The main innovation involves combining the ability to receive a battery from an external source, connect mechanically, hot-swap between the batteries, and dispose of the discharged battery. This unique design uses a FIFO logical process and the force of gravity to replace the energy source. We first describe the Flying Hot-Swap Battery (FHSB) system's conceptual design. The concept is composed of an additional UAV array that delivers new batteries from various ground points. This paper introduces a new concept and mechanism for an onboard system that physically replaces batteries during flight, analogous to “aerial refueling.” This capability allows drones to remain in mid-air indefinitely while pursuing their mission without forcing them to change flight paths for logistical needs. One of the major disadvantages of drones is their limited flight time. As guidance for researchers and practitioners, the paper also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions. This paper provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue operations, acting as aerial wireless BSs and drone light shows). Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ Un-manned Aerial Vehicles (UAVs/drones). It may also facilitate docking procedures where the docking station is itself moving, which may be the case if the docking unit is a mobile ground rover. In many cases, this increased precision can enable more innovative docking mechanisms, less likelihood of mishaps in docking, and also quicker docking. This 0.04 m distance represents an order of magnitude increase in location precision over other currently available solutions. Experimental results demonstrate the feasibility and accuracy of the ground landing station system, achieving average errors of less than 0.04 m with the UAV-MLE target position estimation approach. The maximum likelihood estimator (MLE) algorithm is addressed on an embedded microcontroller for the position estimation based on the RSSI acquired from an array of BLE devices. The GLS system has been embodied for the purpose of testing the UAV landing navigation capability. In this sense, the development of a novel low-cost GLS system for UAV tracking and landing is proposed. However, these RSSI-based techniques present a lack of precision due to the propagation medium characteristics, which leads to UAV position vagueness. Bluetooth low energy (BLE) technology and the received signal strength indicator (RSSI) techniques have been proposed for target location during UAV landing. In this regard, ground landing stations (GLS) systems play a central role to increase the time and area coverage of UAV missions. Earth observation with unmanned aerial vehicles (UAVs) offers an extraordinary opportunity to bridge the gap between field observations and traditional air and space-borne remote sensing.
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