Path Planning Algorithm in Complex Environment: A Survey
Atikah Janis & Abdullah Bade
Download pdf.
Keywords: Path planning, complex environment, autonomous robot
A b s t r a c t
Path finding algorithm is a very challenging problem for navigating autonomous virtual robots in complex environment. A reliable navigation system must be able to identify the virtual robot current location, avoid any collisions and determine the smooth path trajectory of the object. At present, the needs to produce systematic and efficient path finding algorithm with impressive collision scheme has led number of researchers to conduct various experiments to improve and modify the existing algorithms in order to solve several issues in path planning algorithm with collision avoidance for autonomous virtual robot. This paper presents series of path planning algorithms for the last 10 years in order to solve the navigation of autonomous virtual robot in complex environment. We believe that all algorithms reviewed in this paper will give researchers in the field of virtual environment, collision detection and robotic about some fundamental background, issues and challenges on how navigation procedures of autonomous virtual robot in such a complex environment works.

References
[1]       Abrar, M., Marwah, M. & Khaled, M. (2015). Multi-Sensor Based Collision Avoidance Algorithm for Mobile Robot. IEEE Long Island Systems, Applications and Technology LISAT2015. 1 May, 2015. Long Island, New York.
[2]       Amir, H. & Habib, I. (2010). Evolutionary Approach for Mobile Robot Path Planning in Complex Environment. IJCSI International Journal of Computer Science Issues, 7(4), 1694-0784
[3]       Anthony, S. (1994). Optimal and Efficient Path Planning for Partially-Known Environments. IEEE International Conference on Robotics and Automation (ICRA '94). 8-13May, 1994. San Diego, California.
[4]       Borenstein, J. & Koren Y. (1991). The Vector Field Histogram – Fast Obstacle Avoidance for Mobile Robots. IEEE Transactions on Robotics and Automation, 7(3), 278-288.
[5]       Carsten, J., Rankin, A., Ferguson, D., & Stentz, A. (2007). Global Path Planning on Board the Mars Exploration Rovers. Journal of Field Robotics, 26(4), 337-357
[6]       Ferguson, D. & Stents, A. (2006). Field D*: An Interpolation-based Path Planner and Replanner. Proceedings of the International Symposium on Robotics Research (ISRR). 12-15 October 2005. San Francisco.
[7]       Geraerts, R. & Overmars, M. (2007). The corridor map method : A general framework for real-time high-quality path planning. Computer Animation and Virtual Worlds, 18, 107-119
[8]       Geraerts, R. & Overmars, M. (2008). Flexible Path Planning Using Corridor Maps. Algorithms - ESA 2008: 16th Annual European Symposium. 15-17 September 2008. Karlsruhe, Germany.
[9]       Gireesh, K. T., Poornaselvan, K. J. & Sethumadhavan, M. (2010). Fuzzy Support Vector Machine-based Multi-agent Optimal Path Planning Approach to Robotics Environment. Defence Science Journal, 60(4), 387-391
[10]   Hart, P., Nilsson, N. J. & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst. Sci. Cybernetics, 4, 100-107.
[11]   Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press Cambridge, MA, USA.
[12]   Kalarani, G. (2014). A Survey of the Various Path Planning Techniques Used in the Navigation of Autonomous Mobile Robot. Indian Journal of Applied Research, 4(10), 442-444.
[13]   Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90-98
[14]   Leena, N. & Saju, K. K. (2014). A Survey on Path Planning Techniques for Autonomous Mobilerobots. IOSR Journal of Mechanical and Civil Engineering. 76-79
[15]   Nash, A., Daniel, K. & Koenig, S. (2010). Theta* : Any-Angle Path Planning on Grids. Journal of Artificial Intelligence Research, 39, 533-579
[16]   Ouanezar, A., He X. & Gang Zhao. (2008). Survey and the Relative Issues on the Path Planning of Mobile Robot In Rough Terrain (www.paper.edu.cn). Accessed on March 2015.
[17]   Payeur, P., Le-Huy, H. & Gosselin, C. (1994). Robot Path Planning using Neural Networks and Fuzzy Logic. Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics, 2, 800-805.
[18]   Pratihar, K., Deb, K. & Ghosh, A. (1999). A Genetic-Fuzzy Approach for Mobile Robot Navigation Among Moving Obstacles. International Journal of Approximate Reasoning, 20, 145-172.
[19]   Sezer, V. & Gokasan, M. (2012). A Novel Obstacle Avoidance Algorithm: Follow the Gap Method. Robotics and Autonomous Systems, 60(9), 1123-1134.
[20]   Shyba, Z. & Tauseef, G. (2015). A Path Planning Technique for Autonomous Mobile Robot using Free-Configuration Eigenspaces. International Journal of Robotics and Automation, 6(1), 14 - 28
[21]   Stentz, A. (1994). Optimal and Efficient Path Planning for Partially-Known Environments, Intelligent Unmanned Ground Vehicles. Springer US
[22]   Zacksenhouse, M., DeFigueiredo, R. J. P. & Johnson, D. H. (1988). A Neural Network Architecture for cue-based motion planning. Proceedings IEEE International Conference on Decision and Control. 7-9 December, Austin.
[23]   Zelinsky, A. (1994). Using Path Transforms to guide the search for find path in 2D. International Journal Rob. Res., 13(4), 315-325.
[24]   Zeyad, A. A., Shahrizal, S. & Hoshang, K. (2015). A Comprehensive Study on Pathfinding Techniques for Robotics and Video Games. International Journal of Computer Games Technology, 2015, Article ID 736138
[25]   Zhu, Y., Zhang, T., Song, J. & Li, X. (2012). A New Hybrid Navigation Algorithm for Mobile Robots in Environments with Incomplete Knowledge. Knowledge- Based Systems, 27, 302-313.
[26]   Zhu, Y., Zhang, T., Song, J. & Li, X. (2012). A New Bug-type Navigation Algorithm Considering Practical Implementation Issues for Mobile Robots. IEEE International Conference on Robotics and Biomimetics. 531-536.
[27]   Zohaib, M., Pasha, M., Nadeem, J. & Jamshed, I. (2012). Intelligent Bug Algorithm (IBA): A Novel Strategy to Navigate Mobile Robots Autonomously. Third International Multi-topic Conference. 18-20 December, Jamshoro, Pakistan.
[28]   Zohaib, M., Pasha, M., Riaz, A., Javaid, N., Ilahi, M. & Khan, R. D. (2013). Control Strategies for Mobile Robot with Obstacle Avoidance. Journal of Basic and Applied Scientific Research, 3(4), 1027-1036.
[29]   Zohaib, M., Pasha, M., Javaid, N., Salaam, A. & Iqbal, J. (2014). An Improved Algorithm for Collision Avoidance in Environments Having U and H Shaped Obstacles. Studies in Informatics and Control Journal, 23(1), 97-106.