Prosiding (Artikel)

(1)

LEMBAR

HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW KARYA ILMIAH : PROSIDING

Judul Prosiding (Artikel) : Single Product Inventory Control Considering Unknown Demand Using Linear Quadratic Gaussian

Nama/Jumlah Penulis : Sutrisno, Widowati, R. Heru Tjahjana / 3 orang Status Pengusul : penulis ke- 2

Identitas Prosiding : a. Nama Prosiding : International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics )

b. Nomor ISBN : Electronic ISBN: 978-1-5386-6086-7

USB ISBN: 978-1-5386-6085-0

Print ISBN: 978-1-5386-6087-4

c. Volume, nomor, bulan tahun : 2018, Page(s): 17 - 20

d. Penerbit : IEEE

e. DOI artikel (jika ada) : 10.1109/ROBIONETICS.2018.8674677 f. Alamat web Prosiding

URL JURNAL ; https://ieeexplore.ieee.org/xpl/conhome/8672447/proceeding URL ARTIKE : https://ieeexplore.ieee.org/document/8674677

g. Terindeks di :SCOPUS, IEEE Explore Kategori Publikasi Prosiding : Prosiding Internasional Terindeks

(beri pada kategori yang tepat) Prosiding Internasional Prosiding Nasional

Hasil Penilaian Peer Review : Komponen Yang Dinilai

Nilai Reviewer

Nilai Rata-rata Reviewer I Reviewer II

a. Kelengkapan unsur isi prosiding (10%) 2,18 2.34 2.26

b. Ruang lingkup dan kedalaman pembahasan (30%) 7,13 6.25 6.69

c. Kecukupan dan kemutahiran data/informasi dan metodologi (30%)

7,28

5.62 6.45

d. Kelengkapan unsur dan kualitas terbitan/prosiding (30%)

7,00

5.62 6.31

Total = (100%) 23,59 19.83 21.71

Nilai Pengusul = 40% x1/2 4.72 3.96 4.34

Semarang, April 2020

Reviewer 2 Reviewer 1

Prof. Dr. St. Budi Waluya, M.Si Prof. Dr. Basuki Widodo, M.Sc

NIP. 196809071993031002 NIP. 19650506 1989031002

Unit kerja : Matematika FMIPA UNNES Unit kerja : Matematika FSAD ITS

(2)

LEMBAR

USB ISBN: 978-1-5386-6085-0

Print ISBN: 978-1-5386-6087-4

d. Penerbit : IEEE

g. Terindeks di :SCOPUS, IEEE Explore

Kategori Publikasi Prosiding : Prosiding Internasional Terindeks (beri pada kategori yang tepat) Prosiding Internasional

Prosiding Nasional Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimal Prosiding

Nilai Akhir Yang Diperoleh Prosiding

Internasional Terindeks

Prosiding Internasional

Prosiding Nasional

a. Kelengkapan unsur isi prosiding (10%) 2,50 2,18

b. Ruang lingkup dan kedalaman pembahasan (30%)

7,50 7,13

c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

7,50 7,28

d. Kelengkapan unsur dan kualitas terbitan/prosiding (30%)

7,50 7,00

Total = (100%) 25,00 23,59

Nilai Pengusul = 40% x1/2x 23,59 =4.72 Catatan Penilaian artikel oleh Reviewer:

1. Kesesuaian dan kelengkapan unsur isi prosiding :

Penulisan artikel baik dan mengikuti standard penulisan artikel di prosiding International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics), yaitu abstract, Introduction, Result and Discussion (IRaD), Conclusion. Artikel belum memuat Methodology, dan Acknowledgement.

Artikel didukung dengan referensi yang sesuai.

2. Ruang lingkup dan kedalaman pembahasan:

Lingkup bahasan dari artikel ini adalah bidang matematika terapan, khususnya pada bidang sistem dinamis.

Dalam artikel ini dibahas dengan baik tentang linear kuadratik Gaussian (LQG) untuk tujuan kontrol tingkat persediaan produk dari sistem persediaan produk tunggal dengan mempertimbangkan permintaan yang tidak diketahui. Relevansi hasil terkait keputusan optimal yang merupakan pembelian optimal volume produk untuk semua periode waktu.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi :

Informasi yang disajikan cukup baru dan hasil yang diperoleh memuat substansi aplikasi yang penting.

Sumber gagasan penulis untuk artikel ini sangat banyak, komprehensif dan update, yang lebih sepuluh tahun terakhir hanya 4 paper dari 22 sumber yang dirujuk.

(3)

4. Kelengkapan unsur dan kualitas terbitan:

Artikel memenuhi standard penulisan dan isi untuk artikel di prosiding International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics) terindeks di SCOPUS, IEEE Explore.

Surabaya, 18 April 2020 Reviewer 1

Prof. Dr. Basuki Widodo, M.Sc NIP. 19650506 1989031002 Unit kerja : Matematika FSAD ITS

(4)

LEMBAR

USB ISBN: 978-1-5386-6085-0

Print ISBN: 978-1-5386-6087-4

d. Penerbit : IEEE

g. Terindeks di :SCOPUS, IEEE Explore

Kategori Publikasi Prosiding : Prosiding Internasional Terindeks (beri pada kategori yang tepat) Prosiding Internasional

Prosiding Nasional Hasil Penilaian Peer Review :

Komponen Yang Dinilai

Nilai Maksimal Jurnal Ilmiah

Nilai Akhir Yang Diperoleh Prosiding

Internasional Terindeks

Prosiding Internasional

Prosiding Nasional

a. Kelengkapan unsur isi prosiding (10%) 2,50 2.34

b. Ruang lingkup dan kedalaman pembahasan (30%)

7,50

6.25 c. Kecukupan dan kemutahiran

data/informasi dan metodologi (30%)

7,50

5.62 d. Kelengkapan unsur dan kualitas

terbitan/prosiding (30%)

7,50

5.62

Total = (100%) 25,00 19.83

Nilai Pengusul = 40% x1/2x 19.83=3.96 Catatan Penilaian artikel oleh Reviewer :

1. Kesesuaian dan kelengkapan unsur isi prosiding:

Kesesuaian dan kelengkapan unsur isi cukup baik. Artikel tersusun dalam kaidah penuliasan karta ilmiah. Terdiri atas 5 bagian: Introduction, Dynamical system, Linear quadratic gaussian control method, Computational simulation, Concluding remarks. Didukung 22 referensi yang sebagian besar berupa jurnal.

2. Ruang lingkup dan kedalaman pembahasan:

Ruang lingkup dan kedalaman pembahasan cukup baik. Pembahasan mengenai Application of LQG control method to determine the optimal volume of purchasing units for single product inventory system where the demand value is unknown that approached using random variable. Termasuk dalam lingkup Matematika Terapan yang sesuai dengan bidang keilmuan pengusul Temuan kebaharuan kurang ditonjolkan dalam pembahasan.

3. Kecukupan dan kemutakhiran data/informasi dan metodologi :

Kecukupan dan kemutakhiran data/informasi dan metodologi cukup baik. Terdapat 22 referensi yang sebagian besar berupa jurnal (4 diantara referensi lebih dari 10 tahun). Secara substansi ada temuan menarik/kebaharuan.

(5)

4. Kelengkapan unsur dan kualitas terbitan:

Kelengkapan unsur dan kualitas terbitan cukup baik. Artikel diterbitakan dalam International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics). Penerbit IEEE. Terindeks di SCOPUS, IEEE Explore. Hasil Turnitin similarity index=8%. Beberapa editorial kurang teliti.

Semarang, April 2020 Reviewer 2

Prof. Dr. St. Budi Waluya, M.Si NIP. 196809071993031002

Unit kerja : Matematika FMIPA UNNES

(6)

(7)

Document details

6 of 36

Single Product Inventory Control Considering Unknown Demand Using Linear Quadratic Gaussian

(Conference Paper)

, ,

Department of Mathematics, Diponegoro University, Semarang, Indonesia

Abstract

In this paper, we apply an existing control method i.e. linear quadratic Gaussian (LQG) for control purposes of product stock level of single product inventory system considering unknown demand. The value of this unknown demand is approached by a random variable that set to be the disturbance in the used dynamical model. The control purposes is bring the inventory point level to some trajectory reference decided by the decision maker with minimal eﬀort (purchase cost and holding cost). From the performed computational simulation with randomly generated data, the optimal decision which is the optimal purchased product volume for whole time periods was determined and the inventory point was followed the decided reference trajectory. © IEEE.

SciVal Topic Prominence

Topic:

Prominence percentile: 85.301

Author keywords

inventory control linear quadratic Gaussian random demand

Indexed keywords

Engineering controlled terms:

Biomimetics Decision making Gaussian distribution Robotics

Engineering uncontrolled terms

Computational simulation Control methods Decision makers Dynamical model Linear quadratic Gaussian Optimal decisions Random demand Reference trajectories

Engineering main heading:

Inventory control

◅ Back to results ◅ Previous Next ▻

^Export ^Download ^Print ^E-mail  Save to PDF ⋆ Add to List ^More...▻

View at Publisher

Proceedings of the 2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems, Robionetics 2018

25 March 2019, Article number 8674677, Pages 17-20

2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems, Robionetics 2018; Ibis Styles Bandung Braga HotelBandung; Indonesia; 8 August 2018 through 10 August 2018; Category numberCFP18RIP-ART; Code 146696

Sutrisno  Widowati  Heru Tjahjana, R. 

 View references (22)



Inventory control | Inventory | Optimal policies



ISBN: 978-153866086-7

Source Type: Conference Proceeding Original language: English

DOI: 10.1109/ROBIONETICS.2018.8674677 Document Type: Conference Paper

Publisher: Institute of Electrical and Electronics Engineers Inc.

PlumX Metrics

Usage, Captures, Mentions, Social Media and Citations beyond Scopus.

Metrics

Cited by 0 documents

Inform me when this document is cited in Scopus:

Conference Organization

General Chair

Augie Widyotriatmo

Secretary Chair 1

An Nur Aini

Secretary Chair 2

Annisa Nastiti

Technical Program Chair

Yul Y. Nazaruddin

Publication Chair

Arjon Turnip

Publicity Chair

Sisdarmanto Adinandra

International Program Committee Chair

Dessy Novita

International Program Committee (IPC)

Michael Seger (University of Medical Informatics and Technology, Austria) Ubirajara Franco Moreno (Universidade Federal de Santa Catarina, Brazil) Ji-Oh Hahn (University of Alberta, Canada)

Sidney Givigi (Royal Military College of Canada, Canada)

Jianhua Zhang (East China University of Science and Technology, China) Shoukun Wang (Beijing Institute of Technology, China)

Yongji Wang (Huazhong University of Science & Technology, China)

Andreas Dengel (German Research Center for Artificial Intelligence, Germany) Yiwen Wang (Hongkong University of Science and Technology, Hogkong) Debasish Ghose (Indian Institute of Science, Bangalore, India)

J. Suthagar (Karunya University, India)

Koshy George (PES Institute of Technology, India) Partha Pratim Ray (Sikkim University, India)

Bambang Riyanto T (Institut Teknologi Bandung, Indonesia) Dessy Novita (Padjadjaran University, Indonesia)

Estiko Rijanto (Indonesian Institute of Sciences, Indonesia) Irwan Purnama (Indonesian Institute of Sciences, Indonesia) Ismoyo Haryanto (Diponegoro University, Indonesia)

Mauridhi H. Purnomo (Institut Teknologi Sepuluh Nopember, Indonesia) Mohammad Taufik (Padjajaran University, Indonesia)

Noemi Cazzaniga (Politecnico di Milano, Italy)

Akira Namatame (National Defense Academy of Japan, Japan) Ichijo Hodaka (University of Miyazaki, Japan)

Minoru Sasaki (Gifu University, Japan)

Yoshihiro Yamamoto (Tottori University, Japan)

Yogi Ahmad Erlangga (Nazarbayev University, Kazakhstan) Jeehyun Kim (Kyungpook National University, Korea)

iv

(9)

Yong-Hoon Lee (Pusan National University, Korea) Sejoon Lim (Kookmin University, Korea)

Ching Yern Chee (University of Malaya, Malaysia)

Eko Supriyanto (Universiti Teknologi Malaysia, Malaysia) Leong Loong Kong (Universiti Tunku Abdul Rahman, Malaysia) Rini Akmeliawati (International Islamic University Malaysia, Malaysia) Shukor Sanim Mohd Fauzi (Universiti Teknologi MARA, Malaysia) Wayan Suparta (Universiti Kebangsaan Malaysia, Malaysia)

Sulfikar Amir (Nanyang Technological University, Singapore) Rita Padawangi (National University of Singapore, Singapore) Anna Antonyová (University of Prešov, Slovak Republic) Nutthita Chuankrerkkul (Chulalongkom University, Thailand) Kasemsak Uthaichana (Chiang Mai University, Thailand)

Surapong Chatpun (Institute of Biomedical Engineering, Prince of Songkia University, Thailand)

Fan-Gang Tseng (National Tsing Hua University, Taiwan) J. Andrew Yeh (National Tsing Hua University, Taiwan)

Poki Chen (National Taiwan University of Science and Technology, Taiwan) Daniel Abasolo (University of Surrey, United Kingdom)

Yanuar Nugroho (Mancester University, UK)

Le Hoa Nguyen (Hanoi University of Science and Technology, Vietnam) Nguyen Van Cuong (Can Tho University, Vietnam)

v

(10)

xi

TABLE OF CONTENT

Page Copyright ... ii ii

Welcome Message ... iii iii Conference Organization ... iv iv List of Technical Program Committee Members and Reviewers ... ...vi vi Technical Session and Program Schedule ... vii viii TABLE OF CONTENT ... x xi The Comparison Between Geo-magnetism and WiFi for Indoor Positioning

System for Public Places

Dhanny Haryanto, Kanisius Karyono, Samuel Hutagalung

1

Capturing Requirement Correlation in Adaptive Systems 6

Ratih Nur Esti Anggraini, Trevor Martin

6

Online Monitoring & Controlling Industrial Arm Robot Using MQTT Protocol Atmoko Rachmad Andri

12

Single Product Inventory Control Considering Unknown Demand Using Linear 17

Quadratic Gaussian

Sutrisno, Widowati, R. Heru Tjahjana

17

Flocking of Robots with Predictive Localization 21

Augie Widyotriatmo, Joshua Julian Damanik, Dewi Retno Andriani, Endra Joelianto

21

Orientation Searching on Multi Robot

Farhan Ihsani, PUR, Agung Nugroho Jati, Randy Saputra 27

Development of Balancing Spherical Rolling Robot 32

Chanin Joochim

32

Path Reference Generation for Upper-Limb Rehabilitation with Kinematic Model

Augie Widyotriatmo, Muhammad Barri, S Suprijanto 38

Authors Index ... x 44

(11)

2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics) Bandung, Indonesia, August 08-10, 2018

6

Capturing Requirement Correlation in Adaptive Systems

Ratih N.E. Anggraini

Intelligent Systems Lab, University of Bristol Bristol, United Kingdom

ra16032@bristol.ac.uk

T.P. Martin

Intelligent Systems Lab, University of Bristol Bristol, United Kingdom

trevor.martin@bristol.ac.uk

Abstract—An adaptive system is expected to modify its behavior to suit changes in environmental and system condition.

Somehow, it causes problems during requirement specification (and in subsequent verification) since it is difficult to provide all possible adaptation needed during runtime. Thus, it may be necessary to temporarily ignore non-critical requirements to a certain point in order to maintain satisfaction, especially on critical and invariant requirements.

One way to handle uncertainty in the adaptive system is by relaxing requirement and present the verification result using a graded (fuzzy) condition in a requirement satisfaction. Often, relaxing one requirement can affect the satisfaction of another related requirement. In this paper, we use linear regression to capture the relationship between two relaxed system requirements. Pearson and Spearman correlation coefficient is utilized to calculate correlation strength. To illustrate the approach, we consider a smart vacuum system problem.

Keywords—adaptive system; relax requirement, linear regression; Pearson correlation coefficient; Spearman correlation coefficient

I. INTRODUCTION

Adaptive systems are supposed to keep satisfying its requirements by doing adaptation in response to a changing environmental and system conditions [1]. The more complex the environment, the more uncertainties arise. Thus, uncertainty on adaptive systems has become major concerns on many works [2-5].

To deal with uncertainty, especially in requirement specification phase, Whittle et.al proposed a requirement language called RELAX [6]. RELAX incorporated several types of operators to address uncertainty in system properties.

The verification result on the system, then can be described as fuzzy satisfaction to illustrate its degree of satisfaction [7].

Relaxing a requirement satisfaction on adaptive system can become an answer in handling uncertainty. Somehow it also can affect related requirements, either positively or negatively.

Souza et. al introduced Awareness Requirements, requirements that talk about other requirements success or failure [8]. It emphasized the importance on monitoring requirements at runtime to provide feedback loops.

Correlation describes relationship, involving dependency, between two variables and commonly examine how close is the linear relationship with each other. Correlation is useful to predict future association between those variables. Linear regression, Pearson and Spearman correlation coefficient are simplest way in capturing the relationship between two variables. It was widely used in medical and psychological researches [9-12].

In this paper we adopt linear regression to capture correlation between two related requirements in adaptive system. Pearson and Spearman correlation coefficient is used to describe the relationship strength. A case study was used to demonstrate the approach.

II. B^ACKGROUND A. Requirement Relaxation in Adaptive System

The ability of adaptive system to adapt to changing environment led to the growth of uncertainties. Preparing all explicit states during system lifetime become impossible.

Thus, tolerate environment conditions, especially a non- critical one, to a certain point is necessary.

A requirement language called RELAX provide a structured natural language to capture uncertainty in adaptive system [13]. The requirement is written using RELAX grammar shown in (1). It incorporated modal, temporal and ordinal operators with uncertainty factors. RELAX operators are shown in Fig 1. The phrase ‘AS POSSIBLE’ in temporal and ordinal operator facilitate uncertainty which mean the requirement satisfaction can be tolerate to a defined threshold.

ϕ ∷= | | | ϕ | MAY ϕ₁OR MAY ϕ₂

| ϕ | ϕ₁UNTIL ϕ₂ | ϕ |

AFTER e ϕ | IN t ϕ | AS CLOSE AS POSSIBLE f ϕ |

AS EARLY , LATE, MANY, FEW AS POSSIBLE ϕ (1) Table I shows the requirements on Adaptive Assisted Living system written in RELAX language. Requirement R1 is divided into six subset requirements. R1.1 to R1.4 are relax requirements while R1.5 and R1.6 are invariant one.

(12)

12

Online Monitoring & Controlling

Industrial Arm Robot Using MQTT Protocol

Rachmad Andri Atmoko

School of Mechanical & Electrical Engineering Guilin University of Electronic Technology

Guilin, China mokoraden@hotmail.com

Daoguo Yang

School of Mechanical & Electrical Engineering Guilin University of Electronic Technology

Guilin, China d.g.yang@guet.edu.cn

Abstract— Today there are many significant changes on the Industrial management system that we called Industry 4.0. It is the latest revolution of the technology who implemented in industry. As we know for the first time, we are using a steam engine to running industrial equipment, and then we are using electricity which has made a significant revolution in the industrial operation. Nowadays, the presence of the internet technology will be making a new face of the industrial management, especially on the controlling and monitoring operation. This paper will purpose of the implementation of internet technology for monitor and control industrial arm robot in industry. Focus on this paper is implementation MQTT as the latest protocol on the Internet of Things applications. We make a web-based interface for monitoring motion and controlling the angle of joint arm robot in a ROS Industrial simulation environment. Communication between industrial arm robot simulation and client using MQTT protocol give low latency data transmission. The average of latency time transmission between client and robot is 0.3196 second.

Keywords— Industrial ARM Robot, IIoT, MQTT

I. INTRODUCTION

Industrial technology has been growing for more than 200 years since found the steam engine in the 1780s. For the first industrial revolution time, we are using a steam engine for running industrial equipment. The second revolution we are using electricity, and for the third revolution electronic control system come and make a big revolution in the industrial operation. On this revolution control technology begin to use to facilitate the control of many tools in industrial operation.

In 1969 Modicon presented the first Programmable Logic Controller that enabled digital programming of an automation system. Now the presence of the Internet technology will be making a new face of the industrial management primarily on the control operation [1].

The rapid development of electronic technology, information technology and advanced manufacturing technology make the production model of manufacturing enterprises is being transferred from digital to intelligent [2].

Consequences of this era are the emergence of smart manufacturing platforms like Advanced Manufacturing Cloud

of Things (AMCoT) which based on Internet of Things, cloud computing, big data analytics, cyber-physical systems, and prediction technologies [3].

In the industrial area, we have the new paradigm that called Industrial Internet of Things (IIoT). It is a new internet evolution which integrates billions of sensors, cameras, industrial machinery, displays, smartphone, and other smart devices communicating into cloud datacentres and manipulates data in real-time sand virtualized cloud resources for automating an end-to-end manufacturing lifecycle [4].

The industrial robot is essential equipment for applying the concept of smart manufacturing. It has been widely used to do repetitive, tedious, dangerous tasks such as assembly, packaging, and welding. To extend the functional range of the industrial robot, it integrated with network technologies, so it appears the term new cloud robotics [5].

Monitoring and Controlling is an essential task for Smart Manufacturing to ensure the devices working correctly. Some research gives us a point of view so that it can improve quality of industrial management and safety such as to monitoring industrial machinery condition [6] and predictive maintenance [7] and also to ensure system safety device from leakage ammonia [8]. Monitoring the condition of the robot can increase the reliability of performance smart manufacturing so that the condition of anomaly can be detected at the early stage. Online controlling also can be developed further to make the robot motion planning, wherein the concept of smart manufacturing reconfigurable system prosecuted quickly and efficiently.

On this research, we use MQTT protocol as one of the latest protocol on the Internet of things [9]. MQTT protocol is that many used in application Machine to Machine (M2M) and Machine to Cloud (M2C). Many application fields are using this protocol such as real-time weather monitoring and precision farming [10], warehouse management system for climacteric fruits and vegetables [11], and air quality monitoring [12]. The benefit of MQTT is smaller size data packet than other protocol that gives effect increasingly speed of data transmission or low latency. It can be used for application data acquisition that gives benefit faster than HTTP protocol [13].

(13)

2018 International Conference on Robotics, Biomimetics, and Intelligent Computational Systems (Robionetics) Bandung, Indonesia, August 08- 10, 2018

Flocking of Robots with Predictive Localization

Joshua Julian Damanik, Dewi Retno Andriani Engineering Physics Progralll

InSlilUl Teknologi Bandnng Bandung, Indonesia

j oshuajdmk @gmail.com, dewiretn023@gmail .com

AbslraCI-The behavior of Hocking is a natural phenomenon in living things that gives many advantages. The algorithm of flocking is being developed in multi-agents control for mobile robots. One popular model of flocking control is using virtual leader-follower method with artificial potential field. This model requires no physical leader and can be implemented easily.

However, this model requires active participation of all flocking agents. This paper proposes a two-layer system architecture with localization prediction system. With discrete system and time delay in trajectory tracking, the performance of the flocking of nonholonomic robots system is analyzed in simulation. Specific allowed time delay and desired sampling period of sensor and communication system used in the implementation is investigated.

Jlldex Terms-multi-agent system, mobile robots, nonholo- nomic robot, flocking control, tracking control, predictive local- ization

I . INTRODUCT ION

Flocking is a behavior in a number of agents that are interacted cooperatively to reach a speci fic goal. This behavior is inspired from natural phenomenon, such as birds and fishes.

The implementation of flocking could be seen on military equipments, transportations, and industries [I] . Flocking is involved by more than one agent to do tasks and reach targets.

Each task is distributed to each agent and it helps a single agent to keep an eye on the target. [2].

Reynolds in [3] proposed three behavioral rules for flocking, which are I) Flock centering: behavior to stay close with fl ocking agents, 2) Velocity matching: behavior to match velocity with flocking agents, 3) Collision avoidance: behavior to prevent colli sion with flocking agents. These three behavioral rul es cause fl ocking agents to move in formation cohesively.

There are several methods that can be used to control the flocking group: behavior-based strategy [4]- [6], virtual structure [7]- [ I 0], leader-follower [11]- [ 14], and some other strategies. Lawton in [5] suggested that the strategy of virtual structure and leader-follower need a participation of communication from each agent in a community, therefore they require active participation of every agents in a group of flocking.

Quintero in [2] proposed a group of flocking to use a virtual leader as an objective of group, so the followers could easily move by following the viltual agent. This control law is effective at achieving the objecti ve of the group.

To avoid collision with objects other than fl ocking agents, fl ocking agents have to be able to avoid obstacles in environment. One of methods that can be used is to define a potential

978-1-5386-6086-7/18/$31.00 20 18 IEEE

21

Augie Widyotriatmo, Endra Joelianto InSlrumentalion and COII/rol Research Group

InSlilul Teknologi Bandllllg Bandung, Indonesia augie@tf.itb.ac.id, ejoel @tf.itb.ac.id

function . There are some approches in constructing an obstacle avoidance system with potential function , such as, in [14], a moving virtual agent approach is propose, an approach with virtual potential field is suggested in [1 5], and an approach with rotational potential field is conducted in [10].

In the implementation, we often need to consider the system with specific sampling period. A localization system, such as global positioning system (GPS), model matching, laser based locali zati on, etc., and environment recognition systems, such as laser scanner, camera, or other sensors, need an amount of time to collect the data and to be delivered to the multirobot systems. This low sampling period may degrade the performance, or even may lead the multi-robot system into unstable or unsafe situation. The selection of potential function including the parameters is affected by the sampling period of the system. Therefore, the selection of function and tuning parameters of the potential function is one of the important things to be determined in the implementation. The selection of potential funct ions and their parameters is greatly influenced by the sampling period of the system and is an important step to be done in implementation to make sure the good performance, the feasibility, and the stability of the system.

Strategy of designing the implementation of fl ocking control for nonholonomic robot with obstacle avoidance is presented in this paper. Two layers of control system is proposed. First layer is the trajectory generation system and the second layer is the trajectory tracking control system. To cope with the low sampling period of the systems, a locali zation predictor system is proposed. Furthermore, simulation results of the flockin g of nonholonomic robot system are shown considering the low sampling peri od of the locali zation systems.

In the section 2, theory about smooth pairwise potential function, flocki ng control system, and trajectory tracking control system are explained. In the section 3, the architecture of the implementation system, called two layers system, with localization predictor system and the low sampling period on the trajectory tracking control system are explained. In the section 4, the simulation result and di scussion are presented.

Section 5 draws the conclusion of the paper.

II . PRELIM INAR IES A. SlIIoolh Pairwise POlenlial Funclion

A smooth pairwise potential function is used for interaction between agents. We assume that there are No agents, in which

(14)

32

Development of Balancing Spherical Rolling Robot

Chanin Joochim College of Industrial Technology

King Mongkut's University of Technology North Bangkok

Bangkok, Thailand chanin.j@cit.kmutnb.ac.th

Prakasit Pongsorn College of Industrial Technology

Bangkok, Thailand Prakasitpongsorn33@gmail.com

Kasidach jindamanee College of Industrial Technology

Bangkok, Thailand kasidach.kmutnb@gmail.com

Abstract— Industrial technology and robot have become to be the important things in our life. There have large-scale innovations development and growing fast to meet the demands of people that increases continuously. The purpose of this research is to develop the prototype of Balancing Spherical Rolling Robot aims to work in various areas. Balancing Spherical Rolling Robot has true holonomic and would be able to move in any direction without having to change its orientation. The internal mechanics and the corresponding control systems are designed to change the momentum of the robot. Research show the mechanical design and movement experiment in any behavior.

Keywords—balancing spherical robot, autonomous system, mobile robot, holonomic movement, ball bot.

I. INTRODUCTION

The robots are divided into 2 types according to their usage: First, fixed robot is a robot that cannot change the base location itself. It is a mechanical arm that can move only within each joint itself. It is widely used in industrial plants such as automobile assembly plants. Second, mobile robot.

This robot is different from the robot that is attached to it. It can move to any places. By using the wheel or using the legs.

This type of robot is currently undergoing research in the laboratory. To develop into a variety of applications such as robots exploring the moon. Since the nature of a ball, can moves follow the path of least resistance. The spherical robot would have true holonomic and hence would be able to move in any direction without having to change its orientation.

Generally, the drive system of the spherical robot is located inside a shell. Mechanical power is transmitted to the outer shell to rotate. The drive system must be able to transfer power to the outer shell. Balancing mechanism inside is very importance to keep the position forward.

The balancing spherical rolling robot is stabilized robot in a spherical object. It can move backwards left tilt, is controlled through the changing momentum inside the robot.

Researchers developed balancing spherical rolling robot used in underwater experiments [1]. To show the dynamics modeling of single water-jet propeller and coordination of multiple water-jet propellers. Based on the coordination, transforms of three basic motions, surge, heave and yaw. [2]

aims to develop a spherical mobile robot having a spherical shell and a mass-control device inside, which travels around by actively changing its internal mass distribution. [3]

describes a fully-coupled 3D model for spherical wheeled

platforms and not only captures relevant spatial aspects of motion, but also provides a basis for controllers better informed by system dynamics. [4] presented structure of the spherical robot that has several inflatable sections. Proposed pneumatic system inside to control the air pressure. To control robot, move forward section is inflated while the neighboring section towards the intended direction is deflated. The robot rolls towards the next section and moves to the particular direction. Designing the robot is very fluffy and light- weighted. [5] reviewed spherical robottics in several kinds from many researchers. Paper show rresearchers have begun to develop various novel concepts to maneuver and control this family of robot. Also overview of the current research directions that various groups and the various uses of the fundamental principles of physics for propelling a spherical robot.

In this paper study design and build robots stabilized in spherical objects. To study the movement and control of a robot in a spherical object. The application of the Android smartphone is implement. Robot can control over the Bluetooth connection. To command balance rolling, forward, stop, turn. The robot can move in a horizontal plane. The microcontroller is a command processing device. The feedback PID system is used to keep the robot steady, not rolling in the shape of the robot body. It moves to where we want to be controlled by the application.

II. ROBOT PROTOTYPE DESIGN A. Mechanical Design

This section shows the designing of the robot. Figure 1 shows the mechanical designing. The figure can explain as follow 1. motor drive control unit 2. IMU GY-87 sensor, is very impotence to balancing the robot. Sensor gives roll, pitch, yaw data. In order to control the movement IMU have to identify the optimum position. 3. belt to move the weighted steel. 4. weighted steelis very importance the balance the robot. Closed loop PID control has to be implemented. If the weight stays in the middle of the body, robot keep steady state position. If the weight moves to left, momentum of the centroid goes to left. Robot can move to the left position. 5.

Shell of the robot made form acyclic. Bowl by heating mold, to form the spherical shell

. 6.

Bluetooth serial module HC-05.

Use to communicate between mobile and robot. 7. arduino