This is the last evolution of our autonomous drone line-up. Once again, it is designed to participate in the Leonardo Drone Contest 2022 – third edition. In this edition, the drone will operate in an urban environment with a higher level of flexibility regarding the type of task required. The whole mission will be managed autonomously without being controlled by a human pilot. All the necessary computing must be executed on board.
To achieve the mission objectives, the drone uses a complex software suite (developed inside the team) to perform SLAM (Simultaneous Localization and Mapping) attitude determination and computer vision to perceive the environment. The drone only uses cameras and proximity sensors to orient itself since neither LIDAR nor GPS is allowed.
This is an autonomous drone we are designing and building with the aim of participating in the Leonardo Drone Contest 2021. The drone is conceived to navigate in a known area representing an urban environment with the purpose of searching for terrestrial bots, reading the Aruco codes to get information about the landing pads to search for in the field, fly toward them by avoiding the obstacles and finally land over. During the whole mission, the drone must perform autonomously, without being controlled by a human pilot. All required computing must be executed on board, thus requiring a low power, high performance computer.
To achieve the mission objectives, the drone uses SLAM (Simultaneous Localization and Mapping) for determining the state of the drone (position, attitude, speed), then computer vision and machine learning help identifying the terrestrial bots and the landing pads, finally obstacle avoidance and motion planning algorithms define the trajectory to follow for reaching the targets. The drone only uses cameras and proximity sensors to orient itself, since neither LIDAR nor GPS are allowed.
This was the first autonomous drone we designed and built. The aim was to participate in the Leonardo Drone Contest 2020. The drone is conceived to map an unknown area representing an urban environment with the purpose of searching for landing pads in a predefined sequence, fly towards them by avoiding the obstacles and finally land over. During the whole mission, the drone must perform autonomously, without being controlled by a human pilot. All required computing must be executed on board, thus requiring a low power, high performance computer.
To achieve the mission objectives, the drone uses SLAM (Simultaneous Localization and Mapping) for mapping the environment and determining the state of the drone (position, attitude, speed), then computer vision and machine learning help identifying the landing pads, finally obstacle avoidance and motion planning algorithms define the trajectory to follow for reaching the target. The drone only uses cameras and proximity sensors to orient itself and map the environment, since neither LIDAR nor GPS are allowed.