Vision Based Indoor Surveillance Patrol Robot Using Extended Dijkstra Algorithm in Path Planning Manuscript Received: 18 October 2021, Accepted:4 November 2021, Published: 15 December 2021
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Abstract
Vision based patrol robot has been with great interest nowadays due to its consistency, cost effectiveness and no temperament issue. In recent times, Global positioning system (GPS) has been cooperated with Global Navigation Satellite System (GNSS) to come out with better accuracy quality in positioning, navigation, and timing (PNT) services to locate a device. However, such localization service is yet to reach any indoor facility. For an indoor surveillance vision based patrol robot, such limitation hinders its path planning capabilities that allows the patrol robot to seek for the optimum path to reach the appointed destination and return back to its home position. In this paper, a vision based indoor surveillance patrol robot using sensory manipulation technique is presented and an extended Dijkstra algorithm is proposed for the patrol robot path planning. The design of the patrol robot adopted visual type sensor, range sensors and Inertia Measurement Unit (IMU) system to impulsively update the map’s data in line with the patrol robot’s current path and utilize the path planning features to carry out obstacle avoidance and re-routing process in accordance to the obstacle’s type met by the patrol robot. The result conveyed by such approach certainly managed to complete multiple cycles of testing with positive result.
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References
T. Mashrik, H. Zunair and M. F. Karin, “Design and Implementation of Security Patrol Robot Using Android Application,” Asia Modelling Symp., pp. 77-82, 2017.
Y. Xu, T. Liu, B. Sun, Y. Zhang, S. Khatibi and M. Sun, “Indoor Vision/INS Integrated Mobile Robot Navigation Using Multimodel-Based Multifrequency Kalman Filter,” Math. Prob. in Eng., vol. 2021, no. 6694084, pp. 1-8, 2021.
J. Sa, K. Lim and J. Kim, “The Research of Multi-Layer-Based on Path Planning for Generating Optimal Path,” IEEE Transport. Electrification Conf. and Expo, Asia-Pacific, pp. 896-899, 2016.
I. Gorovyi, A. Roenko, A. Pitertsev, I. Chervonyak and V. Vovk, “Real-time System for Indoor User Localization and Navigation Using Bluetooth Beacons,” IEEE First Ukraine Conf. on Elec. and Comp. Eng., pp. 1025-1030, 2017.
O. M. Elfadil, Y. M. Alkasim and E. B. Abbas, “Indoor Navigation Algorithm for Mobile Robot Using Wireless Sensor Networks,” Int. Conf. on Comm., Control, Comp. and Elec. Eng, pp. 1-5, 2017.
O. A. Gbadamosi and D. R. Aremu, “Design Of A Modified Dijkstra’s Algorithm For Finding Alternate Routes For Shortest-Path Problems With Huge Costs,” Int. Conf. in Math., Comp. Eng. and Comp. Sci., pp. 1-6, 2020.
E. Malayjerdi, H. Kalani and M. Malayjerdi, “Self-Tuning Fuzzy PID Control of A Four-Mecanum Wheel Omni-directional Mobile Platform,” Iranian Conf. on Elec. Eng., pp. 816-820, 2018.
R. S. Vidhya, P. Ashritha, M. A. Kumar and N. Rajesha, “Image Noise Declining Approaches by Adopting Effective Filters,” Int. Conf. on Emerging Trends in Sci. and Eng., pp. 1-5, 2019.
L. Q. Tan and B. He, “The Research of Implementation Method of Canny Edge Detection of Video on FPGA,” Int. Conf. on Inform. Sci., Parall. and Distrib. Sys., pp. 1-4, 2020.
I. Baturone and A. A. Gersnoviez, “A Simple Neuro-fuzzy Controller for Car-like Robot Navigation Avoiding Obstacles,” IEEE Int. Fuzzy Sys. Conf., pp. 1-6, 2007.
D. Simon and D. L. Simon, “Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using A Sum-of-Squared-Residuals Approach,” IEEE Trans. on Reliability, vol. 59, pp. 287–296, 2010.
Z. Wang, J. Tan and Z. Sun, “Error Factor and Mathematical Model of Positioning with Odometer Wheel”, Adv. in Mechan. Eng., SAGE Journals, pp. 1-7, 2014.