Affiliation: | 1. Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia Department of Computer Science, Bahauddin Zakariya University, Multan, Pakistan;2. Chair & Professor, Computer Science Department, and Director of Cybersecurity Center, University of Texas-Permian Basin, Odessa, Texas, USA King Abdullah II School of Information Technology, University of Jordan, Amman, Jordan;3. Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Yunlin, ROC, Taiwan;4. Department of Electronics, National Yunlin University of Science and Technology, Yunlin, ROC, Taiwan;5. Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
Abstract: | The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps. |