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A survey on the application of path-planning algorithms for multi-rotor UAVs in precision agriculture

Published online by Cambridge University Press:  13 January 2022

Amin Basiri ,
Valerio Mariani ,
Giuseppe Silano Open the ORCID record for Giuseppe Silano [Opens in a new window] ,
Muhammad Aatif ,
Luigi Iannelli  and
Luigi Glielmo
Amin Basiri*
Affiliation:
Department of Engineering, University of Sannio in Benevento, Piazza Roma 21, 82100 Benevento, Italy.
Valerio Mariani
Affiliation:
Department of Engineering, University of Sannio in Benevento, Piazza Roma 21, 82100 Benevento, Italy.
Giuseppe Silano
Affiliation:
Faculty of Electrical Engineering, Czech Technical University in Prague, 16636 Prague 6, Czech Republic
Muhammad Aatif
Affiliation:
Department of Engineering, University of Sannio in Benevento, Piazza Roma 21, 82100 Benevento, Italy.
Luigi Iannelli
Affiliation:
Department of Engineering, University of Sannio in Benevento, Piazza Roma 21, 82100 Benevento, Italy.
Luigi Glielmo
Affiliation:
Department of Engineering, University of Sannio in Benevento, Piazza Roma 21, 82100 Benevento, Italy.
*
*Corresponding author. E-mail: basiri@unisannio.it
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Abstract

Multi-rotor Unmanned Aerial Vehicles (UAVs), although originally designed and developed for defence and military purposes, in the last ten years have gained momentum, especially for civilian applications, such as search and rescue, surveying and mapping, and agricultural crops and monitoring. Thanks to their hovering and Vertical Take-Off and Landing (VTOL) capabilities and the capacity to carry out tasks with complete autonomy, they are now a standard platform for both research and industrial uses. However, while the flight control architecture is well established in the literature, there are still many challenges in designing autonomous guidance and navigation systems to make the UAV able to work in constrained and cluttered environments or also indoors. Therefore, the main motivation of this work is to provide a comprehensive and exhaustive literature review on the numerous methods and approaches to address path-planning problems for multi-rotor UAVs. In particular, the inclusion of a review of the related research in the context of Precision Agriculture (PA) provides a unified and accessible presentation for researchers who are initiating their endeavours in this subject.

Keywords

unmanned areal vehiclepath planningprecision agriculturemulti-rotor
Type
Review Article
Information
The Journal of Navigation , Volume 75 , Issue 2 , March 2022 , pp. 364 - 383
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

AbuSalim, S. W. G., Ibrahim, R., Saringat, M. Z., Jamel, S. and Wahab, J. A. (2020). Comparative Analysis between Dijkstra and Bellman-Ford Algorithms in Shortest Path Optimization. In: IOP Conference Series: Materials Science and Engineering, Vol. 917, International Conference on Technology, Engineering and Sciences (ICTES), 17–18 April 2020, Penang, Malaysia. Available at: https://iopscience.iop.org/article/10.1088/1757-899X/917/1/012077.Google Scholar
AgFunderNews. (2020). What is precision agriculture? Available at: https://agfundernews.com/what-is-precision-agriculture.html.Google Scholar
Aggarwal, S. and Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 149, 270–299.CrossRefGoogle Scholar
ANN. (2020). Chapter 2 - Geometric Processing and Positioning Techniques. In: Liang, S and Wang, J (eds.). Advanced Remote Sensing (2nd ed.) Academic Press, 59–105. ISBN 9780128158265. https://doi.org/10.1016/B978-0-12-815826-5.00002-7.Google Scholar
Babel, L. (2019). Coordinated target assignment and UAV path planning with timing constraints. Journal of Intelligent & Robotic Systems, 94, 857–869.CrossRefGoogle Scholar
Baek, J. and Choi, Y. (2017). A new algorithm to find raster-based least-cost paths using cut and fill operations. International Journal of Geographical Information Science, 31, 2234–2254.CrossRefGoogle Scholar
Baeza, D., Ihle, C. F. and Ortiz, J. M. (2017). A comparison between ACO and Dijkstra algorithms for optimal ore concentrate pipeline routing. Journal of Cleaner Production, 144, 149–160.CrossRefGoogle Scholar
Bakhtiari, A. A., Navid, H., Mehri, J. and Bochtis, D. D. (2012). Optimal route planning of agricultural field operations using ant colony optimization. Agricultural Engineering International: CIGR Journal, 13, 1–16.Google Scholar
Basiri, A. (2020). Open area path finding to improve wheelchair navigation. Preprint. https://arxiv.org/abs/2011.03850Google Scholar
Basiri, A., Lohan, E. S., Moore, T., Winstanley, A., Peltola, P., Hill, C., Amirian, P. and e Silva, P. F. (2017a). Indoor location based services challenges, requirements and usability of current solutions. Computer Science Review, 24, 1–12.CrossRefGoogle Scholar
Basiri, A., Oskoei, M. A., Basiri, A. and Shahri, A. M. (2017b). Improving Robot Navigation and Obstacle Avoidance using Kinect 2.0. In: 5th RSI International Conference on Robotics and Mechatronics, 486–489.CrossRefGoogle Scholar
Bassiri, A., Asghari Oskoei, M. and Basiri, A. (2018). Particle filter and finite impulse response filter fusion and hector SLAM to improve the performance of robot positioning. Journal of Robotics, 1–10.CrossRefGoogle Scholar
Bhardwaj, M., Choudhury, S. and Scherer, S. (2017). Learning Heuristic Search via Imitation. In: Conference on Robot Learning. PMLR, 271–280.Google Scholar
Bonilla Licea, D., Silano, G., Mounir, G. and Saska, M. (2021). Optimum Trajectory Planning for Multi-Rotor UAV Relays with Tilt and Antenna Orientation Variations. In: 29th European Signal Processing Conference, To Appear.Google Scholar
Bortoff, S. (2000). Path Planning for UAVs. In: Proceedings of the 2000 American Control Conference, vol. 1, 364–368.CrossRefGoogle Scholar
Brassai, S. T., Iantovics, B. and Enăchescu, C. (2012). Artificial Intelligence in the path planning optimization of mobile agent navigation. Procedia Economics and Finance, 3, 243–250.CrossRefGoogle Scholar
Budiyanto, A., Cahyadi, A., Adji, T. B. and Wahyunggoro, O. (2015). UAV Obstacle Avoidance using Potential Field under Dynamic Environment. In: International Conference on Control, Electronics, Renewable Energy and Communications, 187–192.CrossRefGoogle Scholar
Cabreira, T. M., Brisolara, L. B. and Ferreira, P. R. Jr (2019). Survey on coverage path planning with unmanned aerial vehicles. Drones, 3, 4.CrossRefGoogle Scholar
Cao, X., Zou, X., Jia, C., Chen, M. and Zeng, Z. (2019). RRT-based path planning for an intelligent litchi-picking manipulator. Computers and Electronics in Agriculture, 156, 105–118.CrossRefGoogle Scholar
Cekmez, U., Ozsiginan, M. and Sahingoz, O. K. (2017). Multi-UAV Path Planning with Multi Colony Ant Optimization. In: International Conference on Intelligent Systems Design and Applications. Springer, 407–417.Google Scholar
Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C. (2009) [1990]. Introduction to Algorithms (3rd ed.) MIT Press and McGraw-Hill, 1320 pp. ISBN: 0-262-03384-4.Google Scholar
Daponte, P., De Vito, L., Glielmo, L., Iannelli, L., Liuzza, D., Picariello, F. and Silano, G. (2019). A Review on the Use of Drones for Precision Agriculture, Vol. 275. IOP Publishing, 012022.Google Scholar
Deb, A. (2011). Introduction to Soft Computing Techniques: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms. In: Soft Computing in Textile Engineering. Elsevier, pages 3–24.Google Scholar
Demkiv, L., Ruffo, M., Silano, G., Bednar, J. and Saska, M. (2021). An Application of Stereo Thermal Vision for Preliminary Inspection of Electrical Power Lines by MAVs. In: Aerial Robotic Systems Physically Interacting with the Environment. To Appear.Google Scholar
Dewang, H. S., Mohanty, P. K. and Kundu, S. (2018). A robust path planning for mobile robot using smart pARTICLE swarm optimization. Procedia computer science, 133, 290–297.CrossRefGoogle Scholar
Dewangan, R. K., Shukla, A. and Godfrey, W. W. (2019). Three dimensional path planning using grey wolf optimizer for UAVs. Applied Intelligence, 49, 2201–2217.CrossRefGoogle Scholar
Dhulkefl, E. J. et al. (2019). Path planning algorithms for unmanned aerial vehicles. International Journal of Trend in Scientific Research and Development, 3, 359–362.CrossRefGoogle Scholar
Dib, O., Manier, M.-A., Moalic, L. and Caminada, A. (2017). Combining VNS with genetic algorithm to solve the one-to-one routing issue in road networks. Computers & Operations Research, 78, 420–430.CrossRefGoogle Scholar
do Ó, L., Alexandre Prates, P., Mendonça, R., Lourenço, A., Marques, F. and Barata, J. (2019). Autonomous 3-D Aerial Navigation System for Precision Agriculture. In: IEEE 28th International Symposium on Industrial Electronics, 1144–1149.CrossRefGoogle Scholar
Elbanhawi, M. and Simic, M. (2014). Sampling-based robot motion planning: A review. IEEE Access, 2, 56–77.CrossRefGoogle Scholar
Eski, I. and Kuxş, Z. A. (2019). Control of unmanned agricultural vehicles using neural network-based control system. Neural Computing and Applications, 31, 583–595.CrossRefGoogle Scholar
Ferentinos, K., Arvanitis, K., Kyriakopoulos, K. and Sigrimis, N. (2000). Heuristic motion planning for autonomous agricultural vehicles. IFAC Proceedings Volumes, 33, 325–330.CrossRefGoogle Scholar
Fu, B., Chen, L., Zhou, Y., Zheng, D., Wei, Z., Dai, J. and Pan, H. (2018a). An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robotics and Autonomous Systems, 106, 26–37.CrossRefGoogle Scholar
Fu, Z., Yu, J., Xie, G., Chen, Y. and Mao, Y. (2018b). A heuristic evolutionary algorithm of UAV path planning. Wireless Communications and Mobile Computing, 2018, 1–12, Article ID 2851964. doi:10.1155/2018/2851964Google Scholar
Gramajo, G. and Shankar, P. (2017). An efficient energy constraint based UAV path planning for search and coverage. International Journal of Aerospace Engineering, Article ID 8085623. doi:10.1155/2017/8085623CrossRefGoogle Scholar
Guruji, A. K., Agarwal, H. and Parsediya, D. (2016). Time-efficient A* algorithm for robot path planning. Procedia Technology, 23, 144–149.CrossRefGoogle Scholar
Hafez, A. T., Kamel, M. A., Jardin, P. T. and Givigi, S. N. (2017). Task assignment/trajectory planning for unmanned vehicles via HFLC and PSO. In: International Conference on Unmanned Aircraft Systems, 554–559.CrossRefGoogle Scholar
Hansen, P., Mladenović, N. and Pérez, J. A. M. (2010). Variable neighbourhood search: methods and applications. Annals of Operations Research, 175, 367–407.CrossRefGoogle Scholar
Hao, B. and Yan, Z. (2018). Recovery Path Planning for an Agricultural Mobile robot by Dubins-RRT* Algorithm. International Journal of Robotics and Automation, 33, 1–16.CrossRefGoogle Scholar
Haro, F. and Torres, M. (2006). A comparison of path planning algorithms for omni-directional robots in dynamic environments. In: IEEE 3rd Latin American robotics symposium, 18–25.CrossRefGoogle Scholar
Hayat, S., Yanmaz, E., Brown, T. X. and Bettstetter, C. (2017). Multi-objective UAV path planning for search and rescue. In: IEEE International Conference on Robotics and Automation, 5569–5574.CrossRefGoogle Scholar
Hernández, B. and Giraldo, E. (2018). A Review of Path Planning and Control for Autonomous Robots. In: IEEE 2nd Colombian Conference on Robotics and Automation, 1–6.CrossRefGoogle Scholar
Hidalgo-Paniagua, A., Vega-Rodríguez, M. A. and Ferruz, J. (2016). Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics. Expert Systems with Applications, 58, 20–35.CrossRefGoogle Scholar
Hoang, H. G. (2019). Single drone path planning in complex urban airspace. MAE Student Reports.Google Scholar
Hong, I. and Murray, A. T. (2016). Assessing raster GIS approximation for Euclidean shortest path routing. Transactions in GIS, 20, 570–584.CrossRefGoogle Scholar
Huang, S. and Teo, R. S. H. (2019). Computationally Efficient Visibility Graph-based Generation of 3D Shortest Collision-free Path Among Polyhedral Obstacles for Unmanned Aerial Vehicles. In: International Conference on Unmanned Aircraft Systems, 1218–1223.CrossRefGoogle Scholar
Huang, S., Teo, R. S. H. and Tan, K. K. (2019). Collision avoidance of multi unmanned aerial vehicles: A review. Annual Reviews in Control, 48, 147–164.CrossRefGoogle Scholar
Korkmaz, M. and Durdu, A. (2018). Comparison of Optimal Path Planning Algorithms. In: 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering, 255–258.CrossRefGoogle Scholar
Kuffner, J. J. and LaValle, S. M. (2000. RRT-connect: An Efficient Approach to Single-query Path Planning. In: IEEE International Conference on Robotics and Automation), Vol. 2. IEEE, 995–1001.Google Scholar
LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press, 826 pp. ISBN: 9780521862059. doi:10.1017/CBO9780511546877CrossRefGoogle Scholar
Li, X., Zhao, Y., Zhang, J. and Dong, Y. (2016). A Hybrid PSO Algorithm Based Flight Path Optimization for Multiple Agricultural UAVs. In: IEEE 28th International Conference on Tools with Artificial Intelligence, 691–697.CrossRefGoogle Scholar
Liu, Y., Zhang, X., Guan, X. and Delahaye, D. (2016). Potential Odor Intensity Grid based UAV Path Planning Algorithm with Particle Swarm Optimization Approach. Mathematical Problems in Engineering, 2016, 1–16.Google Scholar
Lozano-Pérez, T. and Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22, 560–570.CrossRefGoogle Scholar
Lugo-Cárdenas, I., Flores, G., Salazar, S. and Lozano, R. (2014). Dubins Path Generation for a Fixed Wing UAV. In: International Conference on Unmanned Aircraft Systems, 339–346.CrossRefGoogle Scholar
Majeed, A. and Lee, S. (2018). A fast global flight path planning algorithm based on space circumscription and sparse visibility graph for unmanned aerial vehicle. Electronics, 7, 375–392.CrossRefGoogle Scholar
Missura, M., Lee, D. D. and Bennewitz, M. (2018a). Minimal Construct: Efficient Shortest Path Finding for Mobile Robots in Polygonal Maps. In: IEEE International Conference on Intelligent Robots and Systems, 7918–7923.Google Scholar
Missura, M., Lee, D. D. and Bennewitz, M. (2018b). Minimal Construct: Efficient Shortest Path Finding for Mobile Robots in Polygonal Maps. In: IEEE International Conference on Intelligent Robots and Systems, 7918–7923.Google Scholar
Mueller, M. W., Hehn, M. and D'Andrea, R. (2015). A computationally efficient motion primitive for quadrocopter trajectory generation. IEEE Transactions on Robotics, 31(6), 1294–1310.CrossRefGoogle Scholar
Nash, A., Koenig, S. and Tovey, C. (2010). Lazy Theta*: Any-angle Path Planning and Path Length Analysis in 3D. In: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 24.Google Scholar
Noguchi, N., Reid, J., Benson, E. and Stombaugh, T. (1998). Vision intelligence for an agricultural mobile robot using a neural network. IFAC Proceedings Volumes, 31, 139–144.CrossRefGoogle Scholar
Nolan, P., Paley, D. A. and Kroeger, K. (2017). Multi-UAS Path Planning for Non-uniform Data Collection in Precision Agriculture. In: IEEE Aerospace Conference, 1–12.CrossRefGoogle Scholar
Noreen, I., Khan, A. and Habib, Z. (2016a). Optimal Path Planning for Mobile Robots Using Memory Efficient A*. In: International Conference on Frontiers of Information Technology, 142–146.CrossRefGoogle Scholar
Noreen, I., Khan, A., Asghar, K. and Habib, Z. (2019a). A path-planning performance comparison of RRT*-AB with MEA* in a 2-dimensional environment. Symmetry, 11, 945.CrossRefGoogle Scholar
Noreen, I., Khan, A., Asghar, K. and Habib, Z. (2019b). A path-planning performance comparison of RRT*-AB with MEA* in a 2-dimensional environment. Symmetry, 11, 945–960.CrossRefGoogle Scholar
Noreen, I. et al. (2016b). Optimal path planning using RRT* based approaches: a survey and future directions. International Journal of Advanced Computer Science and Applications, 7, 97–107.CrossRefGoogle Scholar
Ou, X., Liu, Y. and Zhao, Y. (2017). PSO based UAV Online Path Planning Algorithms. In: International Conference on Automation, Control and Robots, 41–45.CrossRefGoogle Scholar
Penin, B., Giordano, P. R. and Chaumette, F. (2019). Minimum-time trajectory planning under intermittent measurements. IEEE Robotics and Automation Letters, 4(1), 153–160. doi:10.1109/LRA.2018.2883375CrossRefGoogle Scholar
Pham, T. H., Bestaoui, Y. and Mammar, S. (2017). Aerial Robot Coverage Path Planning Approach with Concave Obstacles in Precision Agriculture. In: Workshop on Research, Education and Development of Unmanned Aerial Systems, 43–48.CrossRefGoogle Scholar
Primatesta, S., Guglieri, G. and Rizzo, A. (2019). A risk-aware path planning strategy for UAVs in urban environments. Journal of Intelligent & Robotic Systems, 95, 629–643.CrossRefGoogle Scholar
Qie, H., Shi, D., Shen, T., Xu, X., Li, Y. and Wang, L. (2019). Joint optimization of multi-UAV target assignment and path planning based on multi-agent reinforcement learning. IEEE Access, 7, 146264–146272.CrossRefGoogle Scholar
Quaglia, G., Visconte, C., Scimmi, L. S., Melchiorre, M., Cavallone, P. and Pastorelli, S. (2020). Design of a UGV powered by solar energy for precision agriculture. Robotics, 9, 13.CrossRefGoogle Scholar
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T. and Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148–107165.CrossRefGoogle Scholar
Raheem, F. A. et al. (2018). Path planning algorithm using D* heuristic method based on PSO in dynamic environment. American Scientific Research Journal for Engineering, Technology, and Sciences, 49, 257–271.Google Scholar
Reinecke, M. and Prinsloo, T. (2017). The Influence of Drone Monitoring on Crop Health and Harvest Size. In: 1st International Conference on Next Generation Computing Applications, 5–10.CrossRefGoogle Scholar
Roberge, V., Tarbouchi, M. and Labonté, G. (2012). Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on industrial informatics, 9, 132–141.CrossRefGoogle Scholar
Ropero, F., Muñoz, P. and R-Moreno, M. D. (2019). TERRA: A path planning algorithm for cooperative UGV–UAV exploration. Engineering Applications of Artificial Intelligence, 78, 260–272.CrossRefGoogle Scholar
Scholer, F., la Cour-Harbo, A. and Bisgaard, M. (2011). Configuration Space and Visibility Graph Generation from Geometric Workspaces for UAVs. In: AIAA Guidance, Navigation, and Control Conference, 6416–6427.CrossRefGoogle Scholar
Shao, S., Peng, Y., He, C. and Du, Y. (2020). Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Transactions, 97, 415–430.CrossRefGoogle ScholarPubMed
Silano, G. and Iannelli, L. (2016). An Educational Simulation Platform for GPS-denied Unmanned Aerial Vehicles Aimed to the Detection and Tracking of Moving Objects. In: 2016 IEEE Conference on Control Applications, 1018–1023.CrossRefGoogle Scholar
Silano, G. and Iannelli, L. (2020). CrazyS: A Software-in-the-Loop Simulation Platform for the Crazyflie 2.0 Nano-quadcopter. In: Robot Operating System (ROS): The Complete Reference (Volume 4). Springer International Publishing, 81–115.CrossRefGoogle Scholar
Silano, G. and Iannelli, L. (2021). MAT-fly: an educational platform for simulating unmanned aerial vehicles aimed to detect and track moving objects. IEEE Access, 9, 39333–39343. doi:10.1109/ACCESS.2021.3064758CrossRefGoogle Scholar
Silano, G., Aucone, E. and Iannelli, L. (2018). CrazyS: A Software-in-the-Loop Platform for the Crazyflie 2.0 Nano-Quadcopter. In: 2018 26th Mediterranean Conference on Control and Automation, 1–6.Google Scholar
Silano, G., Oppido, P. and Iannelli, L. (2019). Software-in-the-Loop Simulation for Improving Flight Control System Design: A Quadrotor Case Study. In: IEEE International Conference on Systems, Man and Cybernetics, 466–471.CrossRefGoogle Scholar
Silano, G., Baca, T., Penicka, R., Liuzza, D. and Saska, M. (2021a). Power line inspection tasks with multi-aerial robot systems via signal temporal logic specifications. IEEE Robotics and Automation Letters, 6(2), 4169–4176. doi:10.1109/LRA.2021.3068114CrossRefGoogle Scholar
Silano, G., Bednar, J., Nascimento, T., Capitan, J., Saska, M. and Ollero, A. (2021b). A Multi-Layer Software Architecture for Aerial Cognitive Multi-Robot Systems in Power Line Inspection Tasks. In: International Conference on Unmanned Aircraft Systems, 1624–1629.CrossRefGoogle Scholar
Sinha, K. (2017). Path planning for a UAV in an agricultural environment to tour and cover multiple neighborhoods. Ph.D. thesis, Virginia Tech. Available at: https://vtechworks.lib.vt.edu/handle/10919/79731.Google Scholar
Stafford, J. V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 76, 267–275.CrossRefGoogle Scholar
Sunny, M. S. H., Hossain, E., Mimma, T. N. and Hossain, S. (2017). An Autonomous Robot: Using ANN to Navigate in a Static Path. In: 4th International Conference on Advances in Electrical Engineering, 291–296.CrossRefGoogle Scholar
Sylvester, G., Rambaldi, G., Guerin, D., Wisniewski, A., Khan, N., Veale, J. and Xiao, M. (2018). E-agriculture in Action: Drones for Agriculture. Food and Agriculture Organization of the United Nations and International Telecommunication Union Bangkok. 1–126. Available at: https://www.fao.org/3/I8494EN/i8494en.pdf.Google Scholar
Tamar, A., Wu, Y., Thomas, G., Levine, S. and Abbeel, P. (2016). Value iteration networks. Preprint. Available at: https://arxiv.org/abs/1602.02867.Google Scholar
Tan, J., Zhao, L., Wang, Y., Zhang, Y. and Li, L. (2016). The 3D Path Planning based on A* Algorithm and Artificial Potential Field for the Rotary-wing Flying Robot. In: 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, Vol. 2, 551–556.CrossRefGoogle Scholar
Tang, W., Wang, L., Gu, J. and Gu, Y. (2020). Single neural adaptive PID control for small UAV micro-turbojet engine. Sensors, 20(2), 345–366.CrossRefGoogle ScholarPubMed
Terlizzi, M., Silano, G., Russo, L., Aatif, M., Basiri, A., Mariani, V., Iannelli, L. and Glielmo, L. (2021). A Vision-Based Algorithm for a Path Following Problem. In: International Conference on Unmanned Aircraft Systems, 1630–1635.CrossRefGoogle Scholar
Tokekar, P., Hook, J. V., Mulla, D. and Isler, V. (2016). Sensor planning for a symbiotic UAV and UGV system for precision agriculture. IEEE Transactions on Robotics, 32(6), 1498–1511. doi:10.1109/TRO.2016.2603528CrossRefGoogle Scholar
Vlastelica, M., Paulus, A., Musil, V., Martius, G. and Rolínek, M. (2019). Differentiation of blackbox combinatorial solvers. Preprint. Available at: https://arxiv.org/abs/1912.02175.Google Scholar
Wang, Y., Liang, X., Li, B. and Yu, X. (2017a). Research and Implementation of Global Path Planning for Unmanned Surface Vehicle based on Electronic Chart. In: International Conference on Mechatronics and Intelligent Robotics. Springer, 534–539.CrossRefGoogle Scholar
Wang, Y., Liang, X., Li, B. and Yu, X. (2017b). Research and Implementation of Global Path Planning for Unmanned Surface Vehicle based on Electronic Chart. In: International Conference on Mechatronics and Intelligent Robotics. Springer, 534–539.CrossRefGoogle Scholar
Wang, H.-J., Fu, Y., Zhao, Z.-Q. and Yue, Y.-J. (2019). An improved ant colony algorithm of robot path planning for obstacle avoidance. Journal of Robotics, 2019, 1–9.Google Scholar
Wang, H., Wang, J., Ding, G., Chen, J. and Yang, J. (2020). Completion time minimization for turning angle-constrained UAV-to-UAV communications. IEEE Transactions on Vehicular Technology, 69, 4569–4574.CrossRefGoogle Scholar
Wooden, D. T. (2006). Graph-based path planning for mobile robots. Ph.D. thesis. School of Electrical and Computer Engineering, Georgia Institute of Technology. Available at: https://smartech.gatech.edu/handle/1853/14055?show=full.Google Scholar
Xiao, Z., Wan, H., Zhuo, H. H., Lin, J. and Liu, Y. (2019). Representation learning for classical planning from partially observed traces. Preprint. Available at: https://arxiv.org/abs/1907.08352.Google Scholar
Yang, G. and Kapila, V. (2002). Optimal Path Planning for Unmanned Air Vehicles with Kinematic and Tactical Constraints. In: Proceedings of the 41st IEEE Conference on Decision and Control, Vol. 2, 1301–1306.Google Scholar
Yang, L., Qi, J., Xiao, J. and Yong, X. (2014). A Literature Review of UAV 3D Path Planning. In: Proceeding of the 11th World Congress on Intelligent Control and Automation, 2376–2381.Google Scholar
Yang, J., Wang, X., Li, Z., Yang, P., Luo, X., Zhang, K., Zhang, S. and Chen, L. (2016a). Path Planning of Unmanned Aerial Vehicles for Farmland Information Monitoring based on WSN. In: 12th World Congress on Intelligent Control and Automation, 2834–2838.CrossRefGoogle Scholar
Yang, L., Qi, J., Song, D., Xiao, J., Han, J. and Xia, Y. (2016b). Survey of robot 3D path planning algorithms. Journal of Control Science and Engineering, 2016, 1–23.Google Scholar
Yang, J., Xi, J., Wang, C. and Xie, X. (2018). Multi-base multi-UAV Cooperative Patrol Route Planning Novel Method. In: 33rd Youth Academic Annual Conference of Chinese Association of Automation, 688–693.CrossRefGoogle Scholar
Yang, L., Zhang, X., Zhang, Y. and Xiangmin, G. (2019). Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach. Chinese Journal of Aeronautics, 32, 1504–1519.Google Scholar
Yingkun, Z. (2018). Flight Path Planning of Agriculture UAV based on Improved Artificial Potential Field Method. In: Chinese Control And Decision Conference, 1526–1530.CrossRefGoogle Scholar
Zammit, C. and Van Kampen, E.-J. (2018). Comparison between A* and RRT Algorithms for UAV Path Planning. In: AIAA Guidance, Navigation, and Control Conference, 1846–1868.CrossRefGoogle Scholar
Zhang, Z., Wu, J., Dai, J. and He, C. (2021). Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 1–10. DOI: 10.1177/09544100211007381.Google Scholar
Zhao, Y., Zheng, Z. and Liu, Y. (2018). Survey on computational-intelligence-based UAV path planning. Knowledge-Based Systems, 158, 54–64.CrossRefGoogle Scholar
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