obstacles_critic.cpp

// Copyright (c) 2022 Samsung Research America, @artofnothingness Alexey Budyakov
// Copyright (c) 2023 Open Navigation LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
 
#include <cmath>
#include "nav2_mppi_controller/critics/obstacles_critic.hpp"
#include "nav2_costmap_2d/inflation_layer.hpp"
#include "nav2_core/controller_exceptions.hpp"
 
namespace mppi::critics
{
 
void ObstaclesCritic::initialize()
{
  auto getParam = parameters_handler_->getParamGetter(name_);
  getParam(consider_footprint_, "consider_footprint", false);
  getParam(power_, "cost_power", 1);
  getParam(repulsion_weight_, "repulsion_weight", 1.5f);
  getParam(critical_weight_, "critical_weight", 20.0f);
  getParam(collision_cost_, "collision_cost", 100000.0f);
  getParam(collision_margin_distance_, "collision_margin_distance", 0.10f);
  getParam(near_goal_distance_, "near_goal_distance", 0.5f);
  getParam(inflation_layer_name_, "inflation_layer_name", std::string(""));
 
  collision_checker_.setCostmap(costmap_);
  possible_collision_cost_ = findCircumscribedCost(costmap_ros_);
 
  if (possible_collision_cost_ < 1.0f) {
    RCLCPP_ERROR(
      logger_,
      "Inflation layer either not found or inflation is not set sufficiently for "
      "optimized non-circular collision checking capabilities. It is HIGHLY recommended to set"
      " the inflation radius to be at MINIMUM half of the robot's largest cross-section. See "
      "github.com/ros-planning/navigation2/tree/main/nav2_smac_planner#potential-fields"
      " for full instructions. This will substantially impact run-time performance.");
  }
 
  if (costmap_ros_->getUseRadius() == consider_footprint_) {
    RCLCPP_WARN(
    logger_,
    "Inconsistent configuration in collision checking. Please verify the robot's shape settings "
    "in both the costmap and the obstacle critic.");
    if (costmap_ros_->getUseRadius()) {
      throw nav2_core::ControllerException(
      "Considering footprint in collision checking but no robot footprint provided in the "
      "costmap.");
    }
  }
 
  RCLCPP_INFO(
    logger_,
    "ObstaclesCritic instantiated with %d power and %f / %f weights. "
    "Critic will collision check based on %s cost.",
    power_, critical_weight_, repulsion_weight_, consider_footprint_ ?
    "footprint" : "circular");
}
 
float ObstaclesCritic::findCircumscribedCost(
  std::shared_ptr<nav2_costmap_2d::Costmap2DROS> costmap)
{
  double result = -1.0;
  const double circum_radius = costmap->getLayeredCostmap()->getCircumscribedRadius();
  if (static_cast<float>(circum_radius) == circumscribed_radius_) {
    // early return if footprint size is unchanged
    return circumscribed_cost_;
  }
 
  // check if the costmap has an inflation layer
  const auto inflation_layer = nav2_costmap_2d::InflationLayer::getInflationLayer(
    costmap,
    inflation_layer_name_);
  if (inflation_layer != nullptr) {
    const double resolution = costmap->getCostmap()->getResolution();
    result = inflation_layer->computeCost(circum_radius / resolution);
    inflation_scale_factor_ = static_cast<float>(inflation_layer->getCostScalingFactor());
    inflation_radius_ = static_cast<float>(inflation_layer->getInflationRadius());
  } else {
    RCLCPP_WARN(
      logger_,
      "No inflation layer found in costmap configuration. "
      "If this is an SE2-collision checking plugin, it cannot use costmap potential "
      "field to speed up collision checking by only checking the full footprint "
      "when robot is within possibly-inscribed radius of an obstacle. This may "
      "significantly slow down planning times and not avoid anything but absolute collisions!");
  }
 
  circumscribed_radius_ = static_cast<float>(circum_radius);
  circumscribed_cost_ = static_cast<float>(result);
 
  return circumscribed_cost_;
}
 
float ObstaclesCritic::distanceToObstacle(const CollisionCost & cost)
{
  const float scale_factor = inflation_scale_factor_;
  const float min_radius = costmap_ros_->getLayeredCostmap()->getInscribedRadius();
  float dist_to_obj = (scale_factor * min_radius - log(cost.cost) + log(253.0f)) / scale_factor;
 
  // If not footprint collision checking, the cost is using the center point cost and
  // needs the radius subtracted to obtain the closest distance to the object
  if (!cost.using_footprint) {
    dist_to_obj -= min_radius;
  }
 
  return dist_to_obj;
}
 
void ObstaclesCritic::score(CriticData & data)
{
  using xt::evaluation_strategy::immediate;
  if (!enabled_) {
    return;
  }
 
  if (consider_footprint_) {
    // footprint may have changed since initialization if user has dynamic footprints
    possible_collision_cost_ = findCircumscribedCost(costmap_ros_);
  }
 
  // If near the goal, don't apply the preferential term since the goal is near obstacles
  bool near_goal = false;
  if (utils::withinPositionGoalTolerance(near_goal_distance_, data.state.pose.pose, data.goal)) {
    near_goal = true;
  }
 
  auto && raw_cost = xt::xtensor<float, 1>::from_shape({data.costs.shape(0)});
  auto && repulsive_cost = xt::xtensor<float, 1>::from_shape({data.costs.shape(0)});
 
  const size_t traj_len = data.trajectories.x.shape(1);
  bool all_trajectories_collide = true;
  for (size_t i = 0; i < data.trajectories.x.shape(0); ++i) {
    bool trajectory_collide = false;
    float traj_cost = 0.0f;
    const auto & traj = data.trajectories;
    CollisionCost pose_cost;
    raw_cost[i] = 0.0f;
    repulsive_cost[i] = 0.0f;
 
    for (size_t j = 0; j < traj_len; j++) {
      pose_cost = costAtPose(traj.x(i, j), traj.y(i, j), traj.yaws(i, j));
      if (pose_cost.cost < 1.0f) {continue;}  // In free space
 
      if (inCollision(pose_cost.cost)) {
        trajectory_collide = true;
        break;
      }
 
      // Cannot process repulsion if inflation layer does not exist
      if (inflation_radius_ == 0.0f || inflation_scale_factor_ == 0.0f) {
        continue;
      }
 
      const float dist_to_obj = distanceToObstacle(pose_cost);
 
      // Let near-collision trajectory points be punished severely
      if (dist_to_obj < collision_margin_distance_) {
        traj_cost += (collision_margin_distance_ - dist_to_obj);
      }
 
      // Generally prefer trajectories further from obstacles
      if (!near_goal) {
        repulsive_cost[i] += inflation_radius_ - dist_to_obj;
      }
    }
 
    if (!trajectory_collide) {all_trajectories_collide = false;}
    raw_cost[i] = trajectory_collide ? collision_cost_ : traj_cost;
  }
 
  // Normalize repulsive cost by trajectory length & lowest score to not overweight importance
  // This is a preferential cost, not collision cost, to be tuned relative to desired behaviors
  auto && repulsive_cost_normalized =
    (repulsive_cost - xt::amin(repulsive_cost, immediate)) / traj_len;
 
  if (power_ > 1u) {
    data.costs += xt::pow(
      (critical_weight_ * raw_cost) +
      (repulsion_weight_ * repulsive_cost_normalized),
      power_);
  } else {
    data.costs += (critical_weight_ * raw_cost) +
      (repulsion_weight_ * repulsive_cost_normalized);
  }
 
  data.fail_flag = all_trajectories_collide;
}
 
bool ObstaclesCritic::inCollision(float cost) const
{
  bool is_tracking_unknown =
    costmap_ros_->getLayeredCostmap()->isTrackingUnknown();
 
  using namespace nav2_costmap_2d; // NOLINT
  switch (static_cast<unsigned char>(cost)) {
    case (LETHAL_OBSTACLE):
      return true;
    case (INSCRIBED_INFLATED_OBSTACLE):
      return consider_footprint_ ? false : true;
    case (NO_INFORMATION):
      return is_tracking_unknown ? false : true;
  }
 
  return false;
}
 
CollisionCost ObstaclesCritic::costAtPose(float x, float y, float theta)
{
  CollisionCost collision_cost;
  float & cost = collision_cost.cost;
  collision_cost.using_footprint = false;
  unsigned int x_i, y_i;
  if (!collision_checker_.worldToMap(x, y, x_i, y_i)) {
    cost = nav2_costmap_2d::NO_INFORMATION;
    return collision_cost;
  }
  cost = collision_checker_.pointCost(x_i, y_i);
 
  if (consider_footprint_ &&
    (cost >= possible_collision_cost_ || possible_collision_cost_ < 1.0f))
  {
    cost = static_cast<float>(collision_checker_.footprintCostAtPose(
        x, y, theta, costmap_ros_->getRobotFootprint()));
    collision_cost.using_footprint = true;
  }
 
  return collision_cost;
}
 
}  // namespace mppi::critics
 
#include <pluginlib/class_list_macros.hpp>
 
PLUGINLIB_EXPORT_CLASS(
  mppi::critics::ObstaclesCritic,
  mppi::critics::CriticFunction)