ENHANCED OPTIMAL PATH PLANNING FOR INDOOR MOBILE ROBOT NAVIGATION
Keywords:
Engineering, Mobile Robot Navigation, Optimize path, Computer based gamesAbstract
A more conventional uniform square-described grid-based route planning algorithm is used in many practical robotic applications as well as in a large number of computer-based games to tackle the challenge of energy-efficient and accurate navigation of moving agents. This algorithm can be found in both the real world and the virtual world. In order to satisfy the criteria of the challenge, this is done as part of the effort. The process of determining, inside the workspace of an application, the route that will be the quickest, safest, and most convenient route between any two sites of interest is referred to as "path finding." Path finding is also known as "route finding." The Aand the Theta route planning algorithms, in addition to their many different iterations, are the approaches that are used the majority of the time because of the simplicity of their designs, the efficiency with which they use computing time, the fact that they maximize path length, and the fact that they meticulously plan out paths. This is due to the fact that they maximize path length and that they plan out paths in great detail. Even though there are basic algorithms for identifying routes, the goal of such algorithms is to locate the way or paths that provide the most benefit, even if doing so necessitates compromising the criterion that there be no collisions and making the route more convoluted. These algorithms for route planning either contact the blocked grid cell or are edge-constrained, indicating that they were produced using grid cells. If neither of these conditions is met, then the methods were not constructed using grid cells. Alternately, they fulfill both of these criteria simultaneously. In addition to this, they do not investigate the best possible paths that are free of head-on accidents. In order to improve the level of route safety while simultaneously lowering the degree to which partial path optimality may be obtained, several various ways of path smoothing and path shaping are used. When route finding and post-path smoothing are coupled in a real-world application environment, the end effect is a final path that is more complicated. This, in turn, causes a loss of the property that belongs to true shortest pathways. The conventional algorithms suffer from two significant flaws, both of which must be addressed if they are to continue to be useful. The grid-based route planning approach has a number of drawbacks, the first of which is that it is only able to plan a path by using the edges or vertices of a certain workspace map. This is only one of the many problems with the grid-based route planning technique.
References
Chakravarthy and D. Ghose (1998), “Obstacle avoidance in a dynamic environment: A collision cone approach,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 28, no. 5, pp. 562–574.
Algfoor Z. A., Sunar M. S., and Kolivand (2015), “A comprehensive study on path finding techniques for robotics and video games”, International Journal of Computer Games Technology, Volume 2015, Article ID 736138, 11 pages, DOI: 10.1155/2015/7361382015.
Aleksandr I. Panov, Konstantin S. Yakovlev, and Roman Suvorov (2017), "Grid Path Planning with Deep Reinforcement Learning: Preliminary Results", 8th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2017, Procedia Computer Science 123 (2018), pp.347–353.
Anand Veerswamy, Dr. Bala Amavasai (2006),"Optimal Path Planning Using Improved A Algorithm for Homeland Security Application", AIA'06 IASTED International Conference on Artificial Intelligence and Application, Pp.50-55
Anshika Pal, Ritu Tiwari, A. Shukla (2012), "Modified A* Algorithm for Mobile Robot Path Planning", In book: Soft Computing Techniques in Vision Science, DOI: 10.1007/978-3- 642-25507-6_16,
Aurenhammer, F. (1991), “Voronoi diagrams—a survey of a fundamental geometric data structure”.
Bj ¨ornsson, Y., Enzenberger, M., Holte, R., Schaeffer, J., & Yap P. (2003), “Comparison of differentgrid abstractions for path finding on maps”. In Proceedings of the International JointConference on Artificial Intelligence.
Bjornsson Y., and Halldorsson K. (2006), “Improved heuristics for optimal pathfinding on game maps”, In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), pp. 9–14.
Bohlin, R., & Kavraki, L. (2000), “Path planning using lazy PRM”, In Proceedings of the IEEE Transactions on Robotics and Automation.
Bohlin R. and Kavraki L. E. (2000), “Path planning using lazy PRM”, In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 521–528.
Botea, A., M¨uller, M., & Schaeffer, J. (2004) “Near optimal hierarchical pathfinding” Journal ofGame Development, 1, 1–22.
Bulitko V., Björnsson Y., and Lawrence R. 2010, “Case-based subgoaling in realtie heuristic search for video game path finding”, Journal of Artificial Intelligence Research (JAIR), vol. 39, pp. 269- 300.
Bulitko, V., Sturtevant, N., & Kazakevich, M. (2005), “Speeding up learning in real-time search via automatic state abstraction” In Proceedings of the AAAI Conference on Artificial Intelligence.
Bulitko, V., Luˇstrek, M., Schaeffer, J., Bj ¨ornsson, Y., & Sigmundarson, S. (2008), “Dynamiccontrol in real-time heuristic search”, Journal of Artificial Intelligence Research, 32, 419–452.
C. Goerzen, Z. Kong, B. Mettler (2010), "A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance", Journal of Intelligent Robot System, pp. 65-100,
Kaushik P, Yadav R. Deployment of Location Management Protocol and Fault Tolerant Technique for Mobile Agents. Journal of Advances and Scholarly Research in Allied Education [JASRAE]. 2018;15(6):590-595. DOI: 10.29070/JASRAE.
Kaushik P, Yadav R. Mobile Image Vision and Image Processing Reliability Design for Fault-Free Tolerance in Traffic Jam. Journal of Advances and Scholarly Research in Allied Education (JASRAE). 2018;15(6):606-611. DOI: 10.29070/JASRAE.
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