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A Multi-Operator Based Simulated Annealing Approach for Robot Navigation in Uncertain Environments
Hui Miao
Pages - 50 - 61     |    Revised - 25-02-2010     |    Published - 03-04-2010
Volume - 4   Issue - 1    |    Publication Date - March 2010  Table of Contents
Optimization, MSA, SA, GA, Dynamic Environments
Optimization methods such as simulated annealing (SA) and genetic algorithm (GA) are used for solving optimization problems. However, the computational processing time is crucial for the real-time applications such as mobile robots. A multi-operator based SA approach incorporating with additional four mathematical operators that can find the optimal path for robots in dynamic environments is proposed in this paper. It requires less computation times while giving better trade-offs among simplicity, far-field accuracy, and computational cost. The contributions of the work include the implementing of the simulated annealing algorithm for robot path planning in dynamic environments, and the enhanced new path planner for improving the efficiency of the path planning algorithm. The simulation results are compared with the previous published classic SA approach and the GA approach. The multi-operator based SA (MSA) approach is demonstrated through case studies not only to be effective in obtaining the optimal solution but also to be more efficient in both off-line and on-line processing for robot dynamic path planning. Keywords: Optimization, MSA, SA, GA, Dynamic Environments
CITED BY (9)  
1 Mobadersany, P., Khanmohammadi, S., & Ghaemi, S. A fuzzy multi-stage path-planning method for a robot in a dynamic environment with unknown moving obstacles. Robotica, 1-17.
2 Tazir, M. L., Azouaoui, O., Hazerchi, M., & Brahimi, M. (2015, July). Mobile robot path planning for complex dynamic environments. In Advanced Robotics (ICAR), 2015 International Conference on (pp. 200-206). IEEE.
3 Gholami, S., & Bashirzadeh, R. (2014). A new effective algorithm for on-line robot motion planning. Decision Science Letters, 3(1), 121-130.
4 Padma, T., & Pillai, J. S. (2014). Image Watermarking using PQ Sequences.
5 Belaidi, H., Bentarzi, H., Belaidi, A., & Hentout, A. (2014). Terrain Traversability and Optimal Path Planning in 3D Uneven Environment for an Autonomous Mobile Robot. Arabian Journal for Science and Engineering, 39(11), 8371-8381.
6 Mobadersany, P., Khanmohammadi, S., & Ghaemi, S. (2013, May). An efficient fuzzy method for path planning a robot in complex environments. In Electrical Engineering (ICEE), 2013 21st Iranian Conference on (pp. 1-6). IEEE.
7 Jafarzadeh, H., Gholami, S., & Bashirzadeh, R. (2013). Decision Science Letters.
8 Tanil, Ç. (2012, March). Improved heuristic and evolutionary methods for tactical missile mission planning. In Aerospace Conference, 2012 IEEE (pp. 1-8). IEEE.
9 Hung, J. Y. C. (2011). Investigation of methods for increasing the energy efficiency on unmanned aerial vehicles (UAVS).
1 Google Scholar 
2 Academic Journals Database 
3 ScientificCommons 
4 Academic Index 
5 CiteSeerX 
6 refSeek 
7 iSEEK 
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
1 P. S. Y. Wang, J. Mulvaney, “Genetic-based mobile robot path planning using vertex heuristics,” in Proceedings of the Conference on Cybernetics and Intelligent Systems, vol. 1, Bangkok, Thailand, June 7–9, 2006, pp. 1 – 6.
2 Ahmed Mustafa, Aisha-Hassan A, “Adaptive Emotional Personality Model based on Fuzzy Logic Interpretation of Five Factor Theory,” International Journal of Computer Science and Security, vol. 3, no. 3, pp. 210–215, Sept. 2009.
3 Dzulkifli Mohamad, “Multi Local Feature Selection Using Genetic Algorithm For Face Identification,” International Journal of Computer Science and Security, vol. 1, no. 2, pp. 1–10, Sept. 2007.
4 J. N. Russell, “Artificial Intelligence: A Modern Approach.” Berkeley, CA, USA: Prentice Hall, 2003.
5 L. Hu and Z. Q. Gu, “Research and realization of optimum route planning in vehicle navigation systems based on a hybrid genetic algorithm,” Proceedings of the Institution of Mechanical Engineers Part D – Journal of Automobile Engineering, vol. 222, no. D5, pp. 757–763, May 2008.
6 J. Ayers, “Underwater walking,” Arthropod Structure and Development, vol. 33, no. 3, pp. 347– 360, July 2004.
7 B. Williams and I. Mahon, “Design of an unmanned underwater vehicle for reef surveying,” in Proceedings of the IFAC 3rd Symposium on Mechatronic Systems. Manly NSW, Australia: IEEE, Sept. 15, 2004.
8 S. Chakravorty and J. L. Junkins, “Motion planning in uncertain environments with vision-like sensors,” Automatica, vol. 43, no. 12, pp. 2104–2111, Dec. 2007.
9 Y. Wang, P. W. Sillitoe, and J. Mulvaney, “Mobile robot path planning in dynamic environments,” in Proceedings of the International Conference on Robotics and Automation, vol. 1. Roma: IEEE, Apr. 10–14, 2007, pp. 71–76.
10 P.-Y. Zhang, T.-S. L¨ u, and L.-B. Song, “Soccer robot path planning based on the artificial potential field approach with simulated annealing,” Robotica, vol. 22, no. 5, pp. 563–566, Aug. 2004.
11 A. Stentz, “Optimal and efficient path planning for partially-known environments,” in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, San Diego, CA, USA, May 8–13, 1994, pp. 3310–3317.
12 A. Yahia, A. Stentz, S. Singh, and B. Brummit, “Framed-quadatree path planning for mobile robots operating in sparse environments,” in Proceedings of the IEEE Conference on Robotics and Automation, vol. 1. Leuven, Belgium: IEEE, May 16–20, 1998, pp. 650–655.
13 D. Ferguson and A. Stentz, “Field D*: An interpolation-based path planner and replanner,” in Proceedings of International Symposium on Robotics Research, San Francisco, CA, USA, Oct. 12, 2005, pp. 239–253.
14 A. Stentz, “The focussed D* algorithm for real-time replanning,” In Proceedings of the International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, pp. 1652– 1659, Aug. 20–25, 1995.
15 A. Yahja, S. Singh, and A. Stentz, “An efficient online path planner for outdoor mobile robots operating in vast environments,” Robotics and Autonomous Systems, vol. 32, pp. 129–143, 2000.
16 A. R. Willms and S. X. Yang, “An efficient dynamic system for real-time robot path planning,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 36, no. 4, pp. 755–766, 2006.
17 A. R. Willms and S. X. Yang, “Real-time robot path planning via a distance-propagating dynamic system with obstacle clearance,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 38, no. 3, pp. 884–893, June 2008.
18 H. Miao and Y.-C. Tian, “Robot path planning in dynamic environments using a simulated annealing based approach,” in Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision - ICARCV’2008, Hanoi, Vietnam, Dec. 17–20, 2008, pp. 1253– 1258.
19 Mathworks, “Matlab,” http://www.mathworks.com, retrived on 18 Feb 2009.
20 Sufal Das, Banani Saha, “Data Quality Mining using Genetic Algorithm”, International Journal of Computer Science and Security, vol. 3, no. 2, pp. 105-112. May 2009.
Mr. Hui Miao
Microchip Australia Design Centre, Microchip Technology Inc. - Australia