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Indoor Navigation System of Omni-Directional Mobile Robot Based on Static Obstacles Avoidance


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DOI: https://doi.org/10.15866/ireaco.v13i1.18346

Abstract


This paper introduces and establishes an autonomous indoor navigation guidance system for the Omni-directional mobile robot along with a static obstacles avoidance. The mobile robot is fitted with a Kinect camera as the key sensor to operate in an indoor environment. The proposed navigation guidance system has three main functions; target searching for actions, avoidance of collision, and position. The target searching for actions uses fuzzy Proportional-Derivative (PD) controller to adjust the robot-heading angle to the target point. The collision avoidance consists of a controller that is designed to provide the robot with the proper heading angle to avoid collision with obstacles. Concerning the confinement, two strategies for position are implemented. The primary strategy uses encoders and compass sensor as a feedback signal, while the second strategy uses Kinect sensor in a combination with odometry to distinguish landmarks spots and to right robot position as required. An in-house built three wheeled Omni-directional mobile robot platform is designed to validate the proposed navigation guidance system. The results demonstrate the robot platform ability to navigate to its target autonomously with a minimal error, and to avoid collision with static obstacles.
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Keywords


Robot; Indoor Navigation; Static Obstacles Avoidance; Omni Wheel; Fuzzy CONTROL; Depth Sensor; Kinect

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References


Caceres, C., Rosário, J., Amaya, D., Design, Simulation, and Control of an Omnidirectional Mobile Robot, (2018) International Review of Mechanical Engineering (IREME), 12 (4), pp. 382-389.
https://doi.org/10.15866/ireme.v12i4.13974

Massachusetts Institute of Technology, Technique helps robots find the front door: Navigation method may speed up autonomous last-mile delivery, ScienceDaily, Nov. (2019).

Nasret, A., Mahmood, Z., Optimization and Integration of RFID Navigation System by Using Different Location Algorithms, (2019) International Review of Electrical Engineering (IREE), 14 (4), pp. 291-301.
https://doi.org/10.15866/iree.v14i4.16684

Alijani, F., Sensor Integration for Autonomous Docking of a Mobile Robot with Omnidirectional Platform, (2017) International Journal on Information Technology (IREIT), 5 (3), pp. 66-76.
https://doi.org/10.15866/ireit.v5i3.11250

Mingjing Gao, Min Yu, Hang Guo, Yuan Xu, Mobile robot indoor positioning based on a combination of visual and inertial sensors, Sensors, 19 (8) (2019).
https://doi.org/10.3390/s19081773

Weihua Chen and Tie Zhang, An indoor mobile robot navigation technique using odometry and electronic compass, International Journal of Advanced Robotic Systems, 3 (3) (2017).
https://doi.org/10.1177/1729881417711643

Faiza Gul, Wan Rahiman & Syed Sahal Nazli Alhady, A comprehensive study for robot navigation techniques, Cogent Engineering, 6 (1) (2019).
https://doi.org/10.1080/23311916.2019.1632046

Jaradat, M.A.K., M.H. Garibeh, and E.A. Feilat, Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field. Soft Computing. 16 (1) (2012).
https://doi.org/10.1007/s00500-011-0742-z

Ismail, I.I. and M.F. Nordin, Reactive navigation of autonomous guided vehicle using fuzzy logic, Student Conference on Research and Development, Shah Alam, Malaysia, (2002) pp. 153-156.
https://doi.org/10.1109/scored.2002.1033080

Maaref, H. and C. Barret, Sensor-based navigation of a mobile robot in an indoor environment, Robotics and Autonomous systems, 38 (1) (2002) pp. 1-18.
https://doi.org/10.1016/s0921-8890(01)00165-8

Yukawa, T., et al., Development of an omni-directional mobile robot with two or more cameras and navigation based on cell map information, 32nd Annual Conference on IEEE Industrial Electronics, (2006) pp. 213-218.
https://doi.org/10.1109/iecon.2006.347426

Chatzakos, P., et al., On the development of a modular external-pipe crawling omni-directional mobile robot, Industrial Robot, 33 (4) (2006) pp. 291-297.
https://doi.org/10.1108/01439910610667917

Jun Qian , Bin Zi , Daoming Wang, Yangang Ma and Dan Zhang, The design and development of an omni-directional mobile robot oriented to an intelligent manufacturing system, Sensors, 17 (9) (2017).
https://doi.org/10.3390/s17092073

Pinto, A.M., Moreira, A.P. & Costa, P.G., A localization method based on map-matching and particle swarm optimization, Journal of Intelligent Robot System, 77 (2015) pp. 313–326.
https://doi.org/10.1007/s10846-013-0009-2

Faikul Umam, Control of omni-directional robot using accelerometer sensor on android smartphone, International Journal of Engineering Research & Technology, 3 (1) (2014).

Miah, M.S., Gueaieb, W., RFID-based mobile robot trajectory tracking and point stabilization through on-line neighboring optimal control, Journal of Intelligent Robot System, 78 (2015) pp. 377–399.
https://doi.org/10.1007/s10846-014-0048-3

Qian, K. and A. Song, Autonomous navigation for mobile robot based on a sonar ring and its implementation, 8th IEEE International Symposium on Instrumentation and Control Technology, (2012) pp. 47-50.
https://doi.org/10.1109/isict.2012.6291646

Sobers L. X. Francis, Sreenatha G. Anavatti, and Matthew Garratt, Real time path planning module for autonomous vehicles in cluttered environment using a 3D sensor, International Journal of Vehicle autonomous system, 14 (1) (2018).
https://doi.org/10.1504/ijvas.2018.093106

Ni, J., et al., An improved VFF approach for robot path planning in unknown and dynamic environments, Mathematical Problems in Engineering, (2014).
https://doi.org/10.1155/2014/461237

Zi, B., J. Lin, and S. Qian, Localization, obstacle avoidance planning and control of a cooperative cable parallel robot for multiple mobile cranes, Robotics and Computer-Integrated Manufacturing, 34 (2015) pp. 105-123.
https://doi.org/10.1016/j.rcim.2014.11.005

Zi, B., H. Sun, and D. Zhang, Design, analysis and control of a winding hybrid-driven cable parallel manipulator, Robotics and Computer-Integrated Manufacturing, 48, (2017) pp. 196-208.
https://doi.org/10.1016/j.rcim.2017.04.002

N.A. Zainuddin, Y.M. Mustafah, Y.A.M. Shawgi, N.K.A.M. Rashid, Autonomous navigation of mobile robot using Kinect sensor, International Conference on Computer and Communication Engineering, (2014) pp. 28-31.
https://doi.org/10.1109/iccce.2014.21

Mohammd Abdul Qayum, Nazmun Nahar, Nafiul Alam Siddique, Z. M. Saifullah, Interactive intelligent agents with creative minds: Experiments with mobile robots in cooperating tasks by using machine learning, IEEE International Conference on Imaging Vision & Pattern Recognition, (2017) pp. 1-6.
https://doi.org/10.1109/icivpr.2017.7890884

Hendra Marta Yudha, Tresna Dewi, Nurul Hasana, Pola Risma, Yurni Oktarini, Sari Kartini, Performance comparison of fuzzy logic and neural network design for mobile robot navigation, International Conference on Electrical Engineering and Computer Science, (2019) pp. 79-84.
https://doi.org/10.1109/icecos47637.2019.8984577

Bouchiba, F., Wahid, N., Neuro-Fuzzy Navigation of a Mobile Robot in an Unknown Environment, (2015) International Review of Automatic Control (IREACO), 8 (3), pp. 220-227.
https://doi.org/10.15866/ireaco.v8i3.6102

Jallouli, M., Chtourou, M., Fuzzy Controller Optimization for a Mobile Robot Navigation, (2017) International Journal on Engineering Applications (IREA), 5 (5), pp. 170-177.

Elkari, B., Ayad, H., El Kari, A., Mjahed, M., A New Approach of Fusion Behavior-Based Fuzzy Control for Mobile Robot Navigation, (2017) International Review of Automatic Control (IREACO), 10 (1), pp. 13-22.
https://doi.org/10.15866/ireaco.v10i1.10390

El Kari, B., Ayad, H., El Kari, A., Mjahed, M., Pozna, C., Design and FPGA Implementation of a New Intelligent Behaviors Fusion for Mobile Robot Using Fuzzy Logic, (2019) International Review of Automatic Control (IREACO), 12 (1), pp. 1-10.
https://doi.org/10.15866/ireaco.v12i1.14802

Qian, J., et al., The design and development of an omni-directional mobile robot oriented to an intelligent manufacturing system. Sensors, 17 (9) 2017.
https://doi.org/10.3390/s17092073

Waldron, K. and J. Schmiedeler, Kinematics, Springer Handbook of Robotics, (2008) pp. 9-33.

Jie-Tong Zou, The development of the omnidirectional mobile home care robot, Mobile Robots - Current Trends, computer Science, (2011).
https://doi.org/10.5772/26767

Kalmar-Nagy, T., R. D'Andrea, and P. Ganguly, Near-optimal dynamic trajectory generation and control of an omnidirectional vehicle, Robotics and Autonomous Systems, 46 (1) (2004) p. 47-64.
https://doi.org/10.1016/j.robot.2003.10.003

Mathworks. Remove small objects from binary image. [cited 2014 01.02.2014]; Available from:
https://de.mathworks.com/help/images/ref/bwareaopen.html

Atherton, T.J. and D.J. Kerbyson, Size invariant circle detection. Image and Vision computing, 17 (11) (1999) pp. 795-803.
https://doi.org/10.1016/s0262-8856(98)00160-7

Kinect for Windows Sensor Components and Specifications. [cited 2014 15/05]; Available from:
http://msdn.microsoft.com/en-us/library/jj131033.aspx

University, R.A. RWTH - Mindstorms NXT Toolbox for MATLAB. 2014 [cited 2014 01.02.2014]; Available from:
http://www.mindstorms.rwth-aachen.de/


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