isStateValid

Check if state is valid

Since R2021b

Syntax

isValid = isStateValid(manipSV,states)

Description

isValid = isStateValid(manipSV,states) checks if given states, or joint configurations, are valid for the rigid body tree robot model specified by the state validator manipSV. This object function checks for self-collisions and collisions with the environment based on the properties of the state validator.

Examples

Validate State and Motion Manipulator State Space

Generate states to form a path, validate motion between states, and check for self-collisions and environmental collisions with objects in your world. The manipulatorStateSpace object represents the joint configuration space of your rigid body tree robot model, and can sample states, calculate distances, and enforce state bounds. The manipulatorCollisionBodyValidator object validates the state and motion based on the collision bodies in your robot model and any obstacles in your environment.

Load Robot Model

Use the loadrobot function to access predefined robot models. This example uses the Quanser QArm™ robot and joint configurations are specified as row vectors.

robot = loadrobot("quanserQArm",DataFormat="row");
figure(Visible="on")
show(robot);
xlim([-0.5 0.5])
ylim([-0.5 0.5])
zlim([-0.25 0.75])
hold on

Configure State Space and State Validation

Create the state space and state validator from the robot model.

ss = manipulatorStateSpace(robot);
sv = manipulatorCollisionBodyValidator(ss,SkippedSelfCollisions="parent");

Set the validation distance to 0.05, which is based on the distance normal between two states. You can configure the validator to ignore self collisions to improve the speed of validation, but must consider whether your robot model has the proper joint limit settings set to ensure it does not collide with itself.

sv.ValidationDistance = 0.05;
sv.IgnoreSelfCollision = true;

Place collision objects in the robot environment. Set the Environment property of the collision validator object using a cell array of objects.

box = collisionBox(0.1,0.1,0.5); % XYZ Lengths
box.Pose = trvec2tform([0.2 0.2 0.5]); % XYZ Position
sphere = collisionSphere(0.25); % Radius
sphere.Pose = trvec2tform([-0.2 -0.2 0.5]); % XYZ Position
env = {box sphere};
sv.Environment = env;

Visualize the environment.

for i = 1:length(env)
    show(env{i})
end
view(60,10)

Plan Path

Start with the home configuration as the first point on the path.

rng(0); % Repeatable results
start = homeConfiguration(robot);
path = start;
idx = 1;

Plan a path using these steps, in a loop:

• Sample a nearby goal configuration, using the Gaussian distribution, by specifying the standard deviation for each joint angle.

• Check if the sampled goal state is valid.

• If the sampled goal state is valid, check if the motion between states is valid and, if so, add it to the path.

for i = 2:25
    goal = sampleGaussian(ss,start,0.25*ones(4,1));
    validState = isStateValid(sv,goal);
    
    if validState % If state is valid, check motion between states.
        [validMotion,~] = isMotionValid(sv,path(idx,:),goal);

        if validMotion % If motion is valid, add to path.
            path = [path; goal];
            idx = idx + 1;
        end
    end
end

Visualize Path

After generating the path of valid motions, visualize the robot motion. Because you sampled random states near the home configuration, you should see the arm move around that initial configuration.

To visualize the path of the end effector in 3-D, get the transformation, relative to the base world frame at each point. Store the points as an xyz translation vector. Plot the path of the end effector.

eePose = nan(3,size(path,1));

for i = 1:size(path,1)
    show(robot,path(i,:),PreservePlot=false);
    eePos(i,:) = tform2trvec(getTransform(robot,path(i,:),"END-EFFECTOR")); % XYZ translation vector
    plot3(eePos(:,1),eePos(:,2),eePos(:,3),"-b",LineWidth=2)
    drawnow
end

Input Arguments

manipSV — Manipulator state validator

manipulatorCollisionBodyValidator object

Manipulator state validator, specified as a manipulatorCollisionBodyValidator object, which is a subclass of nav.StateValidator (Navigation Toolbox). The state validator contains properties that determine the behavior of this function and isMotionValid.

states — Robot states in joint space

m-by-n matrix

Robot states in the joint space, specified as an m-by-n matrix of joint positions. m is the total number of states and n is the number of nonfixed joints in the rigidBodyTree robot model.

Data Types: double

Output Arguments

isValid — Validity of state

m-element vector of logical scalars

Validity of input states returned as an m-element vector of logical scalars, where m is the total number of states. A state is valid, 1 or true, if it does not result in self-collisions, collisions with the environment, and it is within state bounds. Otherwise the state is invalid, 0 or false.

Data Types: logical

Version History

Introduced in R2021b

R2023b: Improved Performance of Object Functions

These object functions of the manipulatorCollisionBodyValidator object have improved performance in MATLAB^® R2023b interpreted execution compared to R2023a. For example, this code is about 4 times faster than in the previous release:

function timingtest
    rng(10);
    kuka=loadrobot("kukaIiwa14",DataFormat="row");
    env={collisionBox(0.5,0.5,0.05),...
        collisionSphere(0.3)};
    env{1}.Pose=trvec2tform([0,0,-0.05]);
    env{2}.Pose=trvec2tform([0.1,0.2,0.8]);
    startConfig=[0.08 -0.65 0.05 0.02 0.04 0.49 0.04];
    goalConfig=[2.97 -1.05 0.05 0.02 0.04 0.49 0.04];
    ss=manipulatorStateSpace(kuka);
    sv=manipulatorCollisionBodyValidator(ss,Environment=env,SkippedSelfCollisions="parent",ValidationDistance=0.01);
    tic;
    isvalid=isStateValid(sv,sampleUniform(ss,1e4));
    isvalid=isMotionValid(sv,startConfig,goalConfig);
    toc
end

The approximate execution times are:

R2023a: 68.1 s

R2023b: 18.0 s

This code was timed on a Windows 10 system with a 3.60 GHz Intel^® W-2133 CPU with 64 GB of RAM.