Today, the development of unmanned aerial vehicles (UAVs) has attracted significant attention in both civil and military fields due to their flight flexibility in complex and dangerous environments. However, due to energy constraints, UAVs can only finish a few tasks in a limited time. The problem of finding the best flight path while balancing the task completion time and the coverage rate needs to be resolved urgently. Therefore, this paper proposes a UAV path coverage algorithm base on the greedy strategy and ant colony optimization. Firstly, this paper introduces a secondary advantage judgment and optimizes it using an ant colony optimization algorithm to reach the goal of minimum time and maximum coverage. Simulations are performed for different numbers of mission points and UAVs, respectively. The results illustrate that the proposed algorithm achieves a 2.8% reduction in task completion time while achieving a 4.4% improvement in coverage rate compared to several previous works.

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency.