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, Workshop on Algoritmic Foundations of Robotics, Dartmouth, Hanover, NH, USA , pp. 233-245. Han, L., Rudolph, L., Blumenthal, J. and Valodzin, I. (2006). Stratified deformation space and path planning for a planar closed chain with revolute joints, in S. Akella, N. Amato, W. Huang and B. Mishra (Eds.), International Workshop on Algorithmic Foundations of Robotics WAFR , Springer Tracts in Advanced Robotics, Vol. 47, Springer, New York, NY, pp. 235-250. Kallmann, M., Aubel, A., Abaci, T. and Thalmann, D. (2003). Planning collision-free reaching motions for interactive

uncertainty, Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan , pp. 1327-1332. Collins, P. and Goldsztejn, A. (2008). The reach-and-evolve algorithm for reachability analysis of nonlinear dynamical systems, Electronic Notes in Theoretical Computer Science   223 : 87-102. Fraichard, T. and Mermond, R. (1998). Path planning with uncertainty for car-like robots, Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, Belgium , pp. 27-32. Francis, B. A. and Khargonekar, P. P. (Eds.) (1995). Robust

References Akbaripour, H. and Masehian, E. (2017). Semi-lazy probabilistic roadmap: A parameter-tuned, resilient and robust path planning method for manipulator robots, International Journal of Advanced Manufacturing Technology 89(5-8): 1401-1430. Asano, T., Asano, T., Guibas, L., Hershberger, J. and Imai, H. (1986). Visibility of disjoint polygons, Algorithmica 1(1): 49-63. Bohlin, R. and Kavraki, L.E. (2000). Path planning using lazy PRM, Proceedings of the IEEE International Conference on Robotics and Automation, ICRA’00, San Francisco, CA, USA, Vol. 1, pp

Conference, Cambridge, UK. Klaučo, M., Blažek, S., Kvasnica, M. and Fikar, M. (2014). Mixed-integer SOCP formulation of the path planning problem for heterogeneous multi-vehicle systems, European Control Conference 2014, Strasbourg, France, pp. 1474-1479. Kvasnica, M. (2008). Efficient Software Tools for Control and Analysis of Hybrid Systems, Ph.D. thesis, ETH Zurich, Zurich. Löfberg, J. (2004). YALMIP, http://users.isy.liu.se/johanl/yalmip/. Mathew, N., Smith, S. and Waslander, S. (2014). Optimal path planning in cooperative heterogeneous multi-robot delivery systems, 11

Robot. - Control Theory & Applications, Vol. 27, January 2010, No 1, pp. 111-115. 4. Chaomin, L., S. X. Yang.A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments. - IEEE Transactions on Neural Networks, Vol. 19, 2008, No 7, pp. 1279-1298. 5. Caihong, L., Z. Jingyuan, L. Yibin. Application of Artificial Neural Network Based on Q-Learning for Mobile Robot Path Planning. - In: Proc. of 2006 IEEE International Conference on Information Acquisition, 20-23 August 2006, Piscataway, NJ, USA, IEEE

References 1. Rimon, E., D. E. Koditschek. Exact Robot Navigation Using Artificial Potential Functions. - IEEE Trans. on Robotics and Automation, Vol. 8, 1992, pp. 529-551. 2. Vadakkepat, P., K. C. Tan, W. Ming-Liang. Evolutionary Artificial Potential Fields and Their Application in Real Time Robot Path Planning. - In: Proc. of 2000 Congress on Evolutionary Computation, 16-19 July 2000. 3. Qixin, C., H. Yanwen, Z. Jingliang. An Evolutionary Artificial Potential Field Algorithm for Dynamic Path Planning of Mobile Robot. - In: Proc. of International Conference on

References 1. Araujo de Lemos, R., Garcia, O., Ferreira, J.V. (2015) Local and Global Path Generation for Autonomous Vehicles Using Splines. In: Workshop on Engineering Applications - International Congress on Engineering (WEA) , Bogota, October 2015. Colombia: IEEE, pp. 1–6. 2. Ayawli, B.B.K., Chellali, R., Appiah, A.Y., Kyeremeh, F. (2018) An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning. Journal of Advanced Transportation , 1–27. DOI: 10.1155/2018/8269698 3. Chaari, I., Koubaa, A., Benaceur, H., Ammar, A

Conference on Intelligent Robots and Systems. IEEE Computer Society, 1995. 9. Svec P, Gupta S K.: Automated synthesis of action selection policies for unmanned vehicles operating in adverse environments. Autonomous Robots 149-164, 2012. 10. Goldberg D E.: Genetic Algorithms in Search, Optimization and Machine Learning. xiii(7): 2104–2116,1989. 11. Petres C, Yan P, Patron P, et al.: Path Planning for Autonomous Underwater Vehicles. IEEE Transactions on Robotics, 23(2):331-341, 2007. 12. Lavalle S M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning. Algorithmic

Cybernetics, Vol. SSC-4, 1968, No 2, pp. 100-107. 5. Li, L., Y. Tao, C. Langton. Current Situation and Future of Researching Move Robot Technology. - Robot, Vol. 24, 2002, No 5, pp. 475-480. 6. Zhou, W. The Path Planning of Rescuing and Probing Robot in the Coal Mine and Trajectory Tracking Control Studying. Shanxi: Taiyuan University of Technology, 2011. 7. Xia, L. AFSA and Its Application. Guangxi, Guangxi University for Nationalities, 2009. 8. Yu, H., J. Wei, J. Li. Transformer Fault Diagnosis Based on Improved Artificial Fish Swarm Optimization Algorithm and BP Network

References [1] A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning, 28. [2] M Shahab Alam, M Usman Rafique, and M Umer Khan. Mobile robot path planning in static environments using particle swarm optimization. International Journal of Computer Science and Electronics Engineering (IJCSEE), 3(3):253–257, 2015. [3] Dora-Luz Almanza-Ojeda, Yazmín Gomar-Vera, and Mario Ibarra-Manzano. Occupancy Map Construction for Indoor Robot Navigation. 10 2016. [4] Ismail Altaharwa, Alaa Sheta, and Mohammed