model_based_planning_context.h

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/* Author: Ioan Sucan */
 
#pragma once
 
#include <moveit/ompl_interface/parameterization/model_based_state_space.h>
#include <moveit/constraint_samplers/constraint_sampler_manager.h>
#include <moveit/planning_interface/planning_interface.h>
 
#include <ompl/geometric/SimpleSetup.h>
#include <ompl/tools/benchmark/Benchmark.h>
#include <ompl/tools/multiplan/ParallelPlan.h>
#include <ompl/base/StateStorage.h>
 
namespace ompl_interface
{
namespace ob = ompl::base;
namespace og = ompl::geometric;
namespace ot = ompl::tools;
 
MOVEIT_CLASS_FORWARD(ModelBasedPlanningContext);  // Defines ModelBasedPlanningContextPtr, ConstPtr, WeakPtr... etc
MOVEIT_CLASS_FORWARD(ConstraintsLibrary);         // Defines ConstraintsLibraryPtr, ConstPtr, WeakPtr... etc
 
struct ModelBasedPlanningContextSpecification;
typedef std::function<ob::PlannerPtr(const ompl::base::SpaceInformationPtr& si, const std::string& name,
                                     const ModelBasedPlanningContextSpecification& spec)>
    ConfiguredPlannerAllocator;
typedef std::function<ConfiguredPlannerAllocator(const std::string& planner_type)> ConfiguredPlannerSelector;
 
struct ModelBasedPlanningContextSpecification
{
  std::map<std::string, std::string> config_;
  ConfiguredPlannerSelector planner_selector_;
  constraint_samplers::ConstraintSamplerManagerPtr constraint_sampler_manager_;
 
  ModelBasedStateSpacePtr state_space_;
  og::SimpleSetupPtr ompl_simple_setup_;  // pass in the correct simple setup type
};
 
class ModelBasedPlanningContext : public planning_interface::PlanningContext
{
public:
  ModelBasedPlanningContext(const std::string& name, const ModelBasedPlanningContextSpecification& spec);
 
  ~ModelBasedPlanningContext() override
  {
  }
 
  bool solve(planning_interface::MotionPlanResponse& res) override;
  bool solve(planning_interface::MotionPlanDetailedResponse& res) override;
 
  void clear() override;
  bool terminate() override;
 
  const ModelBasedPlanningContextSpecification& getSpecification() const
  {
    return spec_;
  }
 
  const std::map<std::string, std::string>& getSpecificationConfig() const
  {
    return spec_.config_;
  }
 
  void setSpecificationConfig(const std::map<std::string, std::string>& config)
  {
    spec_.config_ = config;
  }
 
  const moveit::core::RobotModelConstPtr& getRobotModel() const
  {
    return spec_.state_space_->getRobotModel();
  }
 
  const moveit::core::JointModelGroup* getJointModelGroup() const
  {
    return spec_.state_space_->getJointModelGroup();
  }
 
  const moveit::core::RobotState& getCompleteInitialRobotState() const
  {
    return complete_initial_robot_state_;
  }
 
  const ModelBasedStateSpacePtr& getOMPLStateSpace() const
  {
    return spec_.state_space_;
  }
 
  const og::SimpleSetupPtr& getOMPLSimpleSetup() const
  {
    return ompl_simple_setup_;
  }
 
  og::SimpleSetupPtr& getOMPLSimpleSetup()
  {
    return ompl_simple_setup_;
  }
 
  const ot::Benchmark& getOMPLBenchmark() const
  {
    return ompl_benchmark_;
  }
 
  ot::Benchmark& getOMPLBenchmark()
  {
    return ompl_benchmark_;
  }
 
  const kinematic_constraints::KinematicConstraintSetPtr& getPathConstraints() const
  {
    return path_constraints_;
  }
 
  /* \brief Get the maximum number of sampling attempts allowed when sampling states is needed */
  unsigned int getMaximumStateSamplingAttempts() const
  {
    return max_state_sampling_attempts_;
  }
 
  /* \brief Set the maximum number of sampling attempts allowed when sampling states is needed */
  void setMaximumStateSamplingAttempts(unsigned int max_state_sampling_attempts)
  {
    max_state_sampling_attempts_ = max_state_sampling_attempts;
  }
 
  /* \brief Get the maximum number of sampling attempts allowed when sampling goals is needed */
  unsigned int getMaximumGoalSamplingAttempts() const
  {
    return max_goal_sampling_attempts_;
  }
 
  /* \brief Set the maximum number of sampling attempts allowed when sampling goals is needed */
  void setMaximumGoalSamplingAttempts(unsigned int max_goal_sampling_attempts)
  {
    max_goal_sampling_attempts_ = max_goal_sampling_attempts;
  }
 
  /* \brief Get the maximum number of valid goal samples to store */
  unsigned int getMaximumGoalSamples() const
  {
    return max_goal_samples_;
  }
 
  /* \brief Set the maximum number of valid goal samples to store */
  void setMaximumGoalSamples(unsigned int max_goal_samples)
  {
    max_goal_samples_ = max_goal_samples;
  }
 
  /* \brief Get the maximum number of planning threads allowed */
  unsigned int getMaximumPlanningThreads() const
  {
    return max_planning_threads_;
  }
 
  /* \brief Set the maximum number of planning threads */
  void setMaximumPlanningThreads(unsigned int max_planning_threads)
  {
    max_planning_threads_ = max_planning_threads;
  }
 
  /* \brief Get the maximum solution segment length */
  double getMaximumSolutionSegmentLength() const
  {
    return max_solution_segment_length_;
  }
 
  /* \brief Set the maximum solution segment length */
  void setMaximumSolutionSegmentLength(double mssl)
  {
    max_solution_segment_length_ = mssl;
  }
 
  unsigned int getMinimumWaypointCount() const
  {
    return minimum_waypoint_count_;
  }
 
  void setMinimumWaypointCount(unsigned int mwc)
  {
    minimum_waypoint_count_ = mwc;
  }
 
  const constraint_samplers::ConstraintSamplerManagerPtr& getConstraintSamplerManager()
  {
    return spec_.constraint_sampler_manager_;
  }
 
  void setConstraintSamplerManager(const constraint_samplers::ConstraintSamplerManagerPtr& csm)
  {
    spec_.constraint_sampler_manager_ = csm;
  }
 
  void setVerboseStateValidityChecks(bool flag);
 
  void setProjectionEvaluator(const std::string& peval);
 
  void setPlanningVolume(const moveit_msgs::WorkspaceParameters& wparams);
 
  void setCompleteInitialState(const moveit::core::RobotState& complete_initial_robot_state);
 
  bool setGoalConstraints(const std::vector<moveit_msgs::Constraints>& goal_constraints,
                          const moveit_msgs::Constraints& path_constraints, moveit_msgs::MoveItErrorCodes* error);
  bool setPathConstraints(const moveit_msgs::Constraints& path_constraints, moveit_msgs::MoveItErrorCodes* error);
 
  void setConstraintsApproximations(const ConstraintsLibraryPtr& constraints_library)
  {
    constraints_library_ = constraints_library;
  }
 
  ConstraintsLibraryPtr getConstraintsLibraryNonConst()
  {
    return constraints_library_;
  }
 
  const ConstraintsLibraryPtr& getConstraintsLibrary() const
  {
    return constraints_library_;
  }
 
  bool simplifySolutions() const
  {
    return simplify_solutions_;
  }
 
  void simplifySolutions(bool flag)
  {
    simplify_solutions_ = flag;
  }
 
  void setInterpolation(bool flag)
  {
    interpolate_ = flag;
  }
 
  void setHybridize(bool flag)
  {
    hybridize_ = flag;
  }
 
  /* @brief Solve the planning problem. Return true if the problem is solved
     @param timeout The time to spend on solving
     @param count The number of runs to combine the paths of, in an attempt to generate better quality paths
  */
  const moveit_msgs::MoveItErrorCodes solve(double timeout, unsigned int count);
 
  /* @brief Benchmark the planning problem. Return true on successful saving of benchmark results
     @param timeout The time to spend on solving
     @param count The number of runs to average in the computation of the benchmark
     @param filename The name of the file to which the benchmark results are to be saved (automatic names can be
     provided if a name is not specified)
  */
  bool benchmark(double timeout, unsigned int count, const std::string& filename = "");
 
  /* @brief Get the amount of time spent computing the last plan */
  double getLastPlanTime() const
  {
    return last_plan_time_;
  }
 
  /* @brief Get the amount of time spent simplifying the last plan */
  double getLastSimplifyTime() const
  {
    return last_simplify_time_;
  }
 
  /* @brief Apply smoothing and try to simplify the plan
     @param timeout The amount of time allowed to be spent on simplifying the plan*/
  void simplifySolution(double timeout);
 
  /* @brief Interpolate the solution*/
  void interpolateSolution();
 
  /* @brief Get the solution as a RobotTrajectory object*/
  bool getSolutionPath(robot_trajectory::RobotTrajectory& traj) const;
 
  void convertPath(const og::PathGeometric& pg, robot_trajectory::RobotTrajectory& traj) const;
 
  bool loadConstraintApproximations(const ros::NodeHandle& nh);
 
  bool saveConstraintApproximations(const ros::NodeHandle& nh);
 
  virtual void configure(const ros::NodeHandle& nh, bool use_constraints_approximations);
 
protected:
  void preSolve();
  void postSolve();
 
  void startSampling();
  void stopSampling();
 
  virtual ob::ProjectionEvaluatorPtr getProjectionEvaluator(const std::string& peval) const;
  virtual ob::StateSamplerPtr allocPathConstrainedSampler(const ompl::base::StateSpace* ss) const;
  virtual void useConfig();
  virtual ob::GoalPtr constructGoal();
 
  /* @brief Construct a planner termination condition, by default a simple time limit
     @param timeout The maximum time (in seconds) that can be used for planning
     @param start The point in time from which planning is considered to have started
 
     An additional planner termination condition can be specified per planner
     configuration in ompl_planning.yaml via the `termination_condition` parameter.
     Possible values are:
 
     * `Iteration[num]`: Terminate after `num` iterations. Here, `num` should be replaced
       with a positive integer.
     * `CostConvergence[solutions_window,epsilon]`: Terminate after the cost (as specified
       by an optimization objective) has converged. The parameter `solutions_window`
       specifies the minimum number of solutions to use in deciding whether a planner has
       converged. The parameter `epsilon`       is the threshold to consider for convergence.
       This should be a positive number close to 0. If the cumulative moving average does
       not change by a relative fraction of epsilon after a new better solution is found,
       convergence has been reached.
     * `ExactSolution`: Terminate as soon as an exact solution is found or a timeout
       occurs. This modifies the behavior of anytime/optimizing planners to terminate
       upon discovering the first feasible solution.
 
     In all cases, the planner will terminate when either the user-specified termination
     condition is satisfied or the time limit specified by `timeout` has been reached,
     whichever occurs first.
  */
  virtual ob::PlannerTerminationCondition constructPlannerTerminationCondition(double timeout,
                                                                               const ompl::time::point& start);
 
  void registerTerminationCondition(const ob::PlannerTerminationCondition& ptc);
  void unregisterTerminationCondition();
 
  int32_t errorCode(const ompl::base::PlannerStatus& status);
 
  ModelBasedPlanningContextSpecification spec_;
 
  moveit::core::RobotState complete_initial_robot_state_;
 
  og::SimpleSetupPtr ompl_simple_setup_;
 
  ot::Benchmark ompl_benchmark_;
 
  ot::ParallelPlan ompl_parallel_plan_;
 
  std::vector<int> space_signature_;
 
  kinematic_constraints::KinematicConstraintSetPtr path_constraints_;
  moveit_msgs::Constraints path_constraints_msg_;
  std::vector<kinematic_constraints::KinematicConstraintSetPtr> goal_constraints_;
 
  const ob::PlannerTerminationCondition* ptc_;
  std::mutex ptc_lock_;
 
  double last_plan_time_;
 
  double last_simplify_time_;
 
  unsigned int max_goal_samples_;
 
  unsigned int max_state_sampling_attempts_;
 
  unsigned int max_goal_sampling_attempts_;
 
  unsigned int max_planning_threads_;
 
  double max_solution_segment_length_;
 
  unsigned int minimum_waypoint_count_;
 
  bool multi_query_planning_enabled_;
 
  ConstraintsLibraryPtr constraints_library_;
 
  bool simplify_solutions_;
 
  // if false the final solution is not interpolated
  bool interpolate_;
 
  // if false parallel plan returns the first solution found
  bool hybridize_;
};
}  // namespace ompl_interface