Multiple Unmanned Aerial Vehicle

Multiple Unmanned Aerial Vehicle (UAV) systems research centers on coordinating multiple UAVs to achieve complex tasks more efficiently than single UAVs, with primary objectives including optimized path planning, efficient resource management, and robust communication. Current research heavily utilizes reinforcement learning (RL), particularly multi-agent RL, often incorporating deep neural networks like convolutional neural networks (CNNs) and long short-term memory (LSTMs), along with other techniques such as particle swarm optimization and ant colony optimization. This field is significant for its potential to revolutionize various applications, including search and rescue, environmental monitoring, infrastructure inspection, and communication network augmentation, by enabling more efficient and adaptable autonomous operations.