chomp_planner.cpp

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/* Author: E. Gil Jones */
 
#include <chomp_motion_planner/chomp_optimizer.hpp>
#include <chomp_motion_planner/chomp_planner.hpp>
#include <chomp_motion_planner/chomp_trajectory.hpp>
#include <moveit/robot_state/conversions.hpp>
// TODO: Remove conditional include when released to all active distros.
#if __has_include(<tf2/LinearMath/Quaternion.hpp>)
#include <tf2/LinearMath/Quaternion.hpp>
#else
#include <tf2/LinearMath/Quaternion.h>
#endif
#include <tf2_eigen/tf2_eigen.hpp>
#include <tf2_geometry_msgs/tf2_geometry_msgs.hpp>
#include <moveit/utils/logger.hpp>
 
#include <chrono>
 
namespace chomp
{
namespace
{
rclcpp::Logger getLogger()
{
  return moveit::getLogger("moveit.planners.chomp.planner");
}
}  // namespace
 
void ChompPlanner::solve(const planning_scene::PlanningSceneConstPtr& planning_scene,
                         const planning_interface::MotionPlanRequest& req, const ChompParameters& params,
                         planning_interface::MotionPlanDetailedResponse& res) const
{
  auto start_time = std::chrono::system_clock::now();
  res.planner_id = std::string("chomp");
  if (!planning_scene)
  {
    RCLCPP_ERROR(getLogger(), "No planning scene initialized.");
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::FAILURE;
    return;
  }
 
  // get the specified start state
  moveit::core::RobotState start_state = planning_scene->getCurrentState();
  moveit::core::robotStateMsgToRobotState(planning_scene->getTransforms(), req.start_state, start_state);
 
  if (!start_state.satisfiesBounds())
  {
    RCLCPP_ERROR(getLogger(), "Start state violates joint limits");
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_ROBOT_STATE;
    return;
  }
 
  ChompTrajectory trajectory(planning_scene->getRobotModel(), 3.0, .03, req.group_name);
  robotStateToArray(start_state, req.group_name, trajectory.getTrajectoryPoint(0));
 
  if (req.goal_constraints.size() != 1)
  {
    RCLCPP_ERROR(getLogger(), "Expecting exactly one goal constraint, got: %zd", req.goal_constraints.size());
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_GOAL_CONSTRAINTS;
    return;
  }
 
  if (req.goal_constraints[0].joint_constraints.empty() || !req.goal_constraints[0].position_constraints.empty() ||
      !req.goal_constraints[0].orientation_constraints.empty())
  {
    RCLCPP_ERROR(getLogger(), "Only joint-space goals are supported");
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_GOAL_CONSTRAINTS;
    return;
  }
 
  const size_t goal_index = trajectory.getNumPoints() - 1;
  moveit::core::RobotState goal_state(start_state);
  for (const moveit_msgs::msg::JointConstraint& joint_constraint : req.goal_constraints[0].joint_constraints)
    goal_state.setVariablePosition(joint_constraint.joint_name, joint_constraint.position);
  if (!goal_state.satisfiesBounds())
  {
    RCLCPP_ERROR(getLogger(), "Goal state violates joint limits");
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_ROBOT_STATE;
    return;
  }
  robotStateToArray(goal_state, req.group_name, trajectory.getTrajectoryPoint(goal_index));
 
  const moveit::core::JointModelGroup* model_group =
      planning_scene->getRobotModel()->getJointModelGroup(req.group_name);
  // fix the goal to move the shortest angular distance for wrap-around joints:
  for (size_t i = 0; i < model_group->getActiveJointModels().size(); ++i)
  {
    const moveit::core::JointModel* model = model_group->getActiveJointModels()[i];
    const moveit::core::RevoluteJointModel* revolute_joint =
        dynamic_cast<const moveit::core::RevoluteJointModel*>(model);
 
    if (revolute_joint != nullptr)
    {
      if (revolute_joint->isContinuous())
      {
        double start = (trajectory)(0, i);
        double end = (trajectory)(goal_index, i);
        RCLCPP_INFO(getLogger(), "Start is %f end %f short %f", start, end, shortestAngularDistance(start, end));
        (trajectory)(goal_index, i) = start + shortestAngularDistance(start, end);
      }
    }
  }
 
  // fill in an initial trajectory based on user choice from the chomp_config.yaml file
  if (params.trajectory_initialization_method_.compare("quintic-spline") == 0)
  {
    trajectory.fillInMinJerk();
  }
  else if (params.trajectory_initialization_method_.compare("linear") == 0)
  {
    trajectory.fillInLinearInterpolation();
  }
  else if (params.trajectory_initialization_method_.compare("cubic") == 0)
  {
    trajectory.fillInCubicInterpolation();
  }
  else if (params.trajectory_initialization_method_.compare("fillTrajectory") == 0)
  {
    if (res.trajectory.empty())
    {
      RCLCPP_ERROR(getLogger(), "No input trajectory specified");
      return;
    }
    else if (!(trajectory.fillInFromTrajectory(*res.trajectory[0])))
    {
      RCLCPP_ERROR(getLogger(), "Input trajectory has less than 2 points, "
                                "trajectory must contain at least start and goal state");
      return;
    }
  }
  else
  {
    RCLCPP_ERROR(getLogger(), "invalid interpolation method specified in the chomp_planner file");
    return;
  }
 
  RCLCPP_INFO(getLogger(), "CHOMP trajectory initialized using method: %s ",
              (params.trajectory_initialization_method_).c_str());
 
  // optimize!
  auto create_time = std::chrono::system_clock::now();
 
  int replan_count = 0;
  bool replan_flag = false;
  double org_learning_rate = 0.04, org_ridge_factor = 0.0, org_planning_time_limit = 10;
  int org_max_iterations = 200;
 
  // storing the initial chomp parameters values
  org_learning_rate = params.learning_rate_;
  org_ridge_factor = params.ridge_factor_;
  org_planning_time_limit = params.planning_time_limit_;
  org_max_iterations = params.max_iterations_;
 
  std::unique_ptr<ChompOptimizer> optimizer;
 
  // create a non_const_params variable which stores the non constant version of the const params variable
  ChompParameters params_nonconst = params;
 
  // while loop for replanning (recovery behaviour) if collision free optimized solution not found
  while (true)
  {
    if (replan_flag)
    {
      // increase learning rate in hope to find a successful path; increase ridge factor to avoid obstacles; add 5
      // additional secs in hope to find a solution; increase maximum iterations
      params_nonconst.setRecoveryParams(params_nonconst.learning_rate_ + 0.02, params_nonconst.ridge_factor_ + 0.002,
                                        params_nonconst.planning_time_limit_ + 5, params_nonconst.max_iterations_ + 50);
    }
 
    // initialize a ChompOptimizer object to load up the optimizer with default parameters or with updated parameters in
    // case of a recovery behaviour
    optimizer =
        std::make_unique<ChompOptimizer>(&trajectory, planning_scene, req.group_name, &params_nonconst, start_state);
    if (!optimizer->isInitialized())
    {
      RCLCPP_ERROR(getLogger(), "Could not initialize optimizer");
      res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::PLANNING_FAILED;
      return;
    }
 
    RCLCPP_DEBUG(getLogger(), "Optimization took %ld sec to create",
                 (std::chrono::system_clock::now() - create_time).count());
 
    bool optimization_result = optimizer->optimize();
 
    // replan with updated parameters if no solution is found
    if (params_nonconst.enable_failure_recovery_)
    {
      RCLCPP_INFO(getLogger(),
                  "Planned with Chomp Parameters (learning_rate, ridge_factor, "
                  "planning_time_limit, max_iterations), attempt: # %d ",
                  (replan_count + 1));
      RCLCPP_INFO(getLogger(), "Learning rate: %f ridge factor: %f planning time limit: %f max_iterations %d ",
                  params_nonconst.learning_rate_, params_nonconst.ridge_factor_, params_nonconst.planning_time_limit_,
                  params_nonconst.max_iterations_);
 
      if (!optimization_result && replan_count < params_nonconst.max_recovery_attempts_)
      {
        replan_count++;
        replan_flag = true;
      }
      else
      {
        break;
      }
    }
    else
      break;
  }  // end of while loop
 
  // resetting the CHOMP Parameters to the original values after a successful plan
  params_nonconst.setRecoveryParams(org_learning_rate, org_ridge_factor, org_planning_time_limit, org_max_iterations);
 
  RCLCPP_DEBUG(getLogger(), "Optimization actually took %ld sec to run",
               (std::chrono::system_clock::now() - create_time).count());
  create_time = std::chrono::system_clock::now();
  // assume that the trajectory is now optimized, fill in the output structure:
 
  RCLCPP_DEBUG(getLogger(), "Output trajectory has %zd joints", trajectory.getNumJoints());
 
  auto result = std::make_shared<robot_trajectory::RobotTrajectory>(planning_scene->getRobotModel(), req.group_name);
  // fill in the entire trajectory
  for (size_t i = 0; i < trajectory.getNumPoints(); ++i)
  {
    const Eigen::MatrixXd::RowXpr source = trajectory.getTrajectoryPoint(i);
    auto state = std::make_shared<moveit::core::RobotState>(start_state);
    size_t joint_index = 0;
    for (const moveit::core::JointModel* jm : result->getGroup()->getActiveJointModels())
    {
      assert(jm->getVariableCount() == 1);
      state->setVariablePosition(jm->getFirstVariableIndex(), source[joint_index++]);
    }
    result->addSuffixWayPoint(state, 0.0);
  }
 
  res.trajectory.resize(1);
  res.trajectory[0] = result;
  res.description.resize(1);
  res.description[0] = "plan";
 
  RCLCPP_DEBUG(getLogger(), "Bottom took %ld sec to create", (std::chrono::system_clock::now() - create_time).count());
  RCLCPP_DEBUG(getLogger(), "Serviced planning request in %ld wall-seconds",
               (std::chrono::system_clock::now() - start_time).count());
 
  res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::SUCCESS;
  res.processing_time.resize(1);
  res.processing_time[0] = std::chrono::duration<double>(std::chrono::system_clock::now() - start_time).count();
 
  // report planning failure if path has collisions
  if (!optimizer->isCollisionFree())
  {
    RCLCPP_ERROR(getLogger(), "Motion plan is invalid.");
    res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_MOTION_PLAN;
    return;
  }
 
  // check that final state is within goal tolerances
  kinematic_constraints::JointConstraint jc(planning_scene->getRobotModel());
  const moveit::core::RobotState& last_state = result->getLastWayPoint();
  for (const moveit_msgs::msg::JointConstraint& constraint : req.goal_constraints[0].joint_constraints)
  {
    if (!jc.configure(constraint) || !jc.decide(last_state).satisfied)
    {
      RCLCPP_ERROR(getLogger(), "Goal constraints are violated: %s", constraint.joint_name.c_str());
      res.error_code.val = moveit_msgs::msg::MoveItErrorCodes::GOAL_CONSTRAINTS_VIOLATED;
      return;
    }
  }
 
  res.processing_time.resize(1);
  res.processing_time[0] = std::chrono::duration<double>(std::chrono::system_clock::now() - start_time).count();
}
 
}  // namespace chomp