#Jupyterlab docker image full

In this paper, we demonstrate a method for evaluating and selecting suitable hardware to be used for deployment of algorithms for UAV-based remote sensing under considerations of Size, Weight, Power, and Computational constraints. The use of UAVs for remote sensing is increasing.

Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment tools. Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). It is an open framework for distance robotics learning in engineering degrees. RoboticsAcademy is also briefly presented in the paper. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. The course has been successfully taught for five years to students from several university engineering degrees. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. Therefore, the proposed course emphasizes drone programming through practical learning. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. The students may program diverse drones on their computers without a physical presence in this course. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free.

#Jupyterlab docker image full

This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework.