Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 67–70___________________________________________________________________________________________________________Development of Cloud Action for Seamless Robot
Using Backpropagation Neural Network
Wisanu Jitviriya

Faculty of Engineering, King Mongkut’s University of Technology North Bangkok.
1518 Pracharat 1, Wongsawang Rd., Bangsue, Bangkok, Thailand 10800
wisanu.j@eng.kmutnb.ac.th
Jiraphan Inthiam and Eiji Hayashi

Mechanical Information Science and Technology, Kyushu Institute of Technology.
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
jiraphan@mmcs.mse.kyutech.ac.jp and haya@mse.kyutech.ac.jp

Abstract
This paper presents the cloud action model for a five DOF Seamless robotic arm using the inverse kinematics
solution based on artificial neural network (ANN). Levenberg-Marquardt method is used in training algorithm. The
desired position and orientation of the end effector is defined as the input pattern of neural network. In addition, we
propose the cloud action which is the movement patterns of the robotic arm. The cloud action platform is created inorder to perform the basic behaviors of the Seamless robot such as “Catch”, “Approach”, “Interest”, “Look around”,“Alert” and “Avoid” actions. Experimental results show the suitable structure of artificial neural network used forsolving the inverse kinematics equation, and the testing points in the robot’s workspace were verified with therobotic arm.
Keywords: Inverse kinematics solution, Artificial neural network, Levenberg-Marquardt method.

Consequently, in this paper, we present the cloud
action which is the movement patterns of Seamlessrobotic arms. The cloud action platform is created inorder to perform the behavior of robot smoothly. In
addition, we introduce the performance of the training
algorithm which is Levenberg-Marquardt learningalgorithm [4] that is used for solving the inverse
kinematics problem of the Seamless robotic arms.
This paper is organized as follows: Section 2
provides the kinematic modeling of Seamless roboticarm. Section 3 of the paper explains the neural networkfor determining the inverse kinematics solution. Section
4 describes the process of the cloud action model.
Section 5 shows the experimental results of the
proposed model, and finally, Section 6 concludes thepaper.

1. Introduction
Nowadays, the service robot technology isdeveloping and advancing rapidly. Therefore, ourresearches have concentrated on exploring and studying
an artificial intelligent method to create the behavioral
and emotional models of the autonomous robot [1] - [3].
The new model for our robot is the dynamic behavior
selection model based on emotional states whichdevelops from the Consciousness-Based Architecture(CBA) model combined with Self-Organizing Map(SOM) learning and Markovian model. However, it
seems to be the efficient model of the autonomous robot,
but we face some problems in posturing the behavior
and expressing the emotion of the robot.

Copyright © 2017, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 67–70___________________________________________________________________________________________________________z0

Loffx0The homogeneous transformation matrix ofSeamless robotic left arm can be shown in Equation (2)L1z1 z2 Ө2Base frame

Ө1

Ө3z3

 nx
n0
 y5T = n z 0L2z4
Ө4L3
Ө5End Effectorz5

di-(L1+ Loff)
0L20L3ai00000az

00(2)− cθ5 + 90 (cθ2 + 90 sθ4 − sθ2 + 90cθ3 + 90cθ4 )nx =− sθ2 + 90 sθ3 + 90 sθ5 + 90

αi909090-900− cθ5 + 90 (cθ4 (cθ1 sθ3 + 90 − sθ1 cθ2 + 90cθ3 + 90 ) − sθ1 sθ2 + 90 sθ4 )ny =− sθ5 + 90 (cθ1 cθ3 + 90 + sθ1 cθ2 + 90 sθ3 + 90 )− cθ5 + 90 (cθ4 ( sθ1 sθ3 + 90 + cθ1 cθ2 + 90cθ3 + 90 ) + cθ1 sθ2 + 90 sθ4 )nz =2. Kinematics modeling of Seamless robotic arm=ox− sθ5 + 90 ( sθ1 cθ3 + 90 − cθ1 cθ2 + 90 sθ3 + 90 )sθ5 + 90 (cθ2 + 90 sθ4 − sθ2 + 90cθ3 + 90cθ4 )− sθ2 + 90 sθ3 + 90cθ5 + 90

For this research work, Seamless robot in ourrobotics laboratory is being used shown in Fig.1.
Actually, it has two arms, but in this case, we only
present the left arm for considering and determining theforward and inverse kinematics solutions. DenavitHartenberg (D-H) method [5] is used to derive its
kinematics. Fig.1 shows the joints and links of the left
side of robotic arm, which has the base frame, shoulder,elbow, and wrist. The DH parameters of the robotic arm
are listed in Table 1. According, DH method, thetransformation can be formed in the chain product of
five homogenous matrices i −1iT as expressed byEquation (1), where, sin θi = sθi and cosθi = cθi .

00 1 2 3 4
5T = 1T 2T 3T 4T 5Tozpx p y pz 1 In Equation (2) indicates the elements of rotational oftransformation matrix ( nx , n y , nz , ox , o y , oz , a x , a y , a z )and the elements of position vector ( p x , p y , p z ). Allelements can be solved as follows:

Table 1. The DH parameters for Seamless robotics arm (Left arm)

Өi
Ө1
Ө2+90
Ө3+90
Ө4
Ө5+90ax
ayx5

Fig.1 Seamless robotics arm (Left arm)

Joints12345oxoy=oysθ5 + 90 (cθ4 (cθ1 sθ3 + 90 − sθ1 cθ2 + 90cθ3 + 90 ) − sθ1 sθ2 + 90 sθ4 )− cθ5 + 90 (cθ1 cθ3 + 90 + sθ1 cθ2 + 90 sθ3 + 90 )oz = sθ5 + 90 (cθ4 ( sθ1 sθ3 + 90 + cθ1 cθ2 + 90cθ3 + 90 ) + cθ1 sθ2 + 90 sθ4 )− cθ5 + 90 ( sθ1 cθ3 + 90 − cθ1 cθ2 + 90 sθ3 + 90 )− cθ2 + 90cθ4 − sθ2 + 90cθ3 + 90 sθ4ax =a y = sθ4 (cθ1 sθ3 + 90 − sθ1 cθ2 + 90cθ3 + 90 ) + sθ1 sθ2 + 90cθ4az = sθ4 ( sθ1 sθ3 + 90 + cθ1 cθ2 + 90cθ3 + 90 ) − cθ1 sθ2 + 90cθ4− ( L1 + Loff ) − L2cθ2 + 90 − L3 (cθ2 + 90cθ4 + sθ2 + 90cθ3 + 90 sθ4 )px =p y = L2 sθ1 sθ2 +90+ L3 ( sθ4 (cθ1 sθ3 + 90 − sθ1 cθ2 + 90cθ3 + 90 ) + sθ1 sθ2 + 90cθ4 )pz = − L2cθ1 sθ2 +90(1)+ L3 ( sθ4 ( sθ1 sθ3 +90 + cθ1 cθ2 +90cθ3 +90 ) − cθ1 sθ2 +90cθ4 )68

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 67–70___________________________________________________________________________________________________________Table 2. Results of each Neural Network structure
Actual Joint Angles
Forward kinematics+-Position and Orientationof End effector

Error

Neural Network(LM Backpropagation)
Desired Joint Angles

Fig.2 Neural Network based inverse kinematics solution

Input layer

Hidden layer

Output layer

Σ

Σ

Ө1

ΣΣ

Ө2

Σ

Σ

Ө3

Σ

Ө4

Σ

Ө5

px
pypz
n, o, a(x,y,z)Σ
BiasijBiasojEpochMSE

Regression

30
32
34
36
38
40
42
44

205
145
193
238
276
167
230
254

51.6
51.3
51.2
51.3
51.2
50.9
52.0
52.1

0.99291
0.99293
0.99295
0.99293
0.99295
0.99296
0.99286
0.99284

Fig.4 Simulation of cloud action of Seamless robot

Fig.3 Multi-layered perceptron neural network structure

3. Neural network based the inverse kinematics
solutiondiffers from the existing trajectory paths, because
Seamless robot that is used to perform the suitablebehavior and express the intelligent emotion. Therefore,
Cloud action model is introduced in this paper. The
procedure of the Cloud action can explain by thefollowing steps:
Step 1: Determine the straight line (distance) between
the target object and end effector position.
Step 2: Find the center point of each behavior on thestraight line. Then, the sphere of each action is created,depending on the rate action.
Step 3: Verify the response action for selecting thegroup action.
Step 4: Random the point in the selecting workspace
based on the random distribution technique.

Solving the inverse kinematics problem is one of themost important task in robot kinematics and control.
The complexity of this problem is obtained from therobot’s geometry and the nonlinear trigonometricequations. Therefore, there are three traditional
techniques used for determining inverse kinematics
problem that can be classified as algebraic, geometricand numerical algorithms. These methods are timeconsuming, incorrect initial estimation, the inverse
kinematics cannot be guaranteed and heavy
computational calculation. Consequently, anothermethod can solve the inverse kinematics problems thatis Neural Networks. It is possible to formulate thenonlinear mathematical model. Many researchers haveverified with this approach in several works [6] - [8].
4.

In hidden layer(Hidden node)5.

Experimental results

In this study, Seamless robotic arm was used toverify the movement of the robot based on Cloud action
model. In Fig.2 shows the process of inverse kinematics
solution. Levenberg-Marquardt method (trainlm) fromMATLAB toolbox was used to train and update theweight and bias values, because it is the fastest backCloud action of Seamless robotic arm

After solving the inverse kinematics problem baseon Neural Network, We try to generate the trajectory
path between the target position and the end effector
position. But trajectory path of Seamless robotic arm

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 1 (June 2017) 67–70___________________________________________________________________________________________________________Look aroundInterest

ApproachCatchAlertAvoid

Fig.5 Results of the six actions with cloud action modelmodel), in order to create the intelligent service robot
perfectly.

propagation neural network algorithm (Supervisedlearning). The designed neural network model isillustrated in Fig.3. The Multi-layered perceptron neuralnetwork model consists of 12 node inputs (the position
vector and nine elements of rotation matrix), 5 node
outputs and only one hidden layer. For training andtesting data of neural network that was determined by
the equation of each element in Equation (2). A work
space data set 33880 data. While 80% of the data set
was set as the training data, 10% to validation and 10%
data for testing data. Table 2 shows the experimentresults of the suitable structure of Neural Network based
inverse kinematics solution for Seamless robotic arm.
After modeling the neural network to calculate theinverse kinematics solution, the movement paths ofrobotics arms were verified by the Cloud action method
as shown in Fig. 4 and Fig.5.
6.

References
1. E. Hayashi, T. Yamasaki, and K. Kuroki, “AutonomousBehaviorSystemCombing
MotivationandConsciousness Using Dopamine,” Proc. of 8th Int. IEEE
Symposium on Computational Intelligence in Robotics
and Automation (CIRA 2009), pp. 126-131, 2009.
2. W. Jitviriya, and E. Hayashi, “Analysis of Robotic Arm’sBehavior using Self Organizing Map combined with
Consciousness-Based Architecture Module,” in Proc.
2013 IEEE/SICE International Symposium on System
Integration (SII2013), pp. 533-538, 2013.
3. W. Jitviriya, J. Inthiam, and E. Hayashi, “DynamicBehavior Selection Model based on Emotional States forConbe-I robot,” Journal of Robotics, Networking andArtificial Life (JRNAL), Vol.2, No.4, pp. 247-251, 2016.
4. D. Marquardt, “An algorithm for least-squares estimationof nonlinear parameters,” SIAM Journal on Applied
Mathematics, Vol.11, No.2, 431–441, 1963.
5. Denavit J., and R.S. Hartenberg, “A Kinematic Notation
for Lower- Pair Mechanism Based on Matrices,” ASME
Journal of Applied Mechanics, pp. 215-221, 1955.
6. Guez, A., and Ahmad, Z., “A Solution to the Inverse
Kinematic in Robotics Using Neural Network
Processing,” IJCNN89, Vol. 2, pp. 299-304, 1989.
7. Oyama, E., et.al,, “Inverse Kinematics Learning byModular Architecture Neural Networks with Performance
Prediction Networks,” Proc. IEEE Int. Conf on Robotics
and Automation, pp. 1006-1012, 2001.
8. Nguyen, L., Patel, RV., and Khorasani, K., “NeuralNetwork Architectures for the Forward Kinematics
Problem in Robotics,” IJCNN90, Vol. 3, pp. 393-399,
1990.

Conclusions

In this paper, we propose the new control systemfor designing and developing a 5 DOF revolute robot
arm, using a backpropagation neural network and thecloud action model. Results have shown that theSeamless robot arm can move randomly in theworkspace of the six basic actions. For future work, wewould like to improve this model into the conventional
actionselectionsystem(Consciousness-BasedArchitecture (CBA) model combined with SelfOrganizing Map (SOM) learning and Markovian70