predict

Load a data file containing a pretrained MPNet into MATLAB® workspace. The MPNet has been trained on a single map for a Dubins vehicle. You can use this pretrained MPNet to find state samples between any start pose and goal pose on the map used for training.

inputData = load("officeMapTrainedMPNET.mat")

inputData = struct with fields:
      encodingSize: 0
       lossWeights: [100 100 10]
        officeArea: [1x1 occupancyMap]
    trainedNetwork: [1x1 dlnetwork]

Read the map used for training the network.

map = inputData.officeArea;

Specify the limits of the state space variables corresponding to the input map.

x = map.XWorldLimits;
y = map.YWorldLimits;
theta = [-pi pi];
stateBounds = [x; y; theta];

Configure the mpnetSE2 object to use the pretrained MPNet for predicting state samples between a start pose and goal pose. Set the EncodingSize value to 0.

mpnet = mpnetSE2(Network=inputData.trainedNetwork,StateBounds=stateBounds,EncodingSize=0);

Specify the start pose and goal pose.

start = [0 -6 0];
goal = [10 3 0];

Display the input map, and plot the start and goal poses.

figure
show(map)
hold on
plot(start(1),start(2),plannerLineSpec.start{:})
plot(goal(1),goal(2),plannerLineSpec.goal{:})
legend(Location="eastoutside")

Use the predict function iteratively to find state samples between the start and the goal poses. Use the sum of the absolute distance between the predicted state and the goal pose as the criterion for computing samples up to the goal point. Specify the distance value threshold as 1. For each new iteration, consider the predicted state sample as the new start pose. Compute the state samples until the distance between the predicted state sample and the goal pose is greater than 1. Plot the predicted state samples.

goalDistance = abs(start(1)-goal(1)) + abs(start(2)-goal(2));
while goalDistance > 1
    statenext = predict(mpnet,start,goal,map);
    start = statenext;
    goalDistance = abs(start(1)-goal(1)) + abs(start(2)-goal(2));
    plot(statenext(1),statenext(2),plannerLineSpec.state{:})
    legend("off")
end

Load a data file containing a pretrained MPNet into the MATLAB workspace. The MPNet has been trained on various 2-D maze maps with widths and heights of 10 meters and resolutions of 2.5 cells per meter. Each maze map contains a passage width of 5 grid cells and a wall thickness of 1 grid cell. You can use this pretrained MPNet to find state space samples on a random maze map with same specifications as those in the training data set.

pretrainedData = load("mazeMapTrainedMPNET")

pretrainedData = struct with fields:
      encodingSize: [9 9]
       lossWeights: [100 100 0]
        mazeParams: {[5]  [1]  'MapSize'  [10 10]  'MapResolution'  [2.5000]}
       stateBounds: [3x2 double]
    trainedNetwork: [1x1 dlnetwork]

Create a random maze map for testing the pretrained MPNet. The grid size ($\mathrm{MapSize}\times \mathrm{MapResolution}$) must be the same as that of the training data.

rng(100,"twister") % Set the seed
testMap = mapMaze(5,1,MapSize=[20 20],MapResolution=1.25);

Specify the start pose and goal pose.

start = [1 3 0];
goal = [18 18 0];

Specify the limits of the state space variables defining the test map.

x = testMap.XWorldLimits;
y =  testMap.YWorldLimits;
theta = [-pi pi];
stateBounds = [x; y; theta];

Configure the mpnetSE2 object to use the pretrained MPNet for predicting state samples on a random map.

mpnet = mpnetSE2(Network=pretrainedData.trainedNetwork,StateBounds=stateBounds,EncodingSize=pretrainedData.encodingSize);

Display the input test map, and plot the start and goal poses.

figure
show(testMap)
hold on
plot(start(1),start(2),plannerLineSpec.start{:})
plot(goal(1),goal(2),plannerLineSpec.goal{:})
legend(Location="eastoutside")

Use the predict function iteratively to find state samples between the start and the goal poses. Use the sum of the absolute distance between the predicted state and the goal pose as the criterion for computing samples up to the goal point. Specify the distance value threshold as 1. For each new iteration, consider the predicted state sample as the new start pose. Compute the state samples until the distance between the predicted state sample and the goal pose is greater than 1. Plot the predicted state samples.

goalDistance = abs(start(1)-goal(1)) + abs(start(2)-goal(2));
while goalDistance > 1
    statenext = predict(mpnet,start,goal,testMap);
    start = statenext;
    goalDistance = abs(start(1)-goal(1)) + abs(start(2)-goal(2));
    plot(statenext(1),statenext(2),plannerLineSpec.state{:})
    legend("off")
end