Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum due to their operational advantages. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Additionally, obtaining optimal flight paths can lower energy consumption, thereby extending battery life for other critical operations. Many of these scenarios, however, involve obstacles such as power lines and trees, which complicate Path Planning. This paper presents an evolutionary computation-based system employing genetic algorithms to address this problem in environments with obstacles. The proposed approach aims to ensure complete coverage of areas with fixed obstacles, such as in field exploration tasks, while minimizing flight time regardless of map size or the number of UAVs in the swarm. No specific goal points or prior information beyond the provided map is required. The experiments conducted in this study used five maps of varying sizes and obstacle densities, as well as a control map without obstacles, with different numbers of UAVs. The results demonstrate that this method can determine optimal paths for all UAVs during full map traversal, thus minimizing resource consumption. A comparative analysis with other state-of-the-art approach is presented to highlight the advantages and potential limitations of the proposed method.

Abstract--This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications. In order to provide seamless network services in crowded, wild, or extreme areas, which is one of the potential scenarios in 6G networks, the use of unmanned aerial vehicles (UAVs) is widely considered where the UAVs are autonomously operated with deep learning algorithms [1]. In this paper, a multi-agent deep reinforcement learning (MADRL) algorithm is designed and evaluated for autonomous is good enough to utilize the desired performance of multiagent aerial mobile base-station (BS) network coordination cooperation and coordination. In order to neural network, a cost function is required, and the function is achieve our desired goal, one of the promising approaches is designed to maximize the quality of services (QoS) in mobile centralized training and distributed execution (CTDE) where access applications.