min_turning_r_ getting param fix

nav2_mppi_controller/include/nav2_mppi_controller/motion_models.hpp

@@ -140,9 +140,9 @@ class AckermannMotionModel : public MotionModel
   /**
     * @brief Constructor for mppi::AckermannMotionModel
     */
-  explicit AckermannMotionModel(ParametersHandler * param_handler)
+  explicit AckermannMotionModel(ParametersHandler * param_handler, const std::string & name)
   {
-    auto getParam = param_handler->getParamGetter("AckermannConstraints");
+    auto getParam = param_handler->getParamGetter(name + ".AckermannConstraints");
     getParam(min_turning_r_, "min_turning_r", 0.2);
   }
 

nav2_mppi_controller/src/optimizer.cpp

@@ -442,7 +442,7 @@ void Optimizer::setMotionModel(const std::string & model)
   } else if (model == "Omni") {
     motion_model_ = std::make_shared<OmniMotionModel>();
   } else if (model == "Ackermann") {
-    motion_model_ = std::make_shared<AckermannMotionModel>(parameters_handler_);
+    motion_model_ = std::make_shared<AckermannMotionModel>(parameters_handler_, name_);
   } else {
     throw nav2_core::ControllerException(
             std::string(

nav2_mppi_controller/test/critics_tests.cpp

@@ -116,7 +116,7 @@ TEST(CriticTests, ConstraintsCritic)
   // Now with ackermann, all in constraint so no costs to score
   state.vx = 0.40 * xt::ones<float>({1000, 30});
   state.wz = 1.5 * xt::ones<float>({1000, 30});
-  data.motion_model = std::make_shared<AckermannMotionModel>(&param_handler);
+  data.motion_model = std::make_shared<AckermannMotionModel>(&param_handler, node->get_name());
   critic.score(data);
   EXPECT_NEAR(xt::sum(costs, immediate)(), 0, 1e-6);
 

nav2_mppi_controller/test/motion_model_tests.cpp

@@ -136,7 +136,7 @@ TEST(MotionModelTests, AckermannTest)
   auto node = std::make_shared<rclcpp_lifecycle::LifecycleNode>("my_node");
   ParametersHandler param_handler(node);
   std::unique_ptr<AckermannMotionModel> model =
-    std::make_unique<AckermannMotionModel>(&param_handler);
+    std::make_unique<AckermannMotionModel>(&param_handler, node->get_name());
 
   // Check that predict properly populates the trajectory velocities with the control velocities
   state.cvx = 10 * xt::ones<float>({batches, timesteps});