journal article·2025-08-08Open access

Enhanced Path Planning by Repositioning the Starting Point

Gregory Gasteratos, Ioannis Karydis

Applied Sciences· Volume 15· Issue 16

Abstract

Drone power management poses ongoing challenges that significantly impact operational effectiveness across various applications. This research examines path planning optimization, particularly focusing on distance minimization to enhance efficiency and performance. When drones must visit static ground stations, analyzing the constituent elements of flight paths reveals that segments connecting the launch pad to initial and final stations emerge as a distinct area for further path optimization. Given scenarios where launch pad relocation remains feasible, this study proposes several alternative methodologies for adjusting launch positions to minimize total flight distances across multiple drone operations. The investigation employed extensive experimentation involving diverse configurations with varying station counts and available drone units. Results demonstrate that repositioning the launch pad to serve as an optimal center point for all drone routes yields substantial improvements in total distance minimization, ranging from 4% to 22% across different operational scenarios. The geometric median approach consistently outperformed alternative positioning strategies, achieving these improvements while maintaining computational efficiency. These findings contribute to sustainable drone operations by reducing energy consumption through optimized flight planning. The methodology proves particularly valuable for applications requiring flexible launch point positioning, offering practical solutions for enhancing operational efficiency in environmental monitoring, precision agriculture, and infrastructure inspection tasks where energy conservation directly impacts mission success and operational viability.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Robotic Path Planning Algorithms · Control and Dynamics of Mobile Robots · Robotic Locomotion and Control

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Informatics, Ionian University, 49132 Corfu, Greece

References (50)

Show source-provided references
1. Autonomous Unmanned Aerial Vehicles Filming In Dynamic Unstructured Outdoor Environments [Applications Corner]10.1109/MSP.2018.2875190 ↗
2. Li, X., and Yang, L. (2012, January 26–27). Design and Implementation of UAV Intelligent Aerial Photography System. Proceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanchang, China.10.1109/IHMSC.2012.144 ↗
3. Mesquita, R., and Gaspar, P. (2020, January 8–9). A Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling—Simulation Results. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain.10.1109/DASA51403.2020.9317271 ↗
4. Doherty, P., and Rudol, P. (2007, January 2–6). A UAV search and rescue scenario with human body detection and geolocalization. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Gold Coast, QLD, Australia.
5. Aljalaud, F., Kurdi, H., and Youcef-Toumi, K. (2023). Autonomous Multi-UAV Path Planning in Pipe Inspection Missions Based on Booby Behavior. Mathematics, 11.10.3390/math11092092 ↗
6. Gonzalez, L.F., Montes, G.A., Puig, E., Johnson, S., Mengersen, K., and Gaston, K.J. (2016). Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors, 16.10.3390/s16010097 ↗
7. Bondi, E., Fang, F., Hamilton, M., Kar, D., Dmello, D., Noronha, V., Choi, J., Hannaford, R., Iyer, A., and Joppa, L. (2019). Automatic Detection of Poachers and Wildlife with UAVs. Artificial Intelligence and Conservation, Cambridge University Press. Artificial Intelligence for Social Good.
8. Mapping Crop Status from an Unmanned Aerial Vehicle for Precision Agriculture Applications10.5194/isprsarchives-XXXIX-B1-485-2012 ↗
9. Optimization of Pesticide Spraying Tasks via Multi-UAVs Using Genetic Algorithm10.1155/2017/7139157 ↗
10. Yinka-Banjo, C., and Ajayi, O. (2019). Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture. Autonomous Vehicles, IntechOpen.10.5772/intechopen.89488 ↗
11. Non-contact vibration monitoring of rotating wind turbines using a semi-autonomous UAV10.1016/j.ymssp.2019.106446 ↗
12. Bemposta Rosende, S., Sánchez-Soriano, J., Gómez Muñoz, C.Q., and Fernández Andrés, J. (2020). Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants. Energies, 13.10.3390/en13215712 ↗
13. Wireless Sensor Networks and Multi-UAV systems for natural disaster management10.1016/j.comnet.2017.05.021 ↗
14. Ecke, S., Dempewolf, J., Frey, J., Schwaller, A., Endres, E., Klemmt, H.J., Tiede, D., and Seifert, T. (2022). UAV-Based Forest Health Monitoring: A Systematic Review. Remote Sens., 14.10.3390/rs14133205 ↗
15. Forestry applications of UAVs in Europe: A review10.1080/01431161.2016.1252477 ↗
16. Gröschler, K.C., and Oppelt, N. (2022). Using Drones to Monitor Broad-Leaved Orchids (Dactylorhiza majalis) in High-Nature-Value Grassland. Drones, 6.10.3390/drones6070174 ↗
17. Miller, I.J., Schieber, B., Bey, Z.D., Benner, E., Ortiz, J.D., Girdner, J., Patel, P., Coradazzi, D.G., Henriques, J., and Forsyth, J. (2020, January 24). Analyzing crop health in vineyards through a multispectral imaging and drone system. Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA.10.1109/SIEDS49339.2020.9106671 ↗
18. Wich, S.A., and Koh, L.P. (2018). Conservation Drones: Mapping and Monitoring Biodiversity, Oxford University Press.10.1093/oso/9780198787617.001.0001 ↗
19. Tovar-Sánchez, A., Román, A., Roque-Atienza, D., and Navarro, G. (2021). Applications of unmanned aerial vehicles in Antarctic environmental research. Sci. Rep., 11.10.1038/s41598-021-01228-z ↗
20. Baldazo, D., Parras, J., and Zazo, S. (2019, January 2–6). Decentralized Multi-Agent Deep Reinforcement Learning in Swarms of Drones for Flood Monitoring. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain.10.23919/EUSIPCO.2019.8903067 ↗
21. Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles10.1016/j.jhydrol.2017.02.038 ↗
22. Krishnamurthy, R., Jonathan, M., Srinivasalu, S., and Glaeser, B. (2019). Chapter 21—Using Low-Cost UAVs for Environmental Monitoring, Mapping, and Modelling: Examples From the Coastal Zone. Coastal Management, Academic Press.
23. Koparan, C., Koc, A.B., Privette, C.V., and Sawyer, C.B. (2018). In Situ Water Quality Measurements Using an Unmanned Aerial Vehicle (UAV) System. Water, 10.10.3390/w10030264 ↗
24. Wildlife research and management methods in the 21st century: Where do unmanned aircraft fit in?10.1139/juvs-2015-0021 ↗
25. Using unmanned aerial vehicles to sample aquatic ecosystems10.1002/lom3.10222 ↗
26. Julien, C., and Nazri, G.A. (1994). Solid State Batteries: Materials Design and Optimization, Springer Science & Business Media.10.1007/978-1-4615-2704-6 ↗
27. Gugan, G., and Haque, A. (2023). Path Planning for Autonomous Drones: Challenges and Future Directions. Drones, 7.10.3390/drones7030169 ↗
28. A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects10.1016/j.apenergy.2019.113823 ↗
29. Vashisth, A., Batth, R.S., and Ward, R. (2021, January 17–18). Existing Path Planning Techniques in Unmanned Aerial Vehicles (UAVs): A Systematic Review. Proceedings of the 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates.10.1109/ICCIKE51210.2021.9410787 ↗
30. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges10.1016/j.comcom.2019.10.014 ↗
31. A comprehensive review of energy sources for unmanned aerial vehicles, their shortfalls and opportunities for improvements10.1016/j.heliyon.2020.e05285 ↗
32. Optrone: Maximizing Performance and Energy Resources of Drone Batteries10.1109/TCAD.2020.3012790 ↗
33. The multiple traveling salesman problem: An overview of formulations and solution procedures10.1016/j.omega.2004.10.004 ↗
34. A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy10.1016/j.cosrev.2021.100369 ↗
35. The traveling salesman problem: An overview of exact and approximate algorithms10.1016/0377-2217(92)90138-Y ↗
36. Liu, W., Li, S., Zhao, F., and Zheng, A. (2009, January 25–27). An ant colony optimization algorithm for the Multiple Traveling Salesmen Problem. Proceedings of the IEEE Conference on Industrial Electronics and Applications, Xi’an, China.
37. Solving multiple traveling salesman problem using the gravitational emulation local search algorithm
38. Michalewicz, Z., and Fogel, D.B. (2013). How to Solve It: Modern Heuristics, Springer Science & Business Media.
39. A new local search for the bees algorithm to optimize multiple traveling salesman problem
40. Hybrid search with neighborhood reduction for the multiple traveling salesman problem10.1016/j.cor.2022.105726 ↗
41. Russell, S., and Norvig, P. (2020). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].
42. Improvements to the Or-opt heuristic for the symmetric travelling salesman problem10.1057/palgrave.jors.2602160 ↗
43. Liu, X.X., Liu, D., Yang, Q., Liu, X.F., Yu, W.J., and Zhang, J. (2021, January 5–7). Comparative Analysis of Five Local Search Operators on Visiting Constrained Multiple Traveling Salesmen Problem. Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence (SSCI), Orlando, FL, USA.10.1109/SSCI50451.2021.9659963 ↗
44. Tabu Search heuristics for the Vehicle Routing Problem with Time Windows10.1007/BF02579017 ↗
45. Gasteratos, G., and Karydis, I. (2024, January 27–30). Path Planning Optimisation for Multiple Drones: Repositioning the Starting Point. Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations, Corfu, Greece.10.1007/978-3-031-63223-5_16 ↗
46. Determining the minimum-area encasing rectangle for an arbitrary closed curve10.1145/360881.360919 ↗
47. The Centroid? Where would you like it to be be?10.1080/00690805.2002.9714213 ↗
48. A Fast Algorithm for Euclidean Bounded Single-Depot Multiple Traveling Salesman Problem
49. Minsker, S., and Strawn, N. (2023). The Geometric Median and Applications to Robust Mean Estimation. arXiv.
50. The Wilcoxon signed rank test for paired comparisons of clustered data10.1111/j.1541-0420.2005.00389.x ↗