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dlCHOMP

Deep learning initial guesser powered CHOMP

Since R2024a

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    Description

    The dlCHOMP object uses Deep-Learning-Based Covariant Hamiltonian Optimization for Motion Planning (DLCHOMP) for rigid body tree robot models. dlCHOMP optimizes the deep learning network to predict trajectories that are both smooth and avoid collisions.

    For an example showing how to train dlCHOMP, see Train Deep-Learning-Based CHOMP Optimizer for Motion Planning.

    To download pretrained dlCHOMP objects and their associated training data, see Pretrained Optimizers.

    To use CHOMP without deep learning, use the manipulatorCHOMP object.

    The dlCHOMP object requires Deep Learning Toolbox™.

    Creation

    Syntax

    DLCHOMP = dlCHOMP(robotRBT,encoder,numWpts)
    DLCHOMP = dlCHOMP(___,Name=Value)

    Description

    DLCHOMP = dlCHOMP(robotRBT,encoder,numWpts) creates a deep-learning CHOMP-based optimizer for a rigid body tree that encodes an obstacle environment using the specified basis point set (BPS) encoder and guesses a trajectory with the specified number of waypoints. The robotRBT, encoder, and numWpts arguments set the RigidBodyTree, BPSEncoder, and NumWaypoints properties, respectively.

    example

    DLCHOMP = dlCHOMP(___,Name=Value) specifies properties using one or more name-value arguments in addition to the input arguments from the previous syntax.

    Properties

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    This property is read-only.

    Robot model used for motion planning, specified as a rigidBodyTree object at object construction.

    You can access the spherical approximation in RigidBodyTreeSpheres as collision geometries on the tree by using the RigidBodyTree property.

    Example: dlCHOMP(loadrobot("kinovaGen3",DataFormat="row"),enc,numwpts)

    Spheres of the bodies in the rigid body tree, stored as an M-by-2 table. M is the number of bodies in the rigid body tree. The first column contains the name of a rigid body in RigidBodyTree and the second column contains a corresponding cell array. Each cell array is a single cell that contains a 4-by-N matrix, where N is the number of spheres for the corresponding rigid body. Each column of the matrix of spheres contains the information for a sphere in the form [r; x; y; z], defined with respect to the frame of the rigid body. r is the radius of the sphere, and x, y, and z are the x-, y-, and z-coordinates of the center of the sphere, respectively.

    Smoothness cost options, specified as a chompSmoothnessOptions object.

    By default, this property contains a chompSmoothnessOptions object with default property values.

    Collision cost options, specified as a chompCollisionOptions object.

    By default, this property contains a chompCollisionOptions object with default property values.

    Motion-planning solver options, specified as a chompSolverOptions object.

    By default, this property contains a chompSolverOptions object with default property values.

    Spherical obstacles in the environment, specified as a 4-by-N matrix. Each column represents the information about a sphere in the form [r; x; y; z], defined with respect to the base frame of the robot. r is the radius of the sphere, and x, y, and z are the x-, y-, and z-coordinates of the center of the sphere, respectively.

    This property is read-only.

    BPS encoder used for encoding the spherical obstacle environment, specified as a bpsEncoder object at object construction.

    The Arrangement property of the bpsEncoder object must have one of these 3-D arrangement values:

    • "uniform-ball-3d"

    • "rectangular-grid-3d"

    Example: dlCHOMP(robot,bpsEncoder("uniform-ball-3d",5000,Radius=2),numwpts)

    This property is read-only.

    Number of desired waypoints in planned trajectories, specified as a positive integer at object construction.

    This property is read-only.

    Neural network used to provide an initial guess to the CHOMP optimizer, stored as a dlnetwork (Deep Learning Toolbox) object.

    The dlCHOMP object creates the dlnetwork object, at construction, based on the specified robot model, BPS encoder, and number of waypoints. The network contains two input layers and one output layer.

    You must specify Network to construct dlCHOMP in generated code.

    This property is read-only.

    Number of inputs to the neural network, stored as a two-element row vector of positive integers in the form [Layer1InputSize Layer2InputSize]. The value of Layer1InputSize is twice the number of joints in RigidBodyTree, to represent both the start and goal configurations. Layer2InputSize is equal to the BPS encoding size.

    For example, the NumInputs property for a robot with 7 joints and a BPS encoding size of 500 is [14 500].

    This property is read-only.

    Number of outputs from the neural network, stored as a positive integer. The number of outputs is equal to the number of joints in RigidBodyTree multiplied by the number of intermediate waypoints excluding the start and goal configurations, NumWaypoints - 2.

    For example, the NumOutputs property for a robot with 7 joints and 12 desired waypoints is 70.

    Object Functions

    generateSamplesGenerate data sets for training deep-learning-based CHOMP optimizer
    trainDLCHOMPTrain deep-learning-based CHOMP optimizer
    optimizeOptimize trajectory using deep-learning-based CHOMP
    resetCHOMPOptionsReset option properties to last trained state
    showVisualize deep-learning-based CHOMP trajectory of rigid body tree

    Examples

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    Open Live Script

    Download a pretrained dlCHOMP object for the KUKA LBR iiwa 7 robot.

    dataZip = matlab.internal.examples.downloadSupportFile("rst/data/dlCHOMP/R2024a/","kukaIiwa7DLCHOMPTrained.zip");
dataFilePaths = unzip(dataZip);

    Load the trainedDLCHOMP MAT file. The file contains the trained DLCHOMP optimizer, obstacles, and start and goal configurations.

    load trainedDLCHOMP.mat
    Warning: Cannot load an object of class 'dlCHOMPDatastore':
Its class cannot be found.

    Add the obstacles to the dlCHOMP object and show the robot in the home configuration with the loaded obstacles.

    trainedDLCHOMP.SphericalObstacles = unseenObstacles;
show(trainedDLCHOMP);
title(["Robot at Home Configuration","in Obstacle Environment"])
axis auto

    Figure contains an axes object. The axes object with title Robot at Home Configuration in Obstacle Environment, xlabel X, ylabel Y contains 46 objects of type patch. These objects represent world, iiwa_link_0, iiwa_link_1, iiwa_link_2, iiwa_link_3, iiwa_link_4, iiwa_link_5, iiwa_link_6, iiwa_link_7, iiwa_link_ee, iiwa_link_ee_kuka, iiwa_link_0_mesh, iiwa_link_1_mesh, iiwa_link_2_mesh, iiwa_link_3_mesh, iiwa_link_4_mesh, iiwa_link_5_mesh, iiwa_link_6_mesh, iiwa_link_7_mesh, iiwa_link_0_coll_mesh, iiwa_link_1_coll_mesh, iiwa_link_2_coll_mesh, iiwa_link_3_coll_mesh, iiwa_link_4_coll_mesh, iiwa_link_5_coll_mesh, iiwa_link_6_coll_mesh, iiwa_link_7_coll_mesh.

    Optimize trajectory between the start and goal configuration.

    trainedDLCHOMP.RigidBodyTree.DataFormat = "column"
    trainedDLCHOMP = 
  dlCHOMP with properties:

           RigidBodyTree: [1×1 rigidBodyTree]
    RigidBodyTreeSpheres: [11×1 table]
       SmoothnessOptions: [1×1 chompSmoothnessOptions]
           SolverOptions: [1×1 chompSolverOptions]
        CollisionOptions: [1×1 chompCollisionOptions]
      SphericalObstacles: [4×24 double]
              BPSEncoder: [1×1 bpsEncoder]
            NumWaypoints: 40
                 Network: [1×1 dlnetwork]
               NumInputs: [14 10000]
              NumOutputs: 266


    [wpts,tpts,solninfo] = optimize(trainedDLCHOMP,unseenStart,unseenGoal);

    Visualize the trajectory.

    figure
a = show(trainedDLCHOMP,wpts);
title("Optimized Trajectory")
axis equal

    Figure contains an axes object. The axes object with title Optimized Trajectory, xlabel X, ylabel Y contains 464 objects of type patch. These objects represent world, iiwa_link_0, iiwa_link_1, iiwa_link_2, iiwa_link_3, iiwa_link_4, iiwa_link_5, iiwa_link_6, iiwa_link_7, iiwa_link_ee, iiwa_link_ee_kuka, iiwa_link_0_mesh, iiwa_link_1_mesh, iiwa_link_2_mesh, iiwa_link_3_mesh, iiwa_link_4_mesh, iiwa_link_5_mesh, iiwa_link_6_mesh, iiwa_link_7_mesh, iiwa_link_0_coll_mesh, iiwa_link_1_coll_mesh, iiwa_link_2_coll_mesh, iiwa_link_3_coll_mesh, iiwa_link_4_coll_mesh, iiwa_link_5_coll_mesh, iiwa_link_6_coll_mesh, iiwa_link_7_coll_mesh.

    More About

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    Tips

    Guidance for Training DLCHOMP

    Read each condition to determine which resource to use for DLCHOMP training or retraining guidance.

    1. If you do not have any trained dlCHOMP objects, then see the Train Deep-Learning-Based CHOMP Optimizer for Motion Planning example. You can also use this example if you have a trained dlCHOMP object that does not have the desired BPS encoding, robot model, or environment.

    2. If you have a trained dlCHOMP object that does not have the desired number of waypoints, but does have the desired BPS encoding, robot model, and environment, then see the Using Pretrained DLCHOMP Optimizer to Predict Higher Number of Waypoints example.

    3. If you have a trained dlCHOMP object that does not have the desired data options or CHOMP options, but does have the desired BPS encoding, robot model, environment, and number of waypoints, then see the Using Pretrained DLCHOMP Optimizer in Unseen Obstacle Environment example.

    References

    [1] Tenhumberg, Johannes, Darius Burschka, and Berthold Bäuml. “Speeding Up Optimization-Based Motion Planning through Deep Learning.” In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7182–89. Kyoto, Japan: IEEE, 2022. https://doi.org/10.1109/IROS47612.2022.9981717.

    [2] Ratliff, Nathan, Siddhartha Srinivasa, Matt Zucker, and Andrew Bagnell. “CHOMP: Gradient Optimization Techniques for Efficient Motion Planning.” In 2009 IEEE International Conference on Robotics and Automation, 489–94. Kobe, Japan: IEEE, 2009. https://doi.org/10.1109/ROBOT.2009.5152817.

    Extended Capabilities

    Version History

    Introduced in R2024a

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    See Also

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