Ricerc@Sapienza

An innovative coil configuration for inductively-based wireless power transfer (WPT) technology is presented to recharge the battery of a compact unmanned aerial vehicle (UAV) (i.e., a remotely controlled drone). To improve the tolerance of the WPT system to coil misalignment caused by imperfect landing, a large primary coil is proposed in combination with a single turn, secondary coil. The on-board secondary coil is integrated in a part of the drone used as protection of the propellers.

A near-field wireless power transfer (WPT) technology is applied to recharge the battery of a small size drone. The WPT technology is an extremely attractive solution to build an autonomous base station where the drone can land to wirelessly charge the battery without any human intervention. The innovative WPT design is based on the use of a mechanical part of the drone, i.e., landing gear, as a portion of the electrical circuit, i.e., onboard secondary coil.

A high power and high efficiency Wireless Power Transfer (WPT) system based on magnetic resonant coupling is proposed to automatically recharge the battery of a small Unmanned Aerial Vehicle (UAV) (a remotely controlled drone). The drone is equipped with a WPT receiving (Rx) circular coil with an electronic system to control the battery charging process, while the WPT transmitting (Tx) circular coil is placed on a ground station and connected to the electric power feeding system.

In the last years, the technology of small-scale Unmanned Aerial Vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has lead to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. These applications often require to stitch together several video frames to get a comprehensive view of large areas (mosaicking), or to detect differences between images or mosaics acquired at different times (change detection).

UAV-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers).