Since Amazon robotics expanded the use of drones to package deliveries to customers, drone applications have been expanded to many industries along with its ability to perform various tasks autonomously. The fundamental technology of drones’ autonomy comes from perceiving its surrounding, creating its own map based on onboard sensors and estimate its location within the map.
Unmanned aerial vehicles (UAVs) have become increasingly used in the fields of data collection, surveillance, and search and rescue missions. For many UAVs, knowledge of the instantaneous wind speed and direction and overall wind field are of interest, as this knowledge can help better control the position and orientation of the UAV in heavy wind. In addition, knowledge of the wind field can be used to prolong flight times and improve the efficiency of flight of the UAV, by planning paths for the UAV to follow that exploit the wind and harvest energy from it.
Since Amazon robotics expanded the use of drones to package deliveries to customers, drone applications have been expanded to many industries along with its ability to perform various tasks autonomously. The fundamental technology of dronesâ autonomy comes from perceiving its surrounding, creating its own map based on onboard sensors and estimate its location within the map.
Quadrotors are one of the most popular choices for unmanned aerial vehicles (UAVs) in situations where fast disturbance rejection, vertical takeoff and landing (VTOL) capabilities, and maneuverability are required. However, the quadrotor is inherently underactuated, and as a result, it is impossible to independently control the orientation and position of the vehicle. One solution to this problem involves rotors that can rotate relative to the vehicle frame, allowing for the angle of each rotor relative to the main vehicle frame to be independently controlled.
The ability for an autonomous robot to create its own map based on onboard sensors and simultaneously, localize itself within this map is know as Simultaneous Localization and Mapping (SLAM). Although the theory behind SLAM has been well developed much work still needs to be done in realizing SLAM solutions that meet situation-specific real-world requirements. This is because sensors and actuators onboard a robot are always corrupted by noise. In particular, Unmanned Aerial Vehicles (UAVs), which travel in 3D, face additional difficulties due to the nonlinearities associated with rotation.
The first step for any robot to achieve true autonomy is to create a map of its surroundings and localize itself within this map at the same time. This is popularly known as the Simultaneous Localization and Mapping (SLAM) problem. Although much theory has been developed over the years to solve the SLAM problem, researchers have been having difficulties in real-world application. This is because sensors and actuators onboard a robot are always corrupted by noise. In particular, Unmanned Aerial Vehicles (UAVs) face additional difficulties that land vehicles do not.
Unmanned Aerial Vehicles (UAVs) became increasingly more popular since the global industry realized the unlimited possible applications assignable to these vehicles for reasonable costs. In this way, the company ARA Robotique designs flight controllers for multi-rotors UAVs that need accurate positions, velocities and attitude (roll, pitch, yaw) knowledge. This project focuses on designing a low-cost inertial navigation system (INS) suitable for aerial navigation.
ARA Robotique is a company specialized in the development of a state-of-art flight controller for light multirotor UAV. One of the critical subsystems of a flight controller is its navigation system which measures the position and the orientation of the vehicle which is then used to ensure the flight stability and to operate the UAV. To complete its flight controller design, ARA Robotique is interested in developing a robust and accurate Inertial Navigation System (INS) based on low cost Microelectromechanical system (MEMS) technology.
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