Document Delivery Robot Based on Image Processing and ... · DOCUMENT DELIVERY ROBOT BASED ON IMAGE PROCESSING AND FUZZY CONTROL ... design of fuzzy logic controller to resolve a

DOCUMENT DELIVERY ROBOT BASED ON IMAGE PROCESSING AND FUZZY CONTROL

Jih-Gau Juang and Chang-Yen YangDepartment of Communications, Navigation and Control Engineering,

National Taiwan Ocean University Keelung, Taiwan, R.O.C.E-mail: [email protected]

IMETI 2015 C5002_SCINo. 16-CSME-35, E.I.C. Accession 3921

ABSTRACTThe objective of this study is to integrate image processing, pattern recognition, RFID, and fuzzy theory intoan omnidirectional wheeled mobile robot for receiving and delivering documents between rooms. In imagepre-processing, the Hue-Saturation-Lightness color space is applied to avoid light interference, and thengrayscale image threshold is used to obtain binary image. The median filter is utilized to filter the noisesof speckle and salt-and-pepper, so color segmentation is then applied to capture desired color for trackingcontrol. Pattern recognition is performed by the Adaptive Resonance Theory. RFID reader and room tag isused to verify the room number of the destination so that the recognition error from image processing canbe avoided. Fuzzy theory is implemented into an omnidirectional wheeled mobile robot control design fordriving the wheels of the robot. Experimental results show that the proposed control scheme can make theomnidirectional mobile robot move to destination, receive and deliver documents between offices.

Keywords: wheeled mobile robot; image processing; adaptive resonance theory; fuzzy control; RFID.

ROBOT MOBILE DE LIVRAISON BASÉ SUR LE TRAITEMENT DE L’IMAGE ET LACOMMANDE DE LOGIQUE FLOUE

RÉSUMÉL’objet de la présente étude est d’intégrer le traitement de l’image, la reconnaissance de formes, le RFID et lathéorie de logique floue dans un robot omnidirectionnel mobile sur roues pour la réception et la livraison dedocuments entre des pièces séparées. En prétraitement d’images, l’interface de couleur Teinte, Saturation,Luminosité (TSL) est appliquée pour éviter l’interférence lumineuse et l’image à échelle de gris à l’aided’une valeur seuil, et est utilisée pour obtenir une image binaire. Le filtre médian est utilisé pour filtrerles bruits parasites et le bruit impulsionnel, la segmentation de couleurs est alors appliquée pour capter lacouleur désirée pour la commande de suivi. La reconnaissance des formes est accomplie par la théorie dela résonance adaptive. Le lecteur RFID et le système de repérage TAG sont utilisés pour vérifier le numérode la pièce de destination pour que la reconnaissance d’erreurs de traitement de l’image puisse être évitée.La théorie du flou est implantée dans une commande de robot omnidirectionnel sur roues pour commanderles roues du robot. Les expériences démontrent le schéma de commande proposé peut faire avancer le robotmobile jusqu’à destination, recevoir et livrer des documents entre des pièces séparées.

Mots-clés : robot mobile sur roues; traitement de l’image; théorie de résonnance adaptive; commande floue;RFID.

Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No. 5, 2016 677

1. INTRODUCTION

As the progress of technologies, human life has been changed a lot in past few decades. One of the pro-gressed technologies is robot system. Robots have been gradually used in our daily life that provide enter-tainment, home security, medical care, and other areas of service. Intelligent robots integrate automation,control, electronics, mechanics, and communication technologies to perform some tasks that are not suit-able for human or are applied to replace human forces. Due to variety needs, there are many types of robots.In response to the various needs of different robot systems, advanced theories are developed, such as pathplanning, visual image processing technology, obstacle avoidance technology, and arms control. How toimprove the accuracy of robot’s performance becomes one of the main focuses in the field of intelligentrobotic control. Omnidirectional wheeled mobile robot (WMR) is one of the mobile robot researches thathas been discussed widely. It has more advantages than normal WMR, such as mode of action, easy control,and high mobility [1–3]. To design an intelligent system for omnidirectional WMR, providing documentsdelivery service between room is the objective of this research. When omnidirectional WMR is working,one must consider how to adapt to the environment and improve efficiency of mission. In this research, weuse fuzzy theory to avoid obstacles and use the adaptive resonance theory (ART) [4] to recognize patterns.The intelligent system of omnidirectional WMR integrates many functions, such as environment sensing,dynamic decision-making and tracking, behavior controlling and executing, etc. Omnidirectional WMRis a nonlinear model, dynamic equations are needed for system analysis and control regulation design. Afuzzy control system is a nonlinear system, and the control system is simple and flexible. The purpose ofthis study is to use real-time image from webcam, and then provide target recognition and tracking whichincludes destination’s room number identification. In image recognition, we use the ART network that canrecognize numbers and patterns fast and correctly. RFID reader identifies destination again when omnidirec-tional WMR moves forward to the destination. Ultrasonic sensors receive reflecting values when the WMRmoves, so these values are used to determine the locations of the obstacles, and use these values as inputsof the fuzzy controller to avoid obstacles. Contributions of this study are the proposed control scheme cancontrol omnidirectional WMR to perform documents delivery service that use real-time image processing totrack target and plan path automatically. ART is utilized to recognize command patterns and room numbers.RFID is applied to verify destination. Obstacles avoidance is controlled by fuzzy controller.

The WMR technologies are discussed widely in recent years. Many researchers have presented theirresearches of intelligent robot control. Researches of intelligent robot include obstacles avoidance, pathplanning, object tracking, etc. Leow [5] analyzed kinematic model of omnidirectional WMR. Chen uti-lized a real-valued genetic algorithm and fuzzy system to an autonomous vehicle control problem. Obstacleavoidance and parking control are performed by the use of a CCD camera, sonar sensor, and localizationsystem on a wheeled mobile robot [6]. Zhan [7] presented moving object tracking by the use of real-timeimage for a wheeled mobile robot control. Wang [8] utilized a localization system to path planning andparking control of a wheeled mobile robot. Yang and Meng [9] proposed a novel neural network approachto real-time collision-free path planning of robot manipulators in a nonstationary environment, which isbased on biologically inspired neural network model for dynamic trajectory generation of a point mobilerobot. Harb et al. [10] proposed neural network control, instead of traditional methods, which provide suit-able solutions to locally navigate and recognize the environments subplace. Al-Sagban and Dhaouadi [11]presented a new neural network based reactive navigation algorithm for wheeled mobile robots. The nav-igation algorithm is optimized by a user-defined objective function which minimizes the traveled distanceto the goal position while avoiding obstacles. Zhong et al. [12] presented a new methodology based onneural network for robot path planning. An improved Hopfield-type neural network model is established forpropagating the target activity among neurons in the manner of physical heat conduction, which guaranteesthat the target and obstacles remain at the peak and the bottom of the activity landscape of the neural net-

678 Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No. 5, 2016

work, respectively. Chen [13] presented intelligent strategies for a wheeled mobile robot to avoid obstacleand move to target location. In short-distance obstacle avoidance model, the wheeled mobile robot utilizessignals of the ultrasonic sensors to avoid obstacle. In target-driven obstacle avoidance model, fuzzy theorywith sensor signals is used to control the speed of the wheeled mobile robot and make it move to targetlocation. Juang [14] integrated image processing techniques, fuzzy theory, wireless communications, andsmartphone to wheeled mobile robot for real-time surveillance. Ohya [15] described a vision-based naviga-tion method in an indoor environment for an autonomous mobile robot which can avoid obstacles. Achmad[16] presented the development of a visual-based fuzzy navigation system that enables a mobile robot inmoving through a corridor or following a wall. The system employs a camera to detect the existence ofwalls on the left, the right, and the front of the robot. A fuzzy logic controller uses the information gatheredby the camera to determine the turning angle and the speed of the robot. Yu and Chen [17] proposed thedesign of fuzzy logic controller to resolve a wheeled mobile robot through digital images, and the designof WMR anti-collision system. Watanabe [18] proposed an image-based fuzzy control to realize obstacleavoidance for an autonomous mobile robot. It does not need to resort the estimation of self-position of theautonomous mobile robot. In the USA, a mobile hospital robot called TUG has been developed by AethonInc. [19]. The TUG is an automatic robotic delivery system that can provide a variety of hospital logisticneeds such as lines delivery or pick up, food service, pharmacy or for lab sample transport. The TUG uses anonboard computer that has Wi-Fi communication abilities to maintain linkage to the central control system.The TUG has been successfully deployed in many hospitals, but the system is very expensive. In [20], theauthors applied an indoor Global Positioning System that uses many overhead TV cameras in the buildingto guide the WMR for documents delivery service. The drawback is that it needs many TV cameras to coverthe work space. In our study, active sensors are installed in the WMR, the WMR can search the destinationwithout passive auxiliary image devices.

2. SYSTEM DESCRIPTION

The omnidirectional WMR is shown in Fig. 1. The waistline of the WMR is 150 cm. There are three omniwheels on the WMR and are separated 120◦ to each other. Three 18V-DC motors are installed to providerated torque 68 mNm. The WMR has six ultrasonic sensors in front of the second layer of the robot, whichare used to measure the distance and detect obstacles. The size of the robot body is 400 mm in length,380 mm in width, and the height is 1000 mm. It has two mechanical arms with fingers that have ability togrip stuff or push button. The length of the arm and the finger are 500 and 170 mm, respectively.

The robot arms have six RX-64 motors, two in the shoulder and one in the elbow on both sides. RX-64 is40.2 mm in length, 41 mm in width, and the height is 61.1 mm. Its stall torque is 5.30 mNm. There are twoRX-28 motors at wrist on both sides. RX-28 is 35.6 mm in length, 35.5 mm in width, and the height is 50.6mm. Its stall torque is 3.77 mNm. A Microsoft LifeCam studio webcam is set on left shoulder of the robotbody. We use it for target tracking and pattern recognition. UHF RFID reader MT-RF800-BT and UHFtag are applied to verify destination, as shown in Fig. 2. PARALLAX PING ultrasonic distance sensor candetect distance from approximately 2 cm to 3 m, burst frequency is 40 KHz, current is 30 mA, and voltageis 5 V.

Before introducing the control scheme, we must know omnidirectional WMR kinematic model [21]in order to further control it. First, we assume omnidirectional WMR is situated on a coordinate sys-tem as shown in Fig. 3, where vm is the speed of target direction, v1, v2, v3 are speeds of each wheel,xm, xy are decomposition of vm, and α is the angle between the target direction and the car’s headingdirection.

Since the vm vector is composed by xm and ym, we can get a v2 speed for the vm direction as follows:


Fig. 1. Omnidirectional wheeled mobile robot.

(a) (b) (c)

Fig. 2. (a) UHF RFID reader MT-RF800-BT, (b) UHF tag, (c) PING ultrasonic distance sensor.

v2 = −sin(δ )xm + cos(δ )ym +Lφ

= −sin(30)vm sin(α)+ cos(30)vm cos(α)

= (−0.5sin(α)+0.866cos(α))vm (1)

Then v1 and v3 can be obtained as follows:

v1 = (−0.5sin(α)−0.866cos(α))vm (2)

v3 = sin(α)vm (3)

3. CONTROL SCHEME

The flow chart of control sequence is shown in Fig. 4. In this study, we use visual image processing tocapture door location, plan path, and recognize room number. Path planning in this study is performed by


(a) (b)

Fig. 3. (a) Omnidirectional WMR coordinate system, (b) simplified kinematic model.

Fig. 4. Flow chart of control sequence.


Fig. 5. Fuzzy sets.

detecting door feature from webcam, using door feature to check target destination, and using fuzzy theoryto avoid obstacles. RFID reader is then used to read tag and double check whether it is the destination ornot. When the robot moves into the desired room, it can recognize a job-done pattern and make sure toaccomplish documents delivery.

Fuzzy theory is applied to the WMR for obstacle avoidance [22]. The fuzzy control structure has fourinputs and one output. Ultrasonic signals S2, S3, S4, S5 are inputs, and the data is distance. Fuzzy sets areF (far), M (medium), N (near). Output DA is turning angle for left or right, and its fuzzy sets are VLL, VL,L, SL, Z, SR, R,VR, VRR. Fuzzy rules are given as follows, and the fuzzy sets are shown in Fig. 5.

Rule 1: If S2 is F and S3 is F and S4 is F and S5 is F, then DA is Z.Rule 2: If S2 is M and S3 is F and S4 is F and S5 is F, then DA is Z.Rule 3: If S2 is N and S3 is F and S4 is F and S5 is F, then DA is Z.Rule 4: If S2 is F and S3 is M and S4 is F and S5 is F, then DA is R.Rule 5: If S2 is M and S3 is M and S4 is F and S5 is F, then DA is R.Rule 6: If S2 is N and S3 is M and S4 is F and S5 is F, then DA is R.Rule 7: If S2 is F and S3 is N and S4 is F and S5 is F, then DA is VRR.Rule 8: If S2 is M and S3 is N and S4 is F and S5 is F, then DA is VRR.Rule 9: If S2 is N and S3 is N and S4 is F and S5 is F, then DA is VRR.. . .Rule 80: If S2 is M and S3 is N and S4 is N and S5 is N, then DA is VLL.Rule 81: If S2 is N and S3 is N and S4 is N and S5 is N, then DA is VLL.


3.1. Image ProcessImage process of the proposed control design has two parts. One is the room number processing, anotheris target pattern processing. In room number processing, the number is captured by a webcam. Originally,the webcam captures the image in RGB color space. Then, the RGB image is transformed to the HSL colorspace and is binarized for pattern recognition in image processing [23]. The RGB color space uses additivecolor by primary color to produce many colors, but colors produced by primary color component are not sointuitional. The HSL color space includes the hue, saturation, and lightness. This color space is similar tohuman visual system and has a powerful perception. RGB color space values are transformed to HSL colorspace values by the following equations:

h =

0◦ if max = min

60◦× g−bmax−min +0◦ if max = r and g≥ b

60◦× g−bmax−min +360◦ if max = r and g < b

60◦× b−rmax−min +120◦ if max = g

60◦× r−gmax−min +240◦ if max = b

(4)

l =12(max+min) (5)

s =

0 if = 0 or max = minmax−minmax+min if 0 < l ≤ 1

2max−min

2−(max+min) if l > 12

(6)

Thresholding is a kind of method to do image segmentation. In the grayscale image, when the gray valueis greater than a threshold pixel, the gray value is set to maximum gradation. If gray value is less than athreshold pixel, the value will be set to the minimum gradation. According to different demand, one candecide threshold pixel [24]. By the thresholding process, grayscale can be transformed to binary image. Inimage processing, we need to do noise reduction before edge detection. Median filter is a nonlinear filteringtechnology and is often used to remove noise from image and signal. It is particularly useful for specklenoise and salt-and-pepper noise. It is also useful in the undesirable edge blur occasions to save edge features.First we set n× n mask and n must be odd. Then, extract value to replace value on image after sorting inrange of mask size [25]. Median filter process is shown in Fig. 6.

Color segmentation can recognize all color from image and extract specific color. To extract specificcolor, use color spectrum to decide threshold of hue, saturation, and lightness. Then, the object with thisspecific color can be obtained from the webcam. In this study, we use this method to detect desired doors.An example is shown in Fig. 7, there are many circles in different colors. If we want to capture red color, weneed to set hue value to 0◦, saturation value to 100%, and lightness value to 0.5. In Fig. 7(b), the predefinedframe is the detection section and only the desired color on the image is detected. When a threshold isapplied, only the desired color is identified and remains in Fig. 7(c).

Image processing of a room number is shown in Fig. 8. After the image of RGB color space (Fig. 8a) istransformed to the HSL color space (Fig. 8b), and then from the HSL color space to binary image as shownin Fig. 8(c), a median filter is applied to filter out noise as shown in Fig. 8(d). In target pattern processing[7], we use RGB color space to decide RGB value that we need, as shown in Fig. 9. This method can helpus to filter out the colors which are not needed. Since the detection area has many colors, so we set the sumof pixels to filter out those are not needed, and use predefined color to be the target, as shown in Fig. 9(b).


Fig. 6. Median filter process.

(a) (b) (c)

Fig. 7. (a) Circles in different colors, (b) mark desired color, (c) result of color segmentation.

3.2. Image IdentificationIn this study, image identification is performed by a competitive learning network called Adaptive Reso-nance Theory (ART) [5]. The ART is applied to recognize target door, room number, and job-done circlepattern. The number of neural units is fixed in competitive learning network. Dynamic network structuremeans the network has enough neural units wait for network use. When input vector and pattern similarityexceeds a certain limit, then resonance phenomenon will occur. If the pattern similarity of the input vectorhas not reach the limit, this input will be considered as a new pattern and this will be memorized by anunused neuron. After all neural units have memorized certain patterns, network stability requirements willexceed plasticity. At this point the network will have no reaction for input. Procedures of the ART neuralnetwork are shown as follows:

Step 1: Set all neurons with initial bond values.Step 2: Present input to the network. If the first image is input, then set it to the first neuron as the winnerand assigned to class 1, and skip to Step 6.Step 3: Enable all neurons which have won and have assigned to certain classes.Step 4: According to the following similarity to find the closest class with the input vector, it is called“closet” refer to the “similarity”. The similarity measurement is defined as (the first rating standard):


Fig. 8. (a) RGB image, (b) HSL image, (c) binary image, (d) image after median filter process.

(a) (b)

Fig. 9. (a) Decide (R, G, B) value in RGB color space that we need, (b) predefined color circle appears in detectionarea then only catch this pattern.

S1(w j,x) =w j · x

β +‖w j‖1=

wTj x

β +∑pi=1 wi j

(7)

Step 5: From Step 4, select the winner that has the largest S1. Assume that the jth neuron is the winner, thenthe second similarity standard is applied to measure the victorious neuron samples stored in the neurons.The second similarity measurement is defined as the evaluation standard (the second rating standard):

S2(w j,x) =w j · x‖x‖1

=wT

j x

∑pi=1 xi

(8)

When S2(w j,x) ≥ ρ (ρ is a positive constant and is less than 1, and is called vigilance parameter), the w j

and x can be regarded as very similar, and then we can perform Step 6. Otherwise the jth neuron is disabled;then go back to Step 4 and find the next winning neuron.Step 6: Adjust the weights of the winner neuron h by following equation:

w j(n+1) = w j(n) AND x (9)

The output of neuron j represents the input pattern x is recognized as class j. Go back to Step 2 to re-acceptnext new input pattern.

The ART neural network is shown in Fig. 10. An example of room number “910” is recognized by theART as shown in Fig. 11(a). RFID reader and tag are utilized to verify the destination. When the WMRapproaches a door, the RFID reader will check the tag’s UID. Each RFID tag has its own UID code. EachUID corresponds to a certain room number. A tag posted on the door is shown in Fig. 11(b).


Fig. 10. ART neural network.

(a) (b)

Fig. 11. (a) Room number 910, (b) RFID tag.

4. EXPERIMENTAL RESULTS

In the first step, the robot searches a door in a preset experimental environment. When the robot detects thenearest target, the robot moves forward to this objective. On the way to this objective, the robot will correctits path by detecting image ranges at all time. When the robot reaches a specified distance in front of thedoor, the robot will correct the angle to aim at the door and doorplate, and then recognize the room numberand check whether it is the destination or not. If the room number is the desired destination, the robot movesto the door and RFID reconfirms at a specified distance again to verify the destination. If the room is notthe destination, the robot will search for the next similar target. When RFID verifies the destination againand confirms it is true, the robot raises its arm. When the robot is 500 mm away from the door, the robotknocks on the door continually until it is opened. After the door is opened, the robot moves into the roomand stops at a specified distance to wait for the command of mission complete. We set a circle pattern as themission complete command for robot’s recognition. When the pattern appears in its detection range and is


(a) (b)

Fig. 12. (a) Schematic diagram of knocking control, (b) RFID reader transmits signal and receives certification replyfrom passive tag.

(a) (b)

Fig. 13. (a) Move forward to the door and raise an arm, (b) approach the door at a specified distance, then knock dooruntil the door is opened.

recognized, then the robot will turn 180◦ and leave the room. After the robot leaves the room, it will go tothe next room.

4.1. Door Knocking Control

The signals of the UHF RFID reader and ultrasonic sensor are used for the door knocking control. When therobot approaches the door and the distance between the door and robot is less than 1 m, the robot stops andidentifies the tag’s UID. If the UID is in accordance with the present room, then the robot will move forwardto the room and raise an arm to prepare for knocking on the door. When the robot approaches the door andthe distance is less than 50 cm, the robot stops its movement and knocks on the door until it is opened. Inthe knocking control, the robot will knock three times and wait for three seconds, then repeat the processuntil the door is opened. Figures 12 and 13 show schematic diagrams of the door knocking control.


Fig. 14. Image detection range in perspective of the webcam.

Fig. 15. Initial set up and experiment environment, the target is not detected thus the robot turns left to find the wall.

4.2. Target TrackingThis section introduces how to track the target and avoid obstacles. Color segmentation is the main pre-process for target tracking control. One can set the color that is needed by color segmentation. A thresholdis preset to cut the door element in blue space, and set the threshold of pixel to determine which is needed.In Fig. 14, the DL and DR frames are detection sections. The DM frame is the target which is the desireddoor. Fuzzy control is applied as follows:

If DL == 0 and DM == 1 and DR == 0, then Motion = W (Move forward).If DL == 1 and DM == 0 and DR == 0, then Motion = A (Turn left).If DL == 0 and DM == 0 and DR == 1, then Motion = D (Turn right).

Figure 15(a) shows the initial setup and experiment environment with starting location and destinationroom being given. In Fig. 15, the target door is not detected at the initial position and dright wall is the nearestdistance between the robot and the wall. So the robot will turn left to the other side of the wall and searchthe door which is nearest.

In Fig. 16(a), the robot turns left and keeps a safety distance dleft wall to the left wall and moves forwardalong the left wall. At the same time, DR section detects the target, then the robot turns right. When the DMsection detects the target, the robot stops to turn right and moves forward to the door.


(a) (b)

Fig. 16. (a) Move along the left wall, DR detects target, robot turns right to track the target. (b) When target is in DMsection, robot moves forward to desired door.

Fig. 17. If the DL section detects the door, the robot will turn left and correct its direction to aim for the door and doorplate.

In Fig. 17, if the door appears in the DL section and the distance is of a specified value, then the robotwill turn left. When the door appears in the DM section, the robot will aim for the door and doorplate.

4.3. Experimental DemonstrationWhen the WMR finds the destination door, the WMR will recognize the room number first, and then verifythe number. If the room number is the desired target, the WMR moves and approaches to the door. If thetag UID of the room is confirmed, then knock the door and wait for door open. After completing documentdelivery, the WMR will see a mission complete pattern. The WMR then moves out of the room. Figure 18shows the sequence of experiment results.

5. CONCLUSION

This paper presents an intelligent scheme based on fuzzy control, RFID, image processing, and adaptiveresonance theory to control an omnidirectional WMR for delivering documents automatically. In imageprocessing, a webcam and vision builder is integrated to process image and to navigate the WMR to the des-tination. A normal image is in RGB color space, however, contact of three color components and produced


(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

(j) (k) (l)

(m) (n) (o)

Fig. 18. Sequence of experimental results: (a) Starting position, (b) Detecting doors, (c) Image from webcam, theframed door is the target, (d) Moving forward to first target, (e) Correct position to aim room plate and door, (f)Capture room number and recognize room number, (g) Check destination number, (h) Approach to the door and readtag UID, (i) Verify UID, (j) Approach door and knock door three times and waiting for door to open, (k) Door isopened, move into room and wait for command to leave room, (l) When circle pattern appears, it means mission iscompleted, (m) Turn around and leave the room, (n) Document delivery service is complete, move back to the startingposition, (o) Arrive at starting position.


color is not so intuition and susceptible to light interference. So the HSL color space is used to solve thisproblem for light interference. The median filter is applied to filter out noise before the number recognition,it can solve the problem of unstable and noise image. Pattern recognition uses color segmentation to recog-nize specified pattern which is needed for target tracking. The control scheme uses human-machine interfaceby the LabVIEW 2012, and integrates the MATLAB codes and image information by vision builder to con-trol the WMR to track target and reach the destination. RFID reader and tag’s UID can verify destinationagain to avoid recognition error from image processing. Experiment results show that the proposed con-trol scheme can drive the omnidirectional WMR to deliver documents automatically. The proposed controlscheme uses less external sensors than other studies. In our study, active sensors are installed in the WMR,the WMR can search the destination without passive auxiliary image devices.

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