y2022/control_loops/localizer/localizer.h

#ifndef Y2022_CONTROL_LOOPS_LOCALIZER_LOCALIZER_H_
#define Y2022_CONTROL_LOOPS_LOCALIZER_LOCALIZER_H_

#include "Eigen/Dense"
#include "Eigen/Geometry"

#include "aos/events/event_loop.h"
#include "aos/containers/ring_buffer.h"
#include "aos/time/time.h"
#include "y2020/vision/sift/sift_generated.h"
#include "y2022/control_loops/localizer/localizer_status_generated.h"
#include "y2022/control_loops/localizer/localizer_output_generated.h"
#include "frc971/control_loops/drivetrain/improved_down_estimator.h"
#include "frc971/control_loops/drivetrain/drivetrain_position_generated.h"
#include "frc971/control_loops/drivetrain/drivetrain_output_generated.h"
#include "frc971/control_loops/drivetrain/localizer_generated.h"
#include "frc971/zeroing/imu_zeroer.h"

namespace frc971::controls {

namespace testing {
class LocalizerTest;
}

// Localizer implementation that makes use of a 6-axis IMU, encoder readings,
// drivetrain voltages, and camera returns to localize the robot. Meant to
// be run on a raspberry pi.
//
// This operates on the principle that the drivetrain can be in one of two
// modes:
// 1) A "normal" mode where it is obeying the regular drivetrain model, with
//    minimal lateral motion and no major external disturbances. This is
//    referred to as the "model" mode in the code/variable names.
// 2) An non-holonomic mode where the robot is just flying around on a 2-D
//    plane with no meaningful constraints (referred to as an "accel" model
//    in the code, because we rely primarily on the accelerometer readings).
//
// In order to determine which mode to be in, we attempt to track whether the
// two models are diverging substantially. In order to do this, we maintain a
// 1-second long queue of "branches". A branch is generated every X iterations
// and contains a model state and an accel state. When the branch starts, the
// two will have identical states. For the remaining 1 second, the model state
// will evolve purely according to the drivetrian model, and the accel state
// will evolve purely using IMU readings.
//
// When the branch reaches 1 second in age, we calculate a residual associated
// with how much the accel and model based states diverged. If they have
// diverged substantially, that implies that the model is a poor match for
// whatever has been happening to the robot in the past second, so if we were
// previously relying on the model, we will override the current "actual"
// state with the branched accel state, and then continue to update the accel
// state based on IMU readings.
// If we are currently in the accel state, we will continue generating branches
// until the branches stop diverging--this will indicate that the model
// matches the accelerometer readings again, and so we will swap back to
// the model-based state.
//
// TODO:
// * Implement paying attention to camera readings.
// * Tune for ADIS16505/real robot.
// * Tune down CPU usage to run on a pi.
class ModelBasedLocalizer {
 public:
  static constexpr size_t kX = 0;
  static constexpr size_t kY = 1;
  static constexpr size_t kTheta = 2;

  static constexpr size_t kVelocityX = 3;
  static constexpr size_t kVelocityY = 4;
  static constexpr size_t kNAccelStates = 5;

  static constexpr size_t kLeftEncoder = 3;
  static constexpr size_t kLeftVelocity = 4;
  static constexpr size_t kLeftVoltageError = 5;
  static constexpr size_t kRightEncoder = 6;
  static constexpr size_t kRightVelocity = 7;
  static constexpr size_t kRightVoltageError = 8;
  static constexpr size_t kNModelStates = 9;

  static constexpr size_t kNModelOutputs = 3;

  static constexpr size_t kNAccelOuputs = 1;

  static constexpr size_t kAccelX = 0;
  static constexpr size_t kAccelY = 1;
  static constexpr size_t kThetaRate = 2;
  static constexpr size_t kNAccelInputs = 3;

  static constexpr size_t kLeftVoltage = 0;
  static constexpr size_t kRightVoltage = 1;
  static constexpr size_t kNModelInputs = 2;

  typedef Eigen::Matrix<double, kNModelStates, 1> ModelState;
  typedef Eigen::Matrix<double, kNAccelStates, 1> AccelState;
  typedef Eigen::Matrix<double, kNModelInputs, 1> ModelInput;
  typedef Eigen::Matrix<double, kNAccelInputs, 1> AccelInput;

  ModelBasedLocalizer(
      const control_loops::drivetrain::DrivetrainConfig<double> &dt_config);
  void HandleImu(aos::monotonic_clock::time_point t,
                 const Eigen::Vector3d &gyro, const Eigen::Vector3d &accel,
                 const Eigen::Vector2d encoders, const Eigen::Vector2d voltage);
  void HandleImageMatch(aos::monotonic_clock::time_point,
                        const vision::sift::ImageMatchResult *) {
    LOG(FATAL) << "Unimplemented.";
  }
  void HandleReset(aos::monotonic_clock::time_point,
                   const Eigen::Vector3d &xytheta);

  flatbuffers::Offset<ModelBasedStatus> PopulateStatus(
      flatbuffers::FlatBufferBuilder *fbb);

  Eigen::Vector3d xytheta() const {
    if (using_model_) {
      return current_state_.model_state.block<3, 1>(0, 0);
    } else {
      return current_state_.accel_state.block<3, 1>(0, 0);
    }
  }

  AccelState accel_state() const { return current_state_.accel_state; };

 private:
  struct CombinedState {
    AccelState accel_state;
    ModelState model_state;
    aos::monotonic_clock::time_point branch_time;
    double accumulated_divergence;
  };

  static flatbuffers::Offset<AccelBasedState> BuildAccelState(
      flatbuffers::FlatBufferBuilder *fbb, const AccelState &state);

  static flatbuffers::Offset<ModelBasedState> BuildModelState(
      flatbuffers::FlatBufferBuilder *fbb, const ModelState &state);

  Eigen::Matrix<double, kNModelStates, kNModelStates> AModel(
      const ModelState &state) const;
  Eigen::Matrix<double, kNAccelStates, kNAccelStates> AAccel() const;
  ModelState DiffModel(const ModelState &state, const ModelInput &U) const;
  AccelState DiffAccel(const AccelState &state, const AccelInput &U) const;

  ModelState UpdateModel(const ModelState &model, const ModelInput &input,
                         aos::monotonic_clock::duration dt) const;
  AccelState UpdateAccel(const AccelState &accel, const AccelInput &input,
                         aos::monotonic_clock::duration dt) const;

  AccelState AccelStateForModelState(const ModelState &state) const;
  ModelState ModelStateForAccelState(const AccelState &state,
                                     const Eigen::Vector2d &encoders,
                                     const double yaw_rate) const;
  double ModelDivergence(const CombinedState &state,
                         const AccelInput &accel_inputs,
                         const Eigen::Vector2d &filtered_accel,
                         const ModelInput &model_inputs);

  const control_loops::drivetrain::DrivetrainConfig<double> dt_config_;
  const StateFeedbackHybridPlantCoefficients<2, 2, 2, double>
      velocity_drivetrain_coefficients_;
  Eigen::Matrix<double, kNModelStates, kNModelStates> A_continuous_model_;
  Eigen::Matrix<double, kNAccelStates, kNAccelStates> A_continuous_accel_;
  Eigen::Matrix<double, kNModelStates, kNModelInputs> B_continuous_model_;
  Eigen::Matrix<double, kNAccelStates, kNAccelInputs> B_continuous_accel_;

  Eigen::Matrix<double, kNModelStates, kNModelStates> Q_continuous_model_;

  Eigen::Matrix<double, kNModelStates, kNModelStates> P_model_;
  // When we are following the model, we will, on each iteration:
  // 1) Perform a model-based update of a single state.
  // 2) Add a hypothetical non-model-based entry based on the current state.
  // 3) Evict too-old non-model-based entries.
  control_loops::drivetrain::DrivetrainUkf down_estimator_;

  // Buffer of old branches these are all created by initializing a new
  // model-based state based on the current state, and then initializing a new
  // accel-based state on top of that new model-based state (to eliminate the
  // impact of any lateral motion).
  // We then integrate up all of these states and observe how much the model and
  // accel based states of each branch compare to one another.
  aos::RingBuffer<CombinedState, 2000> branches_;

  CombinedState current_state_;
  aos::monotonic_clock::time_point t_ = aos::monotonic_clock::min_time;
  bool using_model_;

  double last_residual_ = 0.0;
  double filtered_residual_ = 0.0;
  Eigen::Vector2d filtered_residual_accel_ = Eigen::Vector2d::Zero();
  Eigen::Vector3d abs_accel_ = Eigen::Vector3d::Zero();
  double velocity_residual_ = 0.0;
  double accel_residual_ = 0.0;
  double theta_rate_residual_ = 0.0;
  int hysteresis_count_ = 0;

  int clock_resets_ = 0;

  friend class testing::LocalizerTest;
};

class EventLoopLocalizer {
 public:
  EventLoopLocalizer(
      aos::EventLoop *event_loop,
      const control_loops::drivetrain::DrivetrainConfig<double> &dt_config);

 private:
  aos::EventLoop *event_loop_;
  ModelBasedLocalizer model_based_;
  aos::Sender<LocalizerStatus> status_sender_;
  aos::Sender<LocalizerOutput> output_sender_;
  aos::Fetcher<frc971::control_loops::drivetrain::Position> position_fetcher_;
  aos::Fetcher<frc971::control_loops::drivetrain::Output> output_fetcher_;
  zeroing::ImuZeroer zeroer_;
  aos::monotonic_clock::time_point last_output_send_ =
      aos::monotonic_clock::min_time;
};
}  // namespace frc971::controls
#endif // Y2022_CONTROL_LOOPS_LOCALIZER_LOCALIZER_H_