nav.StateSpace class

Package: nav

Create state space for path planning

Description

nav.StateSpace is an interface for state spaces used for path planning. Derive from this class if you want to define your own state space. This representation allows for sampling, interpolation, and calculating distances between spaces in the state space.

To create a sample template for generating your own state space class, call createPlanningTemplate. For our implementations of the state space class, see State Spaces.

The nav.StateSpace class is a handle class.

Class Attributes

Abstract
true

For information on class attributes, see Class Attributes (MATLAB).

Creation

Description

ssObj = nav.StateSpace(Name,NumStateVariables,Bounds) creates a state space object with a given name and state bounds. NumStateVariables specifies the number of state variables. This constructor can only be called from a derived class. Create your own class definition using createPlanningTemplate.

Properties

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Public Properties

Number of variables in state space, specified as a positive numeric scalar. This property is the dimension of the state space.

Example: 3

Attributes:

SetAccess - immutable

Min and max bounds of state variables, specified as a [min max] n-by-2 matrix. This property depends on NumStateVariables, where n is the number of state variables. When specifying on construction, use the Bounds input.

Example: [-10 10; -10 10; -pi pi]

Attributes:

GetAccess
public
SetAccess
protected
Dependent
true

Data Types: double

Protected Properties

Name of the state space object, specified as a string scalar or character vector.

Example: "customSE2StateSpace"

Attributes:

GetAccess
protected
SetAccess
protected

Methods

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Examples

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This example shows how to use the createPlanningTemplate function to generate a template for customizing your own state space definition and sampler to use with path planning algorithms. A simple implementation is provided with the template.

Call the create template function. This function generates a class definition file for you to modify for your own implementation.

createPlanningTemplate

Class and Property Definition

The first part of the template specifies the class definition and any properties for the class. Derive from the nav.StateSpace class. For this example, create a property for the uniform and normal distributions. You can specify any additional user-defined properties here.

classdef MyCustomStateSpace < nav.StateSpace & ...
        matlabshared.planning.internal.EnforceScalarHandle
     properties
        UniformDistribution
        NormalDistribution
        % Specify additional properties here
    end

Save your custom state space class and ensure your file name matches the class name.

Class Constructor

Use the constructor to set the name of the state space, the number of state variables, and define its boundaries. Alternatively, you can add input arguments to the function and pass the variables in when you create an object.

  • For each state variable, define the [min max] values for the state bounds.

  • Call the constructor of the base class.

  • For this example, you specify the normal and uniform distribution property values using predefined NormalDistribution and UniformDistribution classes.

  • Specify any other user-defined property values here.

 methods
        function obj = MyCustomStateSpace
            spaceName = "MyCustomStateSpace";
            numStateVariables = 3;
            stateBounds = [-100 100;  % [min max]
                           -100 100;
                           -100 100];
            
            obj@nav.StateSpace(spaceName, numStateVariables, stateBounds);
            
            obj.NormalDistribution = matlabshared.tracking.internal.NormalDistribution(numStateVariables);
            obj.UniformDistribution = matlabshared.tracking.internal.UniformDistribution(numStateVariables);
            % User-defined property values here
        end

Copy Semantics

Specify the copy method definition. Copy all the values of your user-defined variables into a new object, so copyObj is a deep copy. The default behavior given in this example creates a new copy of the object with the same name, state bounds, and distributions.

        function copyObj = copy(obj)
            copyObj = feval(class(obj));
            copyObj.StateBounds = obj.StateBounds;
            copyObj.UniformDistribution = obj.UniformDistribution.copy;
            copyObj.NormalDistribution = obj.NormalDistribution.copy;
        end

Enforce State Bounds

Specify how to ensure states are always within the state bounds. For this example, the state values get saturated at the minimum or maximum values for the state bounds.

        function boundedState = enforceStateBounds(obj, state)
            nav.internal.validation.validateStateMatrix(state, nan, obj.NumStateVariables, "enforceStateBounds", "state");
            boundedState = state;
            boundedState = min(max(boundedState, obj.StateBounds(:,1)'), ...
                obj.StateBounds(:,2)');
            
        end

Sample Uniformly

Specify the behavior for sampling across a uniform distribution. support multiple syntaxes to constrain the uniform distribution to a nearby state within a certain distance and sample multiple states.

STATE = sampleUniform(OBJ)
STATE = sampleUniform(OBJ,NUMSAMPLES)
STATE = sampleUniform(OBJ,NEARSTATE,DIST)
STATE = sampleUniform(OBJ,NEARSTATE,DIST,NUMSAMPLES)

For this example, use a validation function to process a varargin input that handles the varying input arguments.

         function state = sampleUniform(obj, varargin)
            narginchk(1,4);
            [numSamples, stateBounds] = obj.validateSampleUniformInput(varargin{:});
            
            obj.UniformDistribution.RandomVariableLimits = stateBounds;
            state = obj.UniformDistribution.sample(numSamples);
        end

Sample from Gaussian Distribution

Specify the behavior for sampling across a Gaussian distribution. Support multiple syntaxes for sampling a single state or multiple states.

STATE = sampleGaussian(OBJ, MEANSTATE, STDDEV)
STATE = sampleGaussian(OBJ, MEANSTATE, STDDEV, NUMSAMPLES)

        function state = sampleGaussian(obj, meanState, stdDev, varargin)    
            narginchk(3,4);
            
            [meanState, stdDev, numSamples] = obj.validateSampleGaussianInput(meanState, stdDev, varargin{:});
            
            obj.NormalDistribution.Mean = meanState;
            obj.NormalDistribution.Covariance = diag(stdDev.^2);
            
            state = obj.NormalDistribution.sample(numSamples);
            state = obj.enforceStateBounds(state);
            
        end

Interpolate Between States

Define how to interpolate between two states in your state space. Use an input, fraction, to determine how to sample along the path between two states. For this example, define a basic linear interpolation method using the difference between states.

        function interpState = interpolate(obj, state1, state2, fraction)
            narginchk(4,4);
            [state1, state2, fraction] = obj.validateInterpolateInput(state1, state2, fraction);
            
            stateDiff = state2 - state1;
            interpState = state1 + fraction' * stateDiff;
        end

Calculate Distance Between States

Specify how to calculate the distance between two states in your state space. Use the state1 and state2 inputs to define the start and end positions. Both inputs can be a single state (row vector) or multiple states (matrix of row vectors). For this example, calculate the distance based on the Euclidean distance between each pair of state positions.

        function dist = distance(obj, state1, state2)
            
            narginchk(3,3);
            
            nav.internal.validation.validateStateMatrix(state1, nan, obj.NumStateVariables, "distance", "state1");
            nav.internal.validation.validateStateMatrix(state2, size(state1,1), obj.NumStateVariables, "distance", "state2");

            stateDiff = bsxfun(@minus, state2, state1);
            dist = sqrt( sum( stateDiff.^2, 2 ) );
        end

Terminate the methods and class sections.

    end
end

Save your state space class definition. You can now use the class constructor to create an object for your state space.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Introduced in R2019b