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uavSensor

Sensor for UAV scenario

Since R2020b

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Description

The uavSensor object creates a sensor that is rigidly attached to a UAV platform, specified as a uavPlatform object. You can specify different mounting positions and orientations. Configure this object to automatically generate readings from a sensor specified as one of these System objects.

Alternatively, you can specify the sensor as a derived class from the uav.SensorAdaptor base class.

Creation

Syntax

sensor = uavSensor(name,platform,sensormodel)
sensor = uavSensor(___,Name=Value)

Description

sensor = uavSensor(name,platform,sensormodel) creates a sensor with the specified name name and sensor model sensormodel, which set the Name and SensorModel properties respectively. The sensor is added to the platform platform specified as a uavPlatform object.

sensor = uavSensor(___,Name=Value) sets properties using one or more name-value arguments in addition to the input arguments in the previous syntax. You can specify the MountingLocation, MountingAngles, or UpdateRate properties as name-value pairs. For example, uavSensor("uavLidar",plat,lidarmodel,MountingLocation=[1 0 0])" places the sensor one meter forward in the x-direction relative to the platform body frame.

example

Properties

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Sensor name, specified as a string scalar. Choose a name to identify this specific sensor.

Example: "uavLidar"

Data Types: string | char

Sensor position on platform, specified as a vector of the form [x y z] in the platform frame. Units are in meters.

Example: [1 0 0] is 1 m in the x-direction.

Data Types: double

Sensor orientation rotation angles, specified as a vector of the form [z y x] where z, y, x are rotations about the z-axis, y-axis, and x-axis, sequentially, in degrees. The orientation is relative to the platform body frame.

Example: [30 90 0]

Data Types: double

Update rate of the sensor, specified as a positive scalar in hertz. By default, the object uses the UpdateRate property of the specified sensor model object.

The sensor update interval (1/UpdateRate) must be a multiple of the update interval for the associated uavScenario object.

Data Types: double

Sensor model for generating readings, specified as one of these System objects.

Object Functions

readGather latest reading from UAV sensor

Examples

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Open Live Script

Create a scenario to simulate unmanned aerial vehicle (UAV) flights between a set of buildings. The example demonstrates updating the UAV pose in open-loop simulations. Use the UAV scenario to visualize the UAV flight and generate simulated point cloud sensor readings.

Introduction

To test autonomous algorithms, a UAV scenario enables you to generate test cases and generate sensor data from the environment. You can specify obstacles in the workspace, provide trajectories of UAVs in global coordinates, and convert data between coordinate frames. The UAV scenario enables you to visualize this information in the reference frame of the environment.

Create Scenario with Polygon Building Meshes

A uavScenario object is a model consisting of a set of static obstacles and movable objects called platforms. Use uavPlatform objects to model fixed-wing UAVs, multirotors, and other objects within the scenario. This example builds a scenario consisting of a ground plane and 11 buildings as by extruded polygons. The polygon data for the buildings is loaded and used to add polygon meshes.

% Create the UAV scenario.
scene = uavScenario(UpdateRate=2,ReferenceLocation=[75 -46 0]);

% Add a ground plane.
color.Gray = 0.651*ones(1,3);
color.Green = [0.3922 0.8314 0.0745];
color.Red = [1 0 0];
addMesh(scene,"polygon",{[-250 -150; 200 -150; 200 180; -250 180],[-4 0]},color.Gray)

% Load building polygons.
load("buildingData.mat");

% Add sets of polygons as extruded meshes with varying heights from 10-30.
addMesh(scene,"polygon",{buildingData{1}(1:4,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{2}(2:5,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{3}(2:10,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{4}(2:9,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{5}(1:end-1,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{6}(1:end-1,:),[0 15]},color.Green)
addMesh(scene,"polygon",{buildingData{7}(1:end-1,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{8}(2:end-1,:),[0 10]},color.Green)
addMesh(scene,"polygon",{buildingData{9}(1:end-1,:),[0 15]},color.Green)
addMesh(scene,"polygon",{buildingData{10}(1:end-1,:),[0 30]},color.Green)
addMesh(scene,"polygon",{buildingData{11}(1:end-2,:),[0 30]},color.Green)

% Show the scenario.
show3D(scene);
xlim([-250 200])
ylim([-150 180])
zlim([0 50])

Figure contains an axes object. The axes object with xlabel East (m), ylabel North (m) contains 12 objects of type patch.

Define UAV Platform and Mount Sensor

You can define a uavPlatform in the scenario as a carrier of your sensor models and drive them through the scenario to collect simulated sensor data. You can associate the platform with various meshes, such as fixedwing, quadrotor, and cuboid meshes. You can define a custom mesh defined ones represented by vertices and faces. Specify the reference frame for describing the motion of your platform.

Load flight data into the workspace and create a quadrotor platform using an NED reference frame. Specify the initial position and orientation based on loaded flight log data. The configuration of the UAV body frame orients the x-axis as forward-positive, the y-axis as right-positive, and the z-axis downward-positive.

load("flightData.mat")

% Set up platform
plat = uavPlatform("UAV",scene,ReferenceFrame="NED", ...
    InitialPosition=position(:,:,1),InitialOrientation=eul2quat(orientation(:,:,1)));

% Set up platform mesh. Add a rotation to orient the mesh to the UAV body frame.
updateMesh(plat,"quadrotor",{10},color.Red,[0 0 0],eul2quat([0 0 pi]))

You can choose to mount different sensors, such as the insSensor, gpsSensor, gnssMeasurementGenerator, or uavLidarPointCloudGenerator System objects to your UAV. Mount a lidar point cloud generator and a uavSensor object that contains the lidar sensor model. Specify a mounting location of the sensor that is relative to the UAV body frame.

lidarmodel = uavLidarPointCloudGenerator(AzimuthResolution=0.3324099,...
    ElevationLimits=[-20 20],ElevationResolution=1.25,...
    MaxRange=90,UpdateRate=2,HasOrganizedOutput=true);

lidar = uavSensor("Lidar",plat,lidarmodel,MountingLocation=[0,0,-1]);

Fly the UAV Platform Along Pre-Defined Trajectory and Collect Point Cloud Sensor Readings

Move the UAV along a pre-defined trajectory, and collect the lidar sensor readings along the way. This data could be used to test lidar-based mapping and localization algorithms.

Preallocate the traj and scatterPlot line plots and then specify the plot-specific data sources. During the simulation of the uavScenario, use the provided plotFrames output from the scene as the parent axes to visualize your sensor data in the correct coordinate frames.

Visualize the scene.

[ax,plotFrames] = show3D(scene);

Update plot view for better visibility.

xlim([-250 200])
ylim([-150 180])
zlim([0 50])
view([-110 30])
axis equal
hold on

Create a line plot for the trajectory. First create the plot with plot3, then manually modify the data source properties of the plot. This improves performance of the plotting.

traj = plot3(nan,nan,nan,Color=[1 1 1],LineWidth=2);
traj.XDataSource = "position(:,2,1:idx+1)";
traj.YDataSource = "position(:,1,1:idx+1)";
traj.ZDataSource = "-position(:,3,1:idx+1)";

Create a scatter plot for the point cloud. Update the data source properties again.

colormap("jet")
pt = pointCloud(nan(1,1,3));
scatterplot = scatter3(nan,nan,nan,1,[0.3020 0.7451 0.9333],...
    Parent=plotFrames.UAV.Lidar);
scatterplot.XDataSource = "reshape(pt.Location(:,:,1),[],1)";
scatterplot.YDataSource = "reshape(pt.Location(:,:,2),[],1)";
scatterplot.ZDataSource = "reshape(pt.Location(:,:,3),[],1)";
scatterplot.CDataSource = "reshape(pt.Location(:,:,3),[],1) - min(reshape(pt.Location(:,:,3),[],1))";

Set up the simulation. Then, iterate through the positions and show the scene each time the lidar sensor updates. Advance the scene, move the UAV platform, and update the sensors.

setup(scene)
for idx = 0:size(position, 3)-1
    [isupdated,lidarSampleTime, pt] = read(lidar);
    if isupdated
        % Use fast update to move platform visualization frames.
        show3D(scene,"Time",lidarSampleTime,FastUpdate=true,Parent=ax);
        % Refresh all plot data and visualize.
        refreshdata
        drawnow limitrate
    end
    % Advance scene simulation time and move platform.
    advance(scene);
    move(plat,[position(:,:,idx+1),zeros(1,6),eul2quat(orientation(:,:,idx+1)),zeros(1,3)])
    % Update all sensors in the scene.
    updateSensors(scene)
end
hold off

Figure contains an axes object. The axes object with xlabel East (m), ylabel North (m) contains 15 objects of type patch, scatter, line.

This example uses:

Open Live Script

Create variables for the GNSS sample rate and the geodetic reference location. Set the sample rate to 10 Hz, and set the reference location to Natick, Massachusetts.

Fs = 10;
refLocNatick = [42.2825 -71.343 53.0352];

Create a UAV scenario using the specified reference location set to Natick, and set the update rate to match the GNSS sample rate.

scene = uavScenario(ReferenceLocation=refLocNatick,UpdateRate=Fs);

Create a UAV platform using the default NED reference frame.

plat = uavPlatform("UAV",scene,InitialPosition=[0 0 -30]);

Create a gnssMeasurementGenerator object with the previously specified GNSS sample rate and reference location.

gnssSensor = gnssMeasurementGenerator(ReferenceLocation=refLocNatick, ...
    SampleRate=Fs);

Attach a GNSS sensor to the UAV platform using the uavSensor object, and specify gnssSensor as the sensor model.

gnss = uavSensor("GNSS",plat,gnssSensor,MountingLocation=[0 0 -0.5]);

Set up the scenario, and get the initial pseudoranges, satellite positions, and satellite status measurements.

setup(scene)
[~,~,pr,satPos,status] = gnss.read
pr = 7×1
107 ×

    2.4225
    2.4319
    2.0445
    2.0632
    2.2279
    2.2875
    2.2568


satPos = 7×3
107 ×

   -1.5534   -0.2628    2.1383
    0.2580   -2.4886   -0.8914
    1.0703   -2.0380    1.3248
   -0.1423   -1.6301    2.0921
    1.5099   -0.2036    2.1756
   -0.3974   -2.6261   -0.0050
    2.2755   -0.8512    1.0732


status = struct with fields:
    LOS: [7x1 logical]

Advance the scene simulation time. The simulation advances a single step based on the specified update rate of 10 Hz, to 0.1 seconds.

advance(scene);

Update the sensors and get the pseudoranges, satellite positions, and satellite status measurements at the new timestep.

updateSensors(scene)
[isUpdated,timestamp,pr,satPos,status] = gnss.read
isUpdated = logical
   1


timestamp = 
0.1000

pr = 7×1
107 ×

    2.4225
    2.4319
    2.0445
    2.0632
    2.2279
    2.2875
    2.2568


satPos = 7×3
107 ×

   -1.5534   -0.2628    2.1383
    0.2580   -2.4886   -0.8914
    1.0703   -2.0380    1.3248
   -0.1423   -1.6301    2.0921
    1.5099   -0.2036    2.1756
   -0.3974   -2.6261   -0.0050
    2.2755   -0.8512    1.0732


status = struct with fields:
    LOS: [7x1 logical]

Use the pseudoranges and satellite positions to estimate the UAV position. This result shows that the UAV is hovering 30 meters above the reference location.

recPos = receiverposition(pr(status.LOS,:),satPos(status.LOS,:))
recPos = 1×3

   42.2825  -71.3433   82.2349

Version History

Introduced in R2020b

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See Also

Functions

Objects

Topics