Abstract
Finding a safe and optimum path from the source node to the target node, while preventing collisions with environmental obstacles, is always a challenging task. This task becomes even more complicated when the application area includes Unmanned Aerial Vehicle (UAV). This is because UAV follows an aerial path to reach the target node from the source node and the aerial paths are defined in 3D space. A* (A-star) algorithm is the path planning strategy of choice to solve path planning problem in such scenarios because of its simplicity in implementation and promise of optimality. However, A* algorithm guarantees to find the shortest path on graphs but does not guarantee to find the shortest path in a real continuous environment. Theta* (Theta-star) and Lazy Theta* (Lazy Theta-star) algorithms are variants of the A* algorithm that can overcome this shortcoming of the A* algorithm at the cost of an increase in computational time. In this research work, a comparative analysis of A-star, Theta-star, and Lazy Theta-star path planning strategies is presented in a 3D environment. The ability of these algorithms is tested in 2D and 3D scenarios with distinct dimensions and obstacle complexity. To present comparative performance analysis of considered algorithms two performance metrices are used namely computational time which is a measure of time taken to generate the path and path length which represents the length of the generated path.
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References
Aine S, Likhachev M (2013) Anytime truncated D*: anytime replanning with truncation. in Proceedings of the Sixth International Symposium on Combinatorial Search. 2–10
Albaghdadi AF, Ali AA (2019) 3D Path planning of fixed and mobile environments using potential field algorithm with Genetic algorithm. 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON) IEEE 115–119.
Al-Mutib K, AlSulaiman M, Emaduddin M, Ramdane H and Mattar E (2011) D* Lite Based Real-Time Multi-Agent Path Planning in Dynamic Environments, 3rd International Conference on Computational Intelligence, Modelling & Simulation, pp. 170–174.
Carsten J, Ferguson D, Stentz A (2006) 3D field D*: Improved path planning and replanning in three dimensions. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, 3381–3386
De FL, Guglieri G, Quagliotti F (2012) Path planning strategies for UAVS in 3D environments. J Intell Rob Syst 65(1):247–264
Filipic B, Minisci E, Vasile M (2020) Bioinspired optimization methods and their applications. Springer, Berlin
Goel U, Varshney S, Jain A, Maheshwari S, Shukla A (2018) Three-dimensional path planning for uavs in dynamic environment using glow-worm swarm optimization. Procedia Comput Sci 133:230–239
Gonzalez D, Perez J, Milanes V, Nashashibi F (2016) A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst 17(4):1135–1145
Han J (2019) An efficient approach to 3D path planning. Inf Sci 478:318–330
Jeauneau V, Jouanneau L (2018) Path planner methods for UAVs in real environment. IFAC-Papers OnLine 51(22):292–297
Koenig S, Likhachev M, Furcy D (2004) Lifelong planning A*. Artif Intell 155:93–146
Koubaa A, Bennaceur H, Chaari I, Trigui S, Ammar A, Sriti MF, Alajlan M, Cheikhrouhou O, Javed Y (2018) Robot path planning and cooperation foundations. Algorithms and Experimentations, Springer, Berlin
LaValle S (2006) Planning algorithms, 1st edn. Cambridge University Press, Cambridge
Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S (2008) Anytime search in dynamic graphs. Artif Intell 172(14):1613–1643
Nash A, Koenig S (2013) Any-angle path planning. Artif Intell Mag 34(4):85–107
Nash A, Daniel K, Koenig S, Felner A (2007) Theta*: Any-angle path planning on grids. Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, Menlo Park, California
Nash A, Koenig S, Tovey CA (2010) Lazy. Theta*: Any-angle path planning and path length analysis in 3D. National Conference on Artificial Intelligence
Omar R, Gu D (2010) 3D path planning for unmanned aerial vehicles using visibility line-based method, In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics. 80–85
Pandey P, Shukla A, Tiwari R (2018) Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm. Int J Syst Assur Eng Manag 9:836–852
Peter H, Nilsson N, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4(2):100–107
Pharpatara P, Herisse B, Bestaoui Y (2017) 3-D trajectory planning of aerial vehicles using RRT*. IEEE Trans Control Syst Technol 25(3):1116–1123
Quan L, Han L, Zhou B, Shen S, Gao F (2020) Survey of UAV motion planning. IET Cyber-Syst Robot 2(1):14–21
Rabin S (ed) (2019) Game AI Pro 360: guide to movement and pathfinding. CRC Press, Boca Raton
Saranya C, Unnikrishnan M, Ali SA, Sheela DS, Lalithambika VR (2016) Terrain based D∗ algorithm for path planning. IFAC-PapersOnLine 49(1):178–182
Sartori D, Zou D, Yu W (2019) An efficient approach to near-optimal 3D trajectory design in cluttered environments for multirotor UAVs. in IEEE 15th International Conference on Automation Science and Engineering 1077–1022
Silva MF, Virk GS, Tokhi MO, Malheiro B, Ferreira P, Guedes P (2017) Human-centric robotics. World Scientific Press, Singapore
Tan J, Zhao L, Wang Y, Zhang Y, Li L (2016) The 3D Path Planning Based on A* Algorithm and Artificial Potential Field for the Rotary-Wing Flying Robot. 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) 551–556
Yang XS (2020) Nature-inspired computation and swarm intelligence. Academic Press, Cambridge
Yang L, Qi J, Xiao J, and Yong X (2014) A literature review of UAV 3D path planning. in IEEE 11th World Congress on Intelligent Control and Automation, pp. 2376–2381
Yang L, Qi J, Song D, Xiao J, Han J, Xia Y (2016) Survey of robot 3D path planning algorithms. J Control Sci Eng 2016:1–22
Yan F, Liu YS, Xiao JZ (2013) Path planning in complex 3D environments using a probabilistic roadmap method. Int J Autom Comput 10:525–533
Zammit C, Kampen EJV (2018) Comparison between A* and RRT Algorithms for UAV Path Planning. AIAA Guidance, Navigation, and Control Conference, 1–23
Zammit C, Jan E, Kampen V (2020) Comparison of A* and RRT in real–time 3D path planning of UAVs. AIAA Scitech 2020 Forum
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Mandloi, D., Arya, R. & Verma, A.K. Unmanned aerial vehicle path planning based on A* algorithm and its variants in 3d environment. Int J Syst Assur Eng Manag 12, 990–1000 (2021). https://doi.org/10.1007/s13198-021-01186-9
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DOI: https://doi.org/10.1007/s13198-021-01186-9