Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
stomp_moveit::cost_functions::CollisionCheckAssigns a cost value to each robot state by evaluating if the robot is in collision
stomp_moveit::noisy_filters::JointLimitsChecks that the joint values are within the limits as defined in the urdf file. It modifies the values of those joints that exceed the limits
stomp_moveit::utils::KinematicConfigConvenience structure that contains the variables used in solving for an ik solution
stomp_moveit::noisy_filters::MultiTrajectoryVisualizationPublishes rviz markers to visualize the noisy trajectories
stomp_moveit::utils::MultivariateGaussianGenerates samples from a multivariate gaussian distribution
stomp_moveit::noise_generators::NormalDistributionSamplingUses a normal distribution to apply noise onto the trajectory
PluginDataPacks plugin information into a single struct
stomp_moveit::utils::polynomial::PolyFitRequestThe Polynomial Fit request data
stomp_moveit::utils::polynomial::PolyFitResultsThe Polynomial Fit results data
stomp_moveit::update_filters::PolynomialSmootherThis is a constrained polynomial trajectory smoother
stomp_moveit::cost_functions::StompCostFunctionAssigns a cost value to each robot state by evaluating the minimum distance between the robot and the nearest obstacle
stomp_moveit::StompNoiseGeneratorInterface class for plugins that apply random noise to the trajectory in order to explore the workspace
stomp_moveit::noisy_filters::StompNoisyFilterInterface class for filtering noisy trajectories
stomp_moveit::StompOptimizationTaskLoads and manages the STOMP plugins during the planning process
stomp_moveit::StompPlannerThe PlanningContext specialization that wraps the STOMP algorithm
stomp_moveit::StompPlannerManagerThe PlannerManager implementation that loads STOMP into moveit
stomp_moveit::update_filters::StompUpdateFilterInterface class which applies filtering methods to the update parameters before it is added onto the optimized trajectory
stomp_moveit::update_filters::TrajectoryVisualizationPublishes rviz markers to visualize the optimized trajectory
stomp_moveit::update_filtersUses a Control Cost Matrix projection in order to smooth out the trajectory. The matrix is built using a numerical derivative method
stomp_moveit::update_filters::UpdateLoggerSaves the update values into a file for post analysis. The file is compatible with the python numpy library and can be loaded into a numpy array by running. 'numpy.loadtxt(file_name)'

Author(s): Jorge Nicho
autogenerated on Sat Jun 8 2019 19:24:01