INTEGRATED VISION GUIDANCE SYSTEMAND ANDROID APPLICATION TO TWO-WHEEL BALANCING ROBOT FOR AUTOMATIC

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN (P): 2249–6890; ISSN (E): 2249–8001 Vol. 10, Issue 3, Jun 2020, 2357–2366 © TJPR Pvt. Ltd.

INTEGRATED VISION GUIDANCE SYSTEMAND ANDROID APPLICATION TO TWO-WHEEL BALANCING ROBOT FOR AUTOMATIC WAREHOUSE APPLICATION ANAN SUEBSOMRAN1, CHANAKARN KLAVOHM2, THANAKAN MALIAKRONG3 & NUCHCHADA WITAYAREE4 1,3,4

King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand 2

King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

ABSTRACT Autonomous and stability driving of two-wheel type of mobile robot presently needs to be continuously studied and researched. This research aims to design and develop a two-wheel mobile robot with stability and motion controllers.

The motion controller is a cascaded series of PD and PID control loops to handle the speed and tilt angle. The vision guidance system is cascaded with a proportional controller to enhance the path maneuver. Integrated IMU sensor and encoder provide the feedback signal of angle and speed while PIXY camera with lane detection provides the path direction feeding to the motion controller. A management software is developed to manage the robot logistic function along a simple warehouse site of 5 pick-up/drop-off stations. From experiments, the robot can carry a load of 1 kg following the path and stop at designated stations within the vicinity of20 cm. By the purposes of this research, the proposed system can achieve the driving and logistical management of the two-wheel robot.

KEYWORDS: Dual-Wheels Robot, Balancing Control, Kalman Filter & Vision Guidance

Received: May 19, 2020; Accepted: Jun 11, 2020; Published: Jul 06, 2020; Paper Id.: IJMPERDJUN2020219

1. INTRODUCTION The idea of a two-wheeled differential drive mobile robot based on the inverted pendulum model has drawn interest from many researches for several years. The nonlinear unstable nature of such system attracts many researchers to contribute many successful and sophisticate models and control systems. The majority is focusing on the control of the dynamics, stability and motion, signal conditioning and interpretations. Various models and controllers have been applied both to explain and control the dynamics of two-wheeled robots. As machine vision has been widely used in autonomous robot and self-driving automobiles, some researches have been studied on integrated vision system to a dual-wheel balancing robot as line tracking [1]. Eventually, the world has seen many decent commercial products based on those control algorithms especially for personal and cargo transportation. For application of transport model stated by [2], they considered the kinematic and dynamic models of two wheel balancing robot and proposed the control method of PID and LQR control design. Functional control of each controller is separated action of selfbalance by LQR and yaw control by PID. Comparative study of controller performance is revealed by experiment and simulation technique. An experiment of two wheel balancing robot control presented by [3] for education purpose. The low cost of Arduino based controller was chosen for electronic control device by the University of Seville. Controller design is adopted for LQR control method. The fuzzy PD controller was implemented for realtime control presented by [4]. The model of dynamic control and simulation was adopted for this research. The

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selection of state feedback control was designed by using LQR control method to obtaining the control gain parameters by Matlab & Simulink simulation.

The other technique of self-balancing control of two-wheel mobile robot based on a control moment gyroscope module [5]. This study tried to improve the balancing of mobile robot with minimize movement, and estimated disturbance for improve robot performance and stability. Validation of system design purpose was shown by simulation and experiment.

Control and balancing of two-wheel mobile robot was applied Takagi Sugeno Fuzzy logic technique [6]. For design membership function to constructing fuzzy relations, input signal to measure such as tilting angle, acceleration and velocity.

Output signal was designed as acceleration and velocity signals to drive the motor equipped at each wheel of robot for maintain stability approach. In addition, fuzzy control was also applied to control for the balance of two-wheel mobile robot [7]. Fuzzy was designed by rule based method for constructing control signal actuating to wheels of robot. Controller for experimental design was adopted by using DSPIC microcontroller. In other balancing control, Two-Wheel Inverted Pendulum (TWIP) was designed by [8]. Mathematic formulation of dynamic model is derived from TWIP configuration, and controller was designed by using LQG & MPC techniques. The results stated that LQG is better performance than MPC control. This design is based on MATLAB-2016 software.

In this paper, we are investigating more of such integration to a dual-wheel balancing robot with more complex environment. We explore the methods which have been investigated and the controllers which have been used, first for balancing and movement of two-wheeled robots on flat terrain, then for two-wheeled robots in other situations, apply the real-time image processing techniques to enhance stability and other decisions of a Dual-Wheels robot. The supervisory software is developed as android application to demonstrate the logistic management capability. The application is also used for user input such as start/stop location, speed, acceleration, and pausing time.

2. SYSTEM OVERVIEW 2.1 Dual-Wheel Balancing Robot The design of our Dual-Wheel Balancing Robot is simply follow [1] and [2], and slightly adjust some parameters due to different mass distribution and motor wheel characteristics.

Figure 1: a) 3D Model,

b) Prototype

c) cg Calculation (using Solid Work Tools).

2.2 Sensory and Vision System A guidance system processes the instance image acquired by CCD camera to detect the guiding line, predicting the path of

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motion and feeding control parameters to a predictive motion control loop. The predictive motion control loop is the combination of modified nested PID/PD controllers with feedback of angle and speed information measured by IMU sensor and encoder is handling the speed and balance controls. To keep the minimum budget, we have chosen to implement the Sensory and Vision System on microprocessor based as indicated in figure 2.

Figure 2: Sensory, Vision and Control Components.

2.3 Testing Site Allocating one of our laboratory of 6 by 10 meter square to form a small warehouse with three pick-up / drop-off zone, i.e.

“A”, “B”, and “C” with color tape for tracking line.

Figure 3: a) Site Layout as Appear on GUI.

b) Testing Area.

3. CONTROL SCHEMES 3.1 Signal Condition After connecting all of the equipment as in figure 2, the reading from IMU is very noisy and has some noticeable time delay as figure 4a. Referring to our previous work, we apply Kalman Filter (KF) which is a well-known algorithm for estimation www.tjprc.org

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and prediction especially when data has a lot of noise.

Figure 3. a) IMU Signal Reading.

b) IMU Reading after KF.

To test our signal conditioning system, we rock the robot back and forth to read the tilt angle. We also command a nearby DC drive of a 60 watts motor with some sequence of speed to see how KF can handle the interference. The result, as in figure 4, is not as good as we have expected but this rough model of KF should work.

Figure 4: IMU Reading after KF (with External Rocking and Small EMI).

Tilt angle reading is tested and calibrated using clinometer application on an android phone, and carefully adjust the zero-span. We finally narrow down the error from +/- 5 degree to +/- 0.3 degree.

Figure 5: Tilt Angle is Calibrated by Clinometer Application at the Origin of the CG Plane.

3.2 Image Processing Pixy2 or CMUcam5 has built-in algorithms that detect and track lines for use with line-following robots. We simply mount Pixy2 image sensor to the center of robot and connect the SPI to Arduino. The deviation is measure by the pixel amount between the lane and center line (red line in figure 6), and fed to a P-Controller, as indicated in figure 7.

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Figure 6: Setup PIXY2 with PixyMON Tools.

Figure 7: Line Position (or Deviation) is Calculated by PIXY2 and Fed to a P-Controller.

3.3 Controller Design Balancing control and motion control is designed by cascade control loop. Vision guidance for navigation is designed by the parallel control loop for control the direction of robot movement along the specific path.

Figure 8: Setup PIXY2 with PixyMON Tools.

Form figure 8, the command set points of balancing and speed control loop are derived from Android application.

Then PID control is designed by using feedback control. Up right angle is measured from encoder, and position and velocity of robot are measured by IMU sensor. The PID gains are set by Kp=10, Ki=0.1, Kd=0.01, and velocity loop is set PID gains byKp=2, Ki=0.000001, Kd=0.0005 for balancing loop for experiment set up. The first loop is speed control loop with PD

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controller gains by Kp=0.07, Kd=0.01. The angle command set point is specified to zero degree. The lower control loop is parallel loop of control is also designed with P control mode by set Kp=0.15 for guidance robot move along the specific path 3.4 Management Software We implemented and android application with MIT App Inventer2 to pass the robot setup parameters, travelling routing such as stopping points, and amount of rounds to go. The communication is transfer over a bluetooth connectivity to ESP HC-05 Bluetooth module shield on the Arduino. The connectivity and commands is verified to be ok within the range of 10 meters.

Figure 9: GUI on Android Application for Routing Command.

4. EXPERIMENTS 4.1 Self-Balancing Test We set the robot in a fix location on the map in stand still (standby) motion. With some fine tuning of the controller gains, we varied the carrying load of the robot as figure 10. For self balancing without load the robot can handle within +/- 1 degree back and forward leaning. As the load is increased to 0.5 kg and 1 kg, the robot can still balancing within +/- 2.5 degrees.

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Figure 10: Various Load Balancing Performance of Two Wheel Robot in Stand Still Mode.

4.2 Line Tracing Test As we command the robot to move along a straight line, the robot can bounce back and move forward along the line as example in figure 11.

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Figure 11: Deviation Control when the Robot is Moving with the Speed of 8 cm/sec.

4.3 Management and Positioning Test As we command the robot to move along the testing loop, each station is indicated with a rectangle outlining black tape of 60x60 centimeters as parking spot. The management android application can command the robot to any spot correctly and within the vicinity of black tape (station boundary). The accuracy is +/- 2 cm along the side of the robot (x-axis in figure 12b.) As the robot tries to balance itself at the station, some occasionally the robot is entrust to adjust the tilt angle and stop at upto 15 centimeter about the center stopping point along the Y-axis.

Station

Figure 12: a) Station Layout.

b) Stopping Point for Pick-up / Drop-off (Parking Spot).

5. CONCLUSIONS The experiment is constructed to verify for position accuracy, speed limitation, and stability. From the results, our Integrated Vision Guidance System can handle less than 19 cm. of positioning error, smooth limited velocity control of 8 cm/s. The motion control system also can carry load of 1 kg at 45 cm from floor level within +/- 3 degree without sudden jerks. In conclusion, the integrated d vision guidance system to dual-wheel balancing robot can be achieved by propose of research.

The integrated vision guidance system presented in this paper has shown significantly prove the dexterity of the dual-wheel balancing robot. As advanced trajectory parameters have been processed and fed to the beforehand to a classical nested-control system, the dual-wheel robot can handle the desired trajectory quite well. The experiments confirm the stability of balancing performance, speed control, tilt angle, and surely tracking and stopping over the test site.

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REFERENCES 1.

T. Jung, S. Jung, “Line Tracking Control of a Two-wheel Balancing Mobile robot”, IEEE International Conference on Industrial Technology, 2014, 91-95.

2.

W. An and Y. Li, "Simulation and control of a two-wheeled self-balancing robot," 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, 2013, pp. 456-461, doi: 10.1109/ROBIO.2013.6739501.

3.

C. Gonzalez, D. Muñoz, La Peña, Low cost two-wheels self-balancing robot for control education, IFAC-Papers OnLine, Volume 50, Issue 1, July 2017, Pages 9174-9179.

4.

Hirur, Shris Hail S., and S. Uwarna Torgal. "Rfid Technique: Barriers to Overcome in the Indian Supply Chain Management."International Journal of Business and General Management (IJBGM) 3. 3, July 2014, 53-58

5.

Junfeng Wu, Wanying Zhang, and Shengda Wang, A Two-Wheeled Self-Balancing Robot with the Fuzzy PD Control Method, Mathematical Problems in Engineering, Volume 2012, Article ID 469491, 13 pages, doi:10.1155/2012/469491

6.

Ji-Hyun Park and Baek-Kyu Cho, Development of a self-balancing robot with a control moment gyroscope, International Journal of AdvancedRobotic Systems, March-April 2018: 1–11, DOI: 10.1177/1729881418770865.

7.

Jain, Vishal, Gagandeep Singh Narula, and Mayank Singh. "Implementation of data mining in online shopping system using tanagra tool." International Journal of Computer Scienceand Engineering 2.1 (2013): 47-58.

8.

Abhijeet Singh Chouhan, Dayal R. Parhi, AnimeshChhotray, Control and Balancing of Two-Wheeled Mobile Robots using Sugeno Fuzzy Logic in the domain of AI Techniques, International Journal of Scientific & Engineering Research Volume 9, Issue 4, April-2018, pp. 111-121.

9.

S. Miasa, M. Al-Mjali, A. Al-Haj Ibrahim and T. A. Tutunji, "Fuzzy control of a two-wheel balancing robot using DSPIC," 2010 7th International Multi- Conference on Systems, Signals and Devices, Amman, 2010, pp. 1-6, doi: 10.1109/SSD.2010.5585525.

10. Shravanthi, A. R., and K. Mahendran. "Cold storage warehouse units in India-a region-wise comparative study." International Journal of Agricultural Science and Research (IJASR) 5.5 (2015): 53-62.

11. S. Khatoon, D. K. Chaturvedi, N. Hasan and M. Istiyaque, "Optimal Controller Design for Two Wheel Mobile Robot," 2018 3rd International Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH), Ghaziabad, India, 2018, pp. 1-5, doi: 10.1109/CIPECH.2018.8724314.

12. Deshmukh, Pramod A. "Review of Lean 6σ, Fusion of Quality and Speed."IMPACT: International Journal of Research in Engineering & Technology (IMPACT: IJRET) 2. 2, Feb 2014, 279-286

AUTHOR PROFILE

Anan Suebsomran received the B.S. degree in mechanical engineering from King Mongkut’s University North Bangkok (KMUTNB), Thailand, in 1996, and received the M.Eng. and D.Eng. in mechatronics from the Asian Institute of Technology (AIT), Thailand, in 1999 and 2006, respectively. Currently, he is working at Department of Teacher Training in Mechanical Engineering, faculty of Technical Education. King Mongkut’s University of Technology North Bangkok (KMUTNB). His research interests include robotics, mechatronics, measurement, and control systems www.tjprc.org

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ChanakarnKlavohm, B.S., M.S. in Electrical and Computer Engineering (ECE), Honors Carnegie Mellon University 1995.Currently, he is working at Department of Production Engineering (Mechatronic Program), faculty of Engineering. King Mongkut’s Universit y of Technology Thonburi (KMUTT).

Thanakan Maliakrong received the B.S. degree in mechanical engineering from King Mongkut’s University North Bangkok (KMUTNB), Thailand, in 2018 at Department of Teacher Training in Mechanical Engineering, faculty of Technical Education. King Mongkut’s University of Technology North Bangkok (KMUTNB). His research interests include robotics, mechatronics, and controls systems

NuchadaWitayaaree received the B.S. degree in mechanical engineering from King Mongkut’s University North Bangkok (KMUTNB), Thailand, in 2018 at Department of Teacher Training in Mechanical Engineering, faculty of Technical Education. King Mongkut’s University of Technology North Bangkok (KMUTNB). His research interests include robotics, mechatronics, and controls systems

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