Abstract
A form of navigation problem called path planning can be resolved using a variety of techniques. This paper presents an overview of path planning techniques, specifically focusing on finding the shortest and most efficient path in a static environment. Self-driving autonomous vehicles can identify the safest, most practical and economically advantageous routes from source to destination using appropriate path planning and decision-making in real-world urban contexts. The proposed work first utilizes an open-source CARLA Simulator to implement path planning using the A star algorithm in its inbuilt town map. It makes use of CARLA library modules such as Waypoint API, CARLA Townmap, and PID controllers for its functionality. Secondly, the local real-world map is exported from the osm.org website and consists of local geographic data required to demonstrate the path planning of autonomous vehicle in a real-world environment. The results are demonstrated using the simulator. With several path planning algorithms present, this work utilizes A* algorithm and gives out the shortest path between start and end locations. The major advantage of using the CARLA simulator is that we can use the inbuilt Python API to convert a given exported .osm file to a .xodr file, which can be integrated into the simulator, thus allowing our algorithms to be tested in real-world scenarios.
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References
Janét J, Luo R, Kay M. The essential visibility graph: an approach to global motion planning for autonomous mobile robots. https://doi.org/10.1109/ROBOT.1995.526023
Sharma SK, Pal BL (2015) Shortest path searching for road network using A* algorithm. IJCSMC 4(7)
Connell D, La HM. Extended rapidly exploring random tree-based dynamic path planning and replanning for mobile robots. https://doi.org/10.1177/1729881418773874
Iyer NC, Shet RM, Nissimagoudar PC, Gireesha HM, Mane V, Kulkarni A, Bijapur A, Akshata A, Neha P. Virtual simulation and testing platform for self-driving cars. ICT Analysis and Applications
Lakhekar GV, Waghmare LM (2015) Dynamic fuzzy sliding mode control of underwater vehicles. In: Azar A, Zhu Q (eds) Advances and applications in sliding mode control systems. Studies in computational intelligence, vol 576. Springer, Cham; Indian J Geo Mar Sci 48(07) (2019)
Khan F, Lakshmana Kumar R, Kadry S, Nam Y, Meqdad MN (2021) Autonomous vehicles: a study of implementation and security. Int J Electr Comput Eng (IJECE) 11(4)
Iyer NC, Pillai P, Bhagyashree K, Mane V, Shet RM, Nissimagoudar PC, Krishna G, Nakul VR. Millimeter-wave AWR1642 RADAR for obstacle detection: autonomous vehicles. https://doi.org/10.1007/978-981-15-3172-9_10
Truong NH, Mai HT, Tran TA, Tran MQ, Nguyen DD, Pham NVP. PaaS: planning as a service for reactive driving in CARLA leaderboard. https://doi.org/10.48550/arXiv.2304.08252
Ishida T. Real-time bidirectional search: coordinated problem solving in uncertain situations. https://doi.org/10.1109/34.506412
Lei X, Zhang Z, Dong P (2018) Key laboratory of intelligent ammunition technology dynamic path planning of unknown environment based on deep reinforced learning, vol 2018. Article ID 5781591. https://doi.org/10.1155/2018/5781591
Zammit C, van Kampen E-J. Comparison between A* and RRT algorithms for UAV path planning. https://doi.org/10.2514/6.2018-1846
Thorpe C. Path relaxation: path planning for a mobile robot. https://doi.org/10.1109/OCEANS.1984.1152243
Palma-Villalon E, Dauchez P. World representation and path planning for a mobile robot. https://doi.org/10.1017/S026357470000357X
Mir I, Gul F, Mir S, Khan MA, Saeed N, Abualigah L, Abuhaija B, Gandomi AH. A survey of trajectory planning techniques for autonomous systems. https://doi.org/10.3390/electronics11182801
Lakhekar GV, Roy RG (2014) Heading control of an underwater vehicle using dynamic fuzzy sliding mode controller. In: 2014 international conference on circuits, power and computing technologies [ICCPCT-2014], Nagercoil, India, pp 1448–1454. https://doi.org/10.1109/ICCPCT.2014.7054969
Iyer NC, Gireesha HM, Shet RM, Nissimgoudar P, Mane V (2020) Autonomous driving platform: an initiative under institutional research project, vol 172. https://doi.org/10.1016/j.procs.2020.05.126
Shamgah L, Tadewos TG, Karimoddini A, Homaifar A. Path planning and control of autonomous vehicles in dynamic reach-avoid scenarios. https://doi.org/10.1109/CCTA.2018.8511519
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We acknowledge our University for providing us to utilize Center for Intelligent Mobility Lab for carrying out our research. We also acknowledge the reviewers for their comments that helped improve the work.
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Shet, R.M., Iyer, N.C., Mirje, M., Bikkannavar, K.V., Rokhade, S. (2024). Path Planning of Autonomous Vehicle for Real World Scenario Using CARLA. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_6
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