An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles

Abstract

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.

Battery consumption estimation methodology for electric unmanned aerial systems

“…On the other hand, instead of taking the parameters from the manufacturer's datasheet, making use of the equations presented in this study, the corresponding line current equation is Equation (17), and, after that, phase voltage can be estimated through Equation (5).…”

“…in the legend, and estimated results, called as Approx. in the legend, for Equation (5). The legend includes the coefficient of determination for all the estimated results, when compared with the measured ones [39].…”

“…It is true that the COVID19 pandemic has had a negative short-term impact in the capacity of companies to invest in this novel technology; however, it is expected that the companies promote the automatisation of their processes, and here, drones could have a relevant role. From the standpoint of commercial applications carried out by drones, the market is focused on light platforms, which are equipped with electric propulsion systems [3][4][5][6][7][8][9]. This market expansion is conditioned by the regulatory framework.…”

Spherical Robots for Special Purposes: A Review on Current Possibilities

“…Like autonomous cars and robots, autonomous drones must detect an obstacle in real-time and avoid it. References [182,183] describe proposals for UAV (Unmanned Aerial Vehicles), which use LiDAR technology to detect obstacles.…”

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

“…The automatic applications of UAVs rely on the continuous control of the position, attitude, speed and other high level commands [2,3]. Specifically, the attitude and height (the clearance from the ground) controllers allow terrain-following of an autonomous quadrotor, which is useful for topography prediction, navigation and obstacle avoidance [4][5][6]. An integrated navigation framework is an autonomous system used for determining the position, attitude and velocity of UAVs using multi-sensors [7,8], i.e., a global positioning system (GPS), an inertial measurement unit ((IMU) e.g., an accelerometer, a gyroscope and a magnetometer) [9] and radar or distance sensors [10][11][12][13].…”