurement and command delay, and mechanical degradation.
In terms of the first reason, differences between the predicted
shuttle positions and the actual shuttle positions can affect the1733

results. The analysis showed that the prediction before about
0.4–0.5 [s] was used, and the average error was 37 [mm] (0.4 [s]before)–45 [mm] (0.5 [s] before). A shuttle was served with aslow and arched trajectory as shown in Figs. 11 and 12, but the
shuttle was more than 2 [m] ahead of the robot at that time.
We employed the flying shuttle model that has been commonlyused in previous studies [5], [6], but it is a simple model that
does not completely express flight characteristics. For example,
it has been reported that the spinning and posture of a shuttle
may affect the flight characteristics of the shuttle [8], [9], but
the effect is not considered in the present model. We have toimprove also the shuttle prediction method in designing a moresophisticated robot system.
In terms of the second reason, analysis of the observation
results of motion capture showed a slight timing shift even for thesame command. Because the sensor data of the robot were notdelayed, the timing shift was due to the delay in communication
between the motion capture and the external computer or theexternal computer and the robot. The delay was 0.015 [s] atmost from the measurement, but because the swings were fast,even a delay of several milliseconds introduced error. We do notdeal with this problem here, but will address it by constructinga system with a shorter communication delay or a system thatcan be well synchronized.
In terms of the third reason, deteriorations can affect the motion although the reproducibility over a short period is good.
Despite training twice again in the middle of the experiment,
the motion gradually changed. We estimate the most critical deterioration would be a friction change of dry bearings in joints
and wire relay points, which are readily worn away. The effectwill be reduced if the dry bearings are replaced with roller bearings, but there could be other deteriorations such as a frictionchange of a piston, a stretching wire, or response change of avalve. The swings are so strong that deterioration of the robot
cannot be completely avoided in long-term operation. The problem can be solved by designing a system where not only is therobot trained in advance but also training results are collectedon line.
In this experiment, the effectiveness of the feedforward control method used by our proposed robot is demonstrated to someextent. We admit the method is basic and not robust enough in its
present form, but it can be extended in the future. The robot will
also become able to return the shuttle with planned direction andpower by considering not only the position of the racket headbut also the velocity or face directions in the command selecting algorithm. However, this requires more training data. Suchcommands can be automatically generated with less training by
employing simulations or a machine learning approach. For example, the movement changes can be predicted when changing
the command without training by the robot model. It is difficult to model a pneumatic robot completely, but learning therobot dynamics model can be helpful [17]. Rather than having acomplete dynamics model, the dynamics of similar swings canbe easily learned. The deterioration problem can also be solved
if the trained trajectories are adjusted by the learned model on
line.

1734

Fig. 12.

IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Sequential photographs of hitting an incoming shuttle (using a upper backhand swing).

V. CONCLUSION
We proposed the concept of a high-speed and lightweight humanoid robot system using an SIPC cylinder for an agile sports
robot especially designed for badminton, and made a forearm
robot as a minimum setup. We successfully implemented a noninterference driven three-DoF wrist by arranging a plurality of
SIPC cylinders on one link and devising ways of tying the wires
while maintaining low weight. Evaluation experiments showedthat the developed robot generates a much higher head speed
during a swing than all previous fast robotic arms as far as
we know. Despite this, the developed robot is lightweight overall because of the use of light and integrated actuators. The
robot can be applied as an agile mobile robot in the future.
The robot also has multiple DoFs similar to those of humans,
and it can thus perform as skillful shots as humans can. As an
example, we demonstrated the spin net shot, which cannot be
performed by previously developed badminton robots having asimple mechanism. A humanoid design also has excellent potential for entertainment or as a sparing partner taking advantage
of its anthropomorphic design and motions.
As another contribution of this research, we expanded thepossibilities of pneumatic robots. Pneumatic actuators are considered difficult to control owing to their slow response andhigh friction. A pneumatic robot is good at high-speed operation, but there have been few studies on robots that make useof the speed. In the present research, we conducted experimentson the accurate and high-speed operation of badminton motion
performed by a pneumatic robot. The examination of reproducibility via a feedforward command showed that high reproducibility can be achieved even for high-speed swings. Using
the result, we conducted an experiment in which the robot attempted to return a shuttle. The task required extremely high accuracy, yet the robot succeeded in returning the shuttle with highprobability.
The robot proposed in this letter is a first step toward thedevelopment of a humanoid badminton robot, and can be extended to a humanoid arm or whole-body humanoid robot withmore DoFs. In addition, our design will be applicable not only
to the badminton robot but also to many kinds of robots that require high-speed motion, especially agile mobile robots having
overall low weight; e.g., other sports robots and autonomous
robots that quickly respond to unexpected events. However, we
also discovered tasks to be tackled, such as those relating tosophistication of the whole system, countermeasures against
deterioration, and the development of new training methods

using simulation or machine learning. We suggested solutions
but these topics are left for future work.
ACKNOWLEDGMENT
The authors would like to thank Glenn Pennycook, MSc, from
Edanz Group (www.edanzediting.com/ac) for editing a draft ofthis manuscript.
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