An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

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Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.

[1] Hwang, Y. K.; Ahuja, N., “Gross motion planning: a survey,” ACM Computing Surveys (CSUR) vol. 24, no.3, 1992, pp.219-291.

[2] Sridharan, K.; Priya T. K., “A parallel algorithm for constructing reduced visibility graph and its FPGA implementation.” Journal of Systems Architecture, vol. 50, no.10, 2004, pp.635-644.

[3] Bhattacharya, P.; Gavrilova, M. L., “Roadmap-based path planning-Using the Voronoi diagram for a clearance-based shortest path,” Robotics & Automation Magazine, IEEE, vol.15, no.2, 2008, pp.58-66.

[4] Garrido, S.; Moreno, L.; Abderrahim, M.; Martin, F., “Path planning for mobile robot navigation using Voronoi diagram and fast marching,” Int. J. Robot. Autom. vol. 2, no.1, 2011, pp.42-64.

[5] O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Proceedings of IEEE International conference on Robotics and Automation, vol. 2, Stanford, CA, March 1985, pp.500-505.

[6] Lai, L. C.; Wu, C. J.; Shiue, Y. L., “A potential field method for robot motion planning in unknown environments,” Journal of the Chinese institute of engineers, vol.30, no.3, 2007, pp.369-377.

[7] J., Koren; Y. Borenstein, “Real-Time obstacle avoidance for fast mobile robot,” IEEE Transaction on Systems, Man, and Cybernetics, vol. 19, no. 5, Sep/Oct 1989, pp.1179-1187.

[8] Y., Borenstein; J. Koren, “Potential field methods and their inherent limitations for mobile robot navigation,” in Proceedings of the IEEE International Conference on Robotics and Automation, vol.2, 1991, pp.1398-1404.

[9] S. S. Ge; Y. J. Cui, “New Potential Functions for Mobile Robot Path Planning,” IEEE Transaction on Robotics and Automation, vol. 16, no. 5, Oct. 2000, pp.615-620.

[10] Borenstein, J.; Koren, Y., “Real-time obstacle avoidance for fast mobile robots,” Systems, Man and Cybernetics, IEEE Transactions on, vol.19, no.5, Sept.-Oct. 1989, pp.1179-1187.

[11] Yim, W. J.; Park, J. B. “Analysis of mobile robot navigation using vector field histogram according to the number of sectors, the robot speed and the width of the path,” Control, Automation and Systems (ICCAS), 2014 14th International Conference on, vol., no., 22-25 Oct. 2014, pp.1037-1040.

[12] Chaomin Luo; Yang, S.X.; Krishnan, M.; Paulik, M., “Autonomous vehicle navigation and mapping with local minima avoidance paradigm in unknown environments,” World Automation Congress (WAC), 2014, pp.823-828

[13] Jiea, D.; Xueming, M.; Kaixiang, P., “IVFH*: Real-time dynamic obstacle avoidance for mobile robots,” Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on, vol., no., 7-10 Dec. 2010, pp.844-847.

[14] Bo You; Jiangyan Qiu; Dongjie Li, “A novel obstacle avoidance method for low-cost household mobile robot,” Automation and Logistics, 2008. ICAL 2008. IEEE International Conference on, vol., no., 1-3 Sept. 2008, pp.111-116.

[15] Yata, T.; Kleeman, L.; Yuta, S. I., “Wall following using angle information measured by a single ultrasonic transducer,” Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on, vol.2, no., 16-20 May 1998, pp.1590-1596.

[16] Hanafi, D.; Abueejela, Y. M.; Zakaria, M. F., “Wall Follower Autonomous Robot Development Applying Fuzzy Incremental Controller,” Intelligent Control and Automation, vol. 4, no.1, 2013, pp.18-25

[17] Ding, C. J.; Duan, P.; Zhang, M. L.; Han, Y. H., “Wall Following of Mobile Robot Based on Fuzzy Genetic Algorithm of Linear Interpolating,” Fuzzy Information and Engineering, vol. 2., Springer Berlin Heidelberg, 2009, pp.1579-1589.

[18] Gavrilut, I.; Tiponut, V.; Gacsadi, A.; Tepelea, L., “Wall-following method for an autonomous mobile robot using two IR sensors,” WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering. Eds. N. E. Mastorakis, et al. No. 12. WSEAS, 2008.

[19] R. Glasis; A. Komoda; S.A.M. Gielen, “Neural network dynamics for path planning and obstalce avoidance,” Neural Networks, vol. 8, no. 1, 1995, pp. 125-133.

[20] C. C. Chang; K. T. Song, “Environment prediction for a mobile robot in a dynamic environment,” IEEE Transaction on Robotics and Automation, vol. 13, no. 6, 1997, pp.862-872.

[21] G. Oriolo, “Real-time map building and navigation for autonomous robots in unknown environment,” IEEE Transaction on Systems, Man, and Cybernetics – Part B: Cybernetics, vol. 28, no. 3, 1998, pp. 316-333.

[22] N. H. C. Yung; C. Ye, “Avoidance of moving obstacles through behavior fusion and motion prediction,” IEEE Int. Conf. on Systems, Man, and Cybernetics, San Diego, CA, USA, 1998, pp. 3424-3429.

[23] Mohanty, P. K.; Parhi, D. R., “Controlling the motion of an autonomous mobile robot using various techniques: a review,” Journal of Advance Mechanical Engineering, vol.1, no.1, 2013, pp.24-39.

[24] Lee, Gim Hee; Marcelo H. Ang Jr. “Mobile Robots Navigation, Mapping, and Localization Part I,” 2009, pp.1072-1079.

[25] Hacene, N.; Mendil, B., “Autonomous Navigation and Obstacle Avoidance for a Wheeled Mobile Robots: A Hybrid Approach,” International Journal of Computer Applications vol. 81, no.7, 2013, pp.34-37.

[26] Atyabi, A.; Powers, D. M., “Review of classical and heuristic-based navigation and path planning approaches,” International Journal of Advancements in Computing Technology, vol. 5, no.14, 2013.

[27] Buniyamin, N.; Wan N. W. A. J.; Sariff, N.; Mohamad, Z., “A simple local path planning algorithm for autonomous mobile robots,” International journal of systems applications, Engineering & development, vol. 5, no. 2, 2011, pp.151-159.

[28] Masehian, E.; Sedighizadeh, D., “Classic and heuristic approaches in robot motion planning-a chronological review,” World Academy of Science, Engineering and Technology, vol. 23, 2007, pp.101-106.

[29] Li, G.; Tamura, Y.; Yamashita, A.; Asama, H., “Effective improved artificial potential field-based regression search method for autonomous mobile robot path planning,” International Journal of Mechatronics and Automation, vol. 3, no.3, 2013, pp.141-170.

[30] L. Tang; S. Dian; G. Gu; K. Zhou; S. Wang; X. Feng, “A Novel potential field method for obstacle avoidance and path planning of mobile robot,” 3rd IEEE Int. Conf. on Computer Science and Technology (ICCSIT), vol. 9, no. 1, 2010, pp. 633-637.

[31] Chen, L., “UUV path planning algorithm based on virtual obstacle,” Mechatronics and Automation (ICMA), 2014 IEEE International Conference on. IEEE, 2014.

[32] Lu, W.; Zhang, G.; Ferrari, S., “An Information Potential Approach to Integrated Sensor Path Planning and Control,” Robotics, IEEE Transactions on, vol.30, no.4, Aug. 2014, pp.919-934

[33] Doria, N. S. F.; Freire, E. O.; Basilio, J. C., “An algorithm inspired by the deterministic annealing approach to avoid local minima in artificial potential fields,” Advanced Robotics (ICAR), 2013 16th International Conference on, vol., no., 25-29 Nov. 2013, pp.1-6.

[34] Guanghui Li; Yamashita, A.; Asama, H.; Tamura, Y., “An efficient improved artificial potential field based regression search method for robot path planning,” Mechatronics and Automation (ICMA), 2012 International Conference on, vol., no., 5-8 Aug. 2012, pp.1227-1232.

[35] Ya-Chun, C.; Yamamoto, Y., “Online deadlock avoidance scheme of wheeled mobile robot under the presence of boxlike obstacles,” Advanced Intelligent Mechatronics. Proceedings, 2005 IEEE/ASME International Conference on, vol., no., 24-28 July 2005, pp.1535-1540.

[36] Li, C.; Cui, G.; Lu, H., “The design of an obstacle avoiding trajectory in unknown environment using potential fields,” Information and Automation (ICIA), 2010 IEEE International Conference on, vol., no., 20-23 June 2010, pp.2050-2054.

[37] Rezaee, H.; Abdollahi, F., “Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts,” Advanced Intelligent Mechatronics (AIM), 2012 IEEE/ASME International Conference on, vol., no., 11-14 July 2012, pp.1-6.

[38] Lee, J.; Nam, Y.; Hong, S., “Random force based algorithm for local minima escape of potential field method,” Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on. IEEE, vol., no., 7-10 Dec. 2010, pp.827-832.

[39] Sugiyama, S.; Yamada, J.; Yoshikawa, T., “Path planning of a mobile robot for avoiding moving obstacles with improved velocity control by using the hydrodynamic potential,” Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, vol., no., 18-22 Oct. 2010, pp.1421-1426.

[40] Song, Q.; & Liu, L., “Mobile robot path planning based on dynamic fuzzy artificial potential field method,” International Journal of Hybrid Information Technology vol.5, no.4, 2012, pp.85-94.

[41] Vadakkepat, P.; Tan, K. C.; Ming-Liang, W., “Evolutionary artificial potential fields and their application in real time robot path planning,” Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. Vol. 1. IEEE, 2000.

[42] Melingui, A.; Chettibi, T.; Merzouki, R.; Mbede, J.B., “Adaptive navigation of an omni-drive autonomous mobile robot in unstructured dynamic environments,” Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on, vol., no., 12-14 Dec. 2013, pp.1924-1929.

[43] Ji-Wung Choi, “A potential field and bug compound navigation algorithm for nonholonomic wheeled robots,” Innovative Engineering Systems (ICIES), 2012 First International Conference on, vol., no., 7-9 Dec. 2012, pp.166-171.

[44] Mohamed, E.F.; El-Metwally, K.; Hanafy, A.R., “An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning,” Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on, vol., no., 15-18 June 2011, pp.555-559.

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

Journal Information

CiteScore 2017: 5.00

SCImago Journal Rank (SJR) 2017: 0.492
Source Normalized Impact per Paper (SNIP) 2017: 2.813

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