A simple vision-based navigation and control strategy for autonomous drone racing

In this paper, we present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags. A simple and low-cost DJI Tello EDU quad-rotor platform was used. Based on the API provided by the manufacturer, we have created a Python application that enables the communication with the drone over WiFi, realises drone positioning based on visual feedback, and generates control. Two control strategies were proposed, compared, and critically analysed. In addition, the accuracy of the positioning method used was measured.<br>The application was evaluated on a laptop computer (about 40 fps) and a Nvidia Jetson TX2 embedded GPU platform (about 25 fps). We provide the developed code on GitHub. <br>

A simple vision-based navigation and control strategy for autonomous drone racing

In this paper, we present a control system that allows a drone to fly autonomously through a series of gates marked with ArUco tags. A simple and low-cost DJI Tello EDU quad-rotor platform was used. Based on the API provided by the manufacturer, we have created a Python application that enables the communication with the drone over WiFi, realises drone positioning based on visual feedback, and generates control. Two control strategies were proposed, compared, and critically analysed. In addition, the accuracy of the positioning method used was measured.<br>The application was evaluated on a laptop computer (about 40 fps) and a Nvidia Jetson TX2 embedded GPU platform (about 25 fps). We provide the developed code on GitHub. <br>

Wireless power charging of drone using vision-based navigation

For more efficient aerial surveillance, charging pads are set up at corresponding distances so that an unmanned aerial vehicle (UAV) can sustain its operations without landing. Usually manual intervention is required to land a UAV for charging and so extend its mission. To enable a UAV to operate autonomously, wireless power charging using inductive coupling is proposed. Using this method, the UAV's battery is charged until it reaches the next charging station. This paper focuses on two significant aspects of the process: vision-based navigation for charging pad detection, and wireless power charging. The coils were designed, and other parameters like mutual inductance, coupling coefficient and the distance between the coils for effective power transmission were analysed, using Ansys and Maxwell software. A quadcopter was built, with battery and Lidar sensor connected to the Arduino controller for low battery voltage detection and height measurement, respectively. Whenever the battery voltage is low, the UAV is steered towards the nearest charging pad using the global position navigation system. To test the process, the quadcopter was flown over the charging pad using a vision-based algorithm pre-defined in the image processor (Raspberry Pi B+).

Autonomous Unmanned Aerial Vehicle for Post-Disaster Management With Cognitive Radio Communication

Unmanned aerial vehicles (UAV) play a significant role in finding victims affected in the post-disaster zone, where a man cannot risk his life under a critical condition of the disaster environment. The proposed design incorporates autonomous vision-based navigation through the disaster environment based on general graph theory with dynamic changes on the length between two or multiple nodes, where a node is a pathway. Camera fixed on it continuously captures the surrounding footage, processing it frame by frame on-site using image processing technique based on a SOC. Identifies victims in the zone and the pathways available for traversal. UAV uses an ultrasonic rangefinder to avoid collision with obstacles. The system alerts the rescue team if any victim detected and transmits the frames using CRN to the off-site console. UAV learns navigation policy that achieves high accuracy in real-time environments; communication using CRN is uninterrupted and useful during such emergencies.