constraint_critic.cpp

// Copyright (c) 2022 Samsung Research America, @artofnothingness Alexey Budyakov
//
// 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 "nav2_mppi_controller/critics/constraint_critic.hpp"
 
namespace mppi::critics
{
 
void ConstraintCritic::initialize()
{
  auto getParam = parameters_handler_->getParamGetter(name_);
  auto getParentParam = parameters_handler_->getParamGetter(parent_name_);
 
  getParam(power_, "cost_power", 1);
  getParam(weight_, "cost_weight", 4.0f);
  RCLCPP_INFO(
    logger_, "ConstraintCritic instantiated with %d power and %f weight.",
    power_, weight_);
 
  float vx_max, vy_max, vx_min;
  getParentParam(vx_max, "vx_max", 0.5f);
  getParentParam(vy_max, "vy_max", 0.0f);
  getParentParam(vx_min, "vx_min", -0.35f);
 
  const float min_sgn = vx_min > 0.0f ? 1.0f : -1.0f;
  max_vel_ = sqrtf(vx_max * vx_max + vy_max * vy_max);
  min_vel_ = min_sgn * sqrtf(vx_min * vx_min + vy_max * vy_max);
}
 
void ConstraintCritic::score(CriticData & data)
{
  using xt::evaluation_strategy::immediate;
 
  if (!enabled_) {
    return;
  }
 
  // Differential motion model
  auto diff = dynamic_cast<DiffDriveMotionModel *>(data.motion_model.get());
  if (diff != nullptr) {
    if (power_ > 1u) {
      data.costs += xt::pow(
        xt::sum(
          (std::move(
            xt::maximum(data.state.vx - max_vel_, 0.0f) +
            xt::maximum(min_vel_ - data.state.vx, 0.0f))) *
          data.model_dt, {1}, immediate) * weight_, power_);
    } else {
      data.costs += xt::sum(
        (std::move(
          xt::maximum(data.state.vx - max_vel_, 0.0f) +
          xt::maximum(min_vel_ - data.state.vx, 0.0f))) *
        data.model_dt, {1}, immediate) * weight_;
    }
    return;
  }
 
  // Omnidirectional motion model
  auto omni = dynamic_cast<OmniMotionModel *>(data.motion_model.get());
  if (omni != nullptr) {
    auto sgn = xt::eval(xt::where(data.state.vx > 0.0f, 1.0f, -1.0f));
    auto vel_total = sgn * xt::hypot(data.state.vx, data.state.vy);
    if (power_ > 1u) {
      data.costs += xt::pow(
        xt::sum(
          (std::move(
            xt::maximum(vel_total - max_vel_, 0.0f) +
            xt::maximum(min_vel_ - vel_total, 0.0f))) *
          data.model_dt, {1}, immediate) * weight_, power_);
    } else {
      data.costs += xt::sum(
        (std::move(
          xt::maximum(vel_total - max_vel_, 0.0f) +
          xt::maximum(min_vel_ - vel_total, 0.0f))) *
        data.model_dt, {1}, immediate) * weight_;
    }
    return;
  }
 
  // Ackermann motion model
  auto acker = dynamic_cast<AckermannMotionModel *>(data.motion_model.get());
  if (acker != nullptr) {
    auto & vx = data.state.vx;
    auto & wz = data.state.wz;
    auto out_of_turning_rad_motion = xt::maximum(
      acker->getMinTurningRadius() - (xt::fabs(vx) / xt::fabs(wz)), 0.0f);
    if (power_ > 1u) {
      data.costs += xt::pow(
        xt::sum(
          (std::move(
            xt::maximum(data.state.vx - max_vel_, 0.0f) +
            xt::maximum(min_vel_ - data.state.vx, 0.0f) + out_of_turning_rad_motion)) *
          data.model_dt, {1}, immediate) * weight_, power_);
    } else {
      data.costs += xt::sum(
        (std::move(
          xt::maximum(data.state.vx - max_vel_, 0.0f) +
          xt::maximum(min_vel_ - data.state.vx, 0.0f) + out_of_turning_rad_motion)) *
        data.model_dt, {1}, immediate) * weight_;
    }
    return;
  }
}
 
}  // namespace mppi::critics
 
#include <pluginlib/class_list_macros.hpp>
 
PLUGINLIB_EXPORT_CLASS(mppi::critics::ConstraintCritic, mppi::critics::CriticFunction)