Name: autopilot sensors for uav flight control
Uploaded: 2023-04-30T07:05:02.000Z
Duration: 8 min 36 s
Channel: JoVE

This procedure will illustrate IMU and ADS sensor calibration and integration with flight computers and demonstrate the use of integrated INS and ADS data acquisition and processing using in an outdoor flight facility. End-to-end flight control for a quadrotor operating in the University of Michigan’s M-Air netted flight test facility is demonstrated.
1. Sensor Calibration: Inertial Measurement Unit (IMU)
Sensor calibration is most effective when performed with support from high-quality test equipment. For the 3-axis IMU, calibrate the rate gyro and accelerometer for each axis separately using a precision rate table (Figure 6). The rate table precisely rotates at a user-defined angular velocity. The user issues a series of rate commands, during which the IMU collects the data needed for sensor calibration. The single-axis calibration experiment described below is therefore repeated three times, once for each IMU sensor axis (x, y, z). 
<ol>
	<li>Mount the IMU on the rate table such that the sensor axis being calibrated is directed radially inward or outward.</li>
	<li>Measure the distance from the center of the table to the center of the IMU center. This is the reference radius for circular motion. </li>
	<li>Mount the DAQ computer, IMU, and battery directly to the rate table, and connect all components directly.</li>
	<li>Set up software to collect IMU rate and acceleration data.</li>
	<li>While the rate table is motionless, record rate gyro, and accelerometer bias values.</li>
	<li>Conduct a series of experiments with different positive and negative constant rate table rotation rates. Sensor calibrations are expected to be linear. Acquire data at rates of 0 (baseline), ±15, ±30, and ±60 degrees/second. The table can spin at faster rates, but the selected values are sufficient to cover signals expected in typical UAV flight operations.</li>
	<li>Collect data from the rate gyro and accelerometer being calibrated for each angular velocity value listed above. Each rotation rate should be established before data is collected to ensure a constant rate is maintained. Collect data over 10 - 15 s, assuming a data collection rate of at least 30 - 100 Hz, to ensure disturbances can be filtered out of final calibration values.</li>
	<li>Disconnect and remove the IMU from the rate table and orient it such that the accelerometer being calibrated points down.</li>
	<li>Collect +1g data through the computer system. </li>
	<li>Flip the IMU such that the accelerometer being calibrated points up and collect -1g data through the computer system. These extra data points are simple to obtain and can be used to validate each linear calibration curve obtained from rate table data at ±1g.  The 1g value is particularly important to calibrate accurately because linear accelerometer data is used to determine the direction “down” relative to the quadcopter body.</li>
	<li>Process the data. Develop linear curve fits for gyro and accelerometer data points, which relates acquired voltages to MKS unit rotational rates (gyro) and linear accelerations (accelerometer). Confirm that calibration error is sufficiently low. Note that the rate table provides direct control of angular velocity for the gyro calibration. The corresponding acceleration, a, induced by the centripetal force of circular motion, can be computed from the specified angular velocity ω and the radius r of the IMU from the rate table center: 
	<img src="/files/ftp_upload/10470/10470eq17.jpg" alt="Angular acceleration equation \( a = \omega^2 r \); physics formula for circular motion." data-altupdatedat="1761830439000">    (9)</li>
</ol>
2. Quadrotor Flight Experiments
For our final series of experiments, we mount the IMU and pitot system on a quadrotor (shown in Figure 7) and fly in the University of Michigan’s M-Air netted flight facility. The vehicle is stabilized through a port of the Ardupilot open source autopilot package to the Beaglebone Blue (no microprocessor used) and configured before flight through the Mission Planner ground station software. A radio-control transmitter/receiver interface enables the pilot to provide “outer loop” commands for quadrotor altitude, side-to-side motion, and heading to Ardupilot’s “inner loop” flight control law regulating quadrotor roll angle, pitch angle, yaw angle (heading), and altitude. [14] 
Because a quadrotor does not require airspeed feedback to stabilize, Ardupilot only relies on IMU data plus a pressure sensor for altitude, which is calibrated during program initialization relative to the takeoff altitude pressure, to stabilize flight given pilot inputs. A fully autonomous extension of Ardupilot requires inertial position data from GPS or other sensing system (e.g., high-speed motion capture). Because our experiments were performed with quadrotors in constrained environments, the pitot air data system is not necessary.  However, pitot systems are essential for fixed-wing aircraft and multicopters attempting precise flight paths following uncertain windy environments. [15, 16] The flight test procedure is divided into three phases: pre-flight, flight test, and post-flight. This subdivision is similar to the procedures followed by pilots of manned aircraft through the use of well-established cockpit checklists. [17]
Pre-flight
<ol>
	<li>Charge batteries and test them before installation.</li>
	<li>Establish a clear test environment (indoor or outdoor), and mark the area to assure uninvolved people remain clear. </li>
	<li>Make sure the flight test team is briefed and qualified (trained) to perform the planned test.</li>
	<li>If flying outdoors, make sure the aircraft and pilot are registered and certified per FAA regulations. A minimum of three people is required for an open-air test: A pilot in command (PIC), visual observer (VO), and ground station operator. For our tests, the quadrotor will fly in a netted facility outdoors. Two tether operators will assure the vehicle cannot fly away for indoor testing. Note that no specific FAA regulations apply to netted flight testing since the UAV does not occupy an open outdoor space.</li>
	<li>Turn on flight computer and ground station laptop. </li>
	<li>Collect preliminary data to ensure that the sensors are functioning properly. The pilot and support team must ensure a clear understanding of the flight plan and that abort/recovery procedures are in place.</li>
</ol>
Flight Test
<ol start="6">
	<li>Start data acquisition on the ground station.</li>
	<li>Confirm flight area is clear/safe. </li>
	<li>Arm thrusters/motors.</li>
	<li>Initiate flight test sequence.</li>
	<li>Conduct the flight test, with the pilot calling out each step, including as a minimum: 
	takeoff (launch), flight mode changes, known waypoint targets or maneuvers, and landing.  Ensure that all personnel are on task and execute emergency procedures (flight termination) as needed. Waypoints and trajectories are specific to each flight. For the quadrotor experiment, we follow moderately aggressive cross and rectangular patterns at a constant altitude and heading, followed by a climb/descent then a yaw sequence. The angular rates and linear accelerations in this flight are easily identified in the data, and they confirm that the IMU and flight controller are functioning correctly.</li>
</ol>
Post-flight
<ol start="11">
	<li>Disarm motors to assure that they will not accidentally turn on. </li>
	<li>Save and download flight data to archival storage.</li>
	<li>Log flight in words per the pilot, VO, and ground station operator feedback.</li>
	<li>Check the batteries and charge as needed. </li>
	<li>Recover equipment, and clean the area for the next occupant.</li>
</ol>

Here we described the sensor systems, data acquisition, and signal filtering process required to enable fixed-wing and rotary-wing aircraft real-time flight control. This data pipeline is an essential element of all manned and unmanned aircraft autopilot systems. Multicopters require autopilots to stabilize, and aircraft of all types critically rely on real-time data acquisition and flight control for all operations as we move toward increasingly autonomous aircraft systems conducting missions involving airborne data col...

real-time flight control: embedded sensor calibration and data acquisition

Source: Ella M. Atkins, Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI
Overview
Autopilot allows aircraft to be stabilized using data collected from onboard sensors that measure the aircraft&#8217;s orientation, angular velocity, and airspeed. These quantities can be adjusted by the autopilot so that the aircraft automatically follows a flight plan from launch (takeoff) through recovery (landing). Similar sensor data is collected to control all types of aircraft, from large fixed-wing commercial transport aircraft to small-scale multiple-rotor helicopters, such as the quadcopter with four thruster units.
With inertial position and velocity captured by a sensor such as the Global Positioning System (GPS), the autopilot real-time flight control system enables a multicopter or fixed-wing aircraft to stabilize its attitude and airspeed to follow a prescribed trajectory. Sensor integration, calibration, data acquisition, and signal filtering are prerequisites for experiments in flight control.
Here we describe a sensor suite that provides the necessary data for flight control. Signal interfaces and data acquisition on two different embedded computer platforms are described, and sensor calibration is summarized. Single-channel moving average and median filters are applied to each data channel to reduce high-frequency signal noise and eliminate outliers.
In this experiment, data acquisition and sensor calibration for real-time flight control is demonstrated. Several published papers have described the principles of sensor data collection and control, and they have recently focused on sensors for small unmanned aerial vehicles (UAVs) [1-3].

Sensor Calibration
An example of a rate gyro calibration plot is shown in Figure 8. In this case, the rate gyro emits a nominal (zero-speed) reading of 2.38 V. Rate gyro voltage data was collected for six different rotational speeds measured in degrees per second, and a linear curve was fit to this data. As shown, the linear fit provides a very good approximation of all collected data points.
Flight Test Results
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Real-time Flight Control: Embedded Sensor Calibration and Data Acquisition

Source: Ella M. Atkins, Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI
Overview
Autopilot allows aircraft to be stabilized ...

A fixed wing aircraft achieves steady flight by balancing four forces: aerodynamic lift, aerodynamic drag, propulsion system thrust and weight. To achieve stable flight, it must also balance moments about all three axis, the roll, pitch and yaw axis. All rotations are defined as angles about these axis with changes in the roll axis causing side-to-side motion, changes to the pitch axis causing forward and backward tilting motion and changes in the yaw axis causing heading changes.In order to stabilize the aircraft to any sudden changes like gusts of wind, a flight control system issues motor and control surface commands that must be updated in real-time. Thus, the control system uses various sensors to maintain an accurate measurement of current altitude, meaning the roll, pitch and yaw angles, as well as the air speed. Once data is acquired from the sensors, the signals are filtered to reduce the impact of noise and outliers on processed data quality. The data is then aggregated into a full estimate of aircraft state and used for flight control.Both fixed wing aircraft and multicopters rely on this control system to monitor and control aircraft altitude. Both also utilizes sensor sweep known as an inertial measurement unit or IMU.An IMU typically consists of three sensor types: accelerometers to measure linear acceleration, rate gyroscopes to measure angular velocity and magnetic field sensors to measure the direction and strength of the local magnetic field. An IMU is often coupled with a GPS system and mounted near the aircraft center of gravity with the sensor axis aligned with the axis of the aircraft body.In this lab, we will demonstrate the calibration of a simple IMU using a precision rate table. We'll then mount the calibrated IMU to a multicopter and perform a flight test to view real time and filter data.In the first part of the experiment, we will calibrate the IMU which contains a rate gyro and accelerometer for each axis using a precision rate table. The rate table precisely rotates at a user defined velocity following a series of rate commands. This enables us to determine the relationship between the voltage readout and velocity.To begin, mount the IMU on the rate table with screws and orient it such as that the sensor axis being calibrated in this case the X-axis, is directly radially inward or outward. Measure the distance from the table center to the IMU center and use this measurement as the reference radius for circular motion. The IMU is mounted on a data acquisition board. Connect the components directly.Now, set up the software to collect the IMU rate and acceleration data. Conduct a series of experiments with different positive and negative constant rate table rotation rates with zero used as the baseline measurement. While the rate table is motionless, record the rate gyro and accelerometer by S values. Then, initiate the test and collect the data.Once all the angular velocities have been tested for that orientation, detach the IMU and reposition it such that the accelerometer is oriented upward. Reattach it, then initiate the test to collect -1 G data. After that, flip the IMU so that the accelerometer is oriented downward and collect +1 G data.When you have completed the calibration of the x-axis, reposition the IMU so that the z-axis sensor is orientally radially outward and repeat all tests, remembering to position the IMU upwards and downwards to calibrate the accelerometer. Perform the same procedure for the y-axis sensor.In the next part of the experiment, we will mount the IMU on the quadrotor and fly it inside of a netted flight facility. A radial control transmitter receiver interface enables the pilot to provide commands for altitude, heading, roll angle, pitch angle and yaw angle.Before starting, charge all batteries and test the components prior to installation on the quadrotor. Then prepare the flight making sure that at least three people, the pilot in command, the visual observer and the ground station operator are all briefed on the flight plans. Bring the quadrotor into the netted flight facility and set it on a flat landing board.The flight test begins with take off from the origin climbing to a 1.5 m altitude. Then, we'll execute a two meter square flight pattern with a 0.5 m/s reference velocity. The quadrotor pauses prior to each change of position. Then we'll execute segments of higher speed traversals at 0.5, 1, and 1.5 m/s to demonstrate how velocity impacts overshoot.To begin the flight test, start the data acquisition on the ground station. After confirming that the flight area is clear, arm the motors. Now, initiate the flight test sequence with the pilot calling out each step before performing them beginning with takeoff. Be sure to announce all flight mode changes, known waypoint targets, or maneuvers.After the flight plan has been executed, alert the rest of the flight team of the final descent and landing of the quadcopter. Then, disarm the motors on the quadcopter. Save and download all flight data and log the flight in the flight logbook. Finally, recover all equipment and clear the area for the next user.Now let's interpret the results. Starting with the calibration data for the IMU, first we show a plot of rotational speed of the rate table versus the gyro voltage. Note that the rate table provides direct control of angular velocity for the gyro calibration. A linear fit to the data enables the calculation of speed from gyro voltage. In this case, the rate gyro emits a nominal zero speed reading of 2.38 volts.Finally, let's look at the flight data. Here we show a 30 second lateral acceleration data set for the quadrotor using our calibrated IMU. This plot shows raw and filtered acceleration measurements from the IMU versus time. The data was filtered in order to remove noise from the measurement. You can see that raw noise data is attenuated. However a time delay is present in the filtered data.In summary, we learned how aircraft control systems use various sensors to measure current altitude and airspeed during flight. We then calibrated a rate gyro and accelerometer and mounted them on a quadrotor before performing flight experiments.

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autopilot sensors for uav flight control

Autopilot flight control depends on onboard sensors that measure an aircraft’s orientation, angular velocity, and airspeed. These measurements let the system stabilize the aircraft and help it follow a flight plan from takeoff through landing.

The same kind of sensor data is used in many aircraft, including large fixed-wing transport planes and small multirotor helicopters such as quadcopters with four thruster units. When inertial position and velocity are captured with a sensor such as the Global Positioning System (GPS), the autopilot can help a multicopter or fixed-wing aircraft hold its attitude and airspeed while following a set trajectory.

Accurate flight control experiments also require sensor integration, calibration, data acquisition, and signal filtering. The sensor suite in this work provides the data needed for flight control, and the signal interfaces and data acquisition methods are shown on two different embedded computer platforms.

Sensor calibration is summarized as part of the setup. To clean the data, single-channel moving average and median filters are applied to each channel. These filters reduce high-frequency noise and remove outliers, which helps prepare the measurements for real-time flight control.

Autopilot Sensors for UAV Flight Control

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Autopilot flight control depends on onboard sensors that measure an aircraft’s orientation, angular velocity, and airspeed. These measurements let the system stabilize the aircraft and help it follow a flight plan from takeoff through landing. The same kind of sensor data is used in many aircraft, including large fixed-wing transport planes and small multirotor helicopters such as quadcopters with four thruster units. When inertial position and velocity are captured with a sensor such as the...

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aeronautical engineering

This collection introduces fundamental concepts in aeronautical engineering with a focus on methods to evaluate aerodynamic performance, techniques to visualize subsonic and supersonic flow patterns, and procedures to calibrate measurement systems for real-time flight control.

See how onboard sensors, GPS, calibration, and filtering support autopilot flight control for fixed-wing aircraft and quadcopters in real time.

Autopilot Sensors for UAV Flight Control

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