Tune localizer, remove error states
Tuning constants are from whatever I was running yesterday.
More substantially, I removed the voltage error and angular error states
(during the day I had just zeroed them out on every iteration, but
actually removing them reduces the dimensionality of the EKF, which is
nice). When I looked at the log streamer when we were running the robot,
it just looked like the voltage error terms were oscillating a bit
around zero, suggesting that they were just being counterproductive.
Change-Id: I9744c4808edf3a43ae1c76d022460ee1d4c9ed3e
diff --git a/frc971/control_loops/drivetrain/BUILD b/frc971/control_loops/drivetrain/BUILD
index a4d2ca6..d3ed728 100644
--- a/frc971/control_loops/drivetrain/BUILD
+++ b/frc971/control_loops/drivetrain/BUILD
@@ -46,6 +46,7 @@
deps = [
":drivetrain_config",
"//aos/containers:priority_queue",
+ "//aos/util:math",
"//frc971/control_loops:c2d",
"//frc971/control_loops:runge_kutta",
"//third_party/eigen",
diff --git a/frc971/control_loops/drivetrain/hybrid_ekf.h b/frc971/control_loops/drivetrain/hybrid_ekf.h
index 82f409c..119386a 100644
--- a/frc971/control_loops/drivetrain/hybrid_ekf.h
+++ b/frc971/control_loops/drivetrain/hybrid_ekf.h
@@ -4,6 +4,7 @@
#include <chrono>
#include "aos/containers/priority_queue.h"
+#include "aos/util/math.h"
#include "frc971/control_loops/c2d.h"
#include "frc971/control_loops/runge_kutta.h"
#include "Eigen/Dense"
@@ -48,11 +49,8 @@
kLeftVelocity = 4,
kRightEncoder = 5,
kRightVelocity = 6,
- kLeftVoltageError = 7,
- kRightVoltageError = 8 ,
- kAngularError = 9,
};
- static constexpr int kNStates = 10;
+ static constexpr int kNStates = 7;
static constexpr int kNInputs = 2;
// Number of previous samples to save.
static constexpr int kSaveSamples = 50;
@@ -70,19 +68,11 @@
// variable-size measurement updates.
typedef Eigen::Matrix<Scalar, kNOutputs, 1> Output;
typedef Eigen::Matrix<Scalar, kNStates, kNStates> StateSquare;
- // State is [x_position, y_position, theta, Kalman States], where
- // Kalman States are the states from the standard drivetrain Kalman Filter,
- // which is: [left encoder, left ground vel, right encoder, right ground vel,
- // left voltage error, right voltage error, angular_error], where:
- // left/right encoder should correspond directly to encoder readings
- // left/right velocities are the velocity of the left/right sides over the
+ // State is [x_position, y_position, theta, left encoder, left ground vel,
+ // right encoder, right ground vel]. left/right encoder should correspond
+ // directly to encoder readings left/right velocities are the velocity of the
+ // left/right sides over the
// ground (i.e., corrected for angular_error).
- // voltage errors are the difference between commanded and effective voltage,
- // used to estimate consistent modelling errors (e.g., friction).
- // angular error is the difference between the angular velocity as estimated
- // by the encoders vs. estimated by the gyro, such as might be caused by
- // wheels on one side of the drivetrain being too small or one side's
- // wheels slipping more than the other.
typedef Eigen::Matrix<Scalar, kNStates, 1> State;
// Constructs a HybridEkf for a particular drivetrain.
@@ -406,9 +396,7 @@
// Encoder derivatives
A_continuous_(kLeftEncoder, kLeftVelocity) = 1.0;
- A_continuous_(kLeftEncoder, kAngularError) = 1.0;
A_continuous_(kRightEncoder, kRightVelocity) = 1.0;
- A_continuous_(kRightEncoder, kAngularError) = -1.0;
// Pull velocity derivatives from velocity matrices.
// Note that this looks really awkward (doesn't use
@@ -425,21 +413,22 @@
B_continuous_.setZero();
B_continuous_.row(kLeftVelocity) = vel_coefs.B_continuous.row(0);
B_continuous_.row(kRightVelocity) = vel_coefs.B_continuous.row(1);
- A_continuous_.template block<kNStates, kNInputs>(0, 7) = B_continuous_;
Q_continuous_.setZero();
// TODO(james): Improve estimates of process noise--e.g., X/Y noise can
// probably be reduced when we are stopped because you rarely jump randomly.
// Or maybe it's more appropriate to scale wheelspeed noise with wheelspeed,
// since the wheels aren't likely to slip much stopped.
- Q_continuous_(kX, kX) = 0.005;
- Q_continuous_(kY, kY) = 0.005;
- Q_continuous_(kTheta, kTheta) = 0.001;
- Q_continuous_.template block<7, 7>(3, 3) =
- dt_config_.make_kf_drivetrain_loop().observer().coefficients().Q;
+ Q_continuous_(kX, kX) = 0.01;
+ Q_continuous_(kY, kY) = 0.01;
+ Q_continuous_(kTheta, kTheta) = 0.0002;
+ Q_continuous_(kLeftEncoder, kLeftEncoder) = ::std::pow(0.03, 2.0);
+ Q_continuous_(kRightEncoder, kRightEncoder) = ::std::pow(0.03, 2.0);
+ Q_continuous_(kLeftVelocity, kLeftVelocity) = ::std::pow(0.1, 2.0);
+ Q_continuous_(kRightVelocity, kRightVelocity) = ::std::pow(0.1, 2.0);
P_.setZero();
- P_.diagonal() << 0.1, 0.1, 0.01, 0.02, 0.01, 0.02, 0.01, 1, 1, 0.03;
+ P_.diagonal() << 0.1, 0.1, 0.01, 0.02, 0.01, 0.02, 0.01;
H_encoders_and_gyro_.setZero();
// Encoders are stored directly in the state matrix, so are a minor
diff --git a/frc971/control_loops/drivetrain/hybrid_ekf_test.cc b/frc971/control_loops/drivetrain/hybrid_ekf_test.cc
index 27119b1..1702ec4 100644
--- a/frc971/control_loops/drivetrain/hybrid_ekf_test.cc
+++ b/frc971/control_loops/drivetrain/hybrid_ekf_test.cc
@@ -52,22 +52,16 @@
EXPECT_EQ(Xdot_ekf(StateIdx::kX, 0), ctheta * (left_vel + right_vel) / 2.0);
EXPECT_EQ(Xdot_ekf(StateIdx::kY, 0), stheta * (left_vel + right_vel) / 2.0);
EXPECT_EQ(Xdot_ekf(StateIdx::kTheta, 0), (right_vel - left_vel) / diameter);
- EXPECT_EQ(Xdot_ekf(StateIdx::kLeftEncoder, 0),
- left_vel + X(StateIdx::kAngularError, 0));
- EXPECT_EQ(Xdot_ekf(StateIdx::kRightEncoder, 0),
- right_vel - X(StateIdx::kAngularError, 0));
+ EXPECT_EQ(Xdot_ekf(StateIdx::kLeftEncoder, 0), left_vel);
+ EXPECT_EQ(Xdot_ekf(StateIdx::kRightEncoder, 0), right_vel);
Eigen::Matrix<double, 2, 1> vel_x(X(StateIdx::kLeftVelocity, 0),
X(StateIdx::kRightVelocity, 0));
Eigen::Matrix<double, 2, 1> expected_vel_X =
velocity_plant_coefs_.A_continuous * vel_x +
- velocity_plant_coefs_.B_continuous *
- (U + X.middleRows<2>(StateIdx::kLeftVoltageError));
+ velocity_plant_coefs_.B_continuous * U;
EXPECT_EQ(Xdot_ekf(StateIdx::kLeftVelocity, 0), expected_vel_X(0, 0));
EXPECT_EQ(Xdot_ekf(StateIdx::kRightVelocity, 0), expected_vel_X(1, 0));
-
- // Dynamics don't expect error terms to change:
- EXPECT_EQ(0.0, Xdot_ekf.bottomRows<3>().squaredNorm());
}
State DiffEq(const State &X, const Input &U) {
return ekf_.DiffEq(X, U);
@@ -93,18 +87,14 @@
CheckDiffEq(State::Zero(), Input::Zero());
CheckDiffEq(State::Zero(), {-5.0, 5.0});
CheckDiffEq(State::Zero(), {12.0, -3.0});
- CheckDiffEq((State() << 100.0, 200.0, M_PI, 1.234, 0.5, 1.2, 0.6, 3.0, -4.0,
- 0.3).finished(),
+ CheckDiffEq((State() << 100.0, 200.0, M_PI, 1.234, 0.5, 1.2, 0.6).finished(),
{5.0, 6.0});
- CheckDiffEq((State() << 100.0, 200.0, 2.0, 1.234, 0.5, 1.2, 0.6, 3.0, -4.0,
- 0.3).finished(),
+ CheckDiffEq((State() << 100.0, 200.0, 2.0, 1.234, 0.5, 1.2, 0.6).finished(),
{5.0, 6.0});
- CheckDiffEq((State() << 100.0, 200.0, -2.0, 1.234, 0.5, 1.2, 0.6, 3.0, -4.0,
- 0.3).finished(),
+ CheckDiffEq((State() << 100.0, 200.0, -2.0, 1.234, 0.5, 1.2, 0.6).finished(),
{5.0, 6.0});
// And check that a theta outisde of [-M_PI, M_PI] works.
- CheckDiffEq((State() << 100.0, 200.0, 200.0, 1.234, 0.5, 1.2, 0.6, 3.0, -4.0,
- 0.3).finished(),
+ CheckDiffEq((State() << 100.0, 200.0, 200.0, 1.234, 0.5, 1.2, 0.6).finished(),
{5.0, 6.0});
}
@@ -112,7 +102,7 @@
// with zero change in time, the state should approach the estimation.
TEST_F(HybridEkfTest, ZeroTimeCorrect) {
HybridEkf<>::Output Z(0.5, 0.5, 1);
- Eigen::Matrix<double, 3, 10> H;
+ Eigen::Matrix<double, 3, 7> H;
H.setIdentity();
auto h = [H](const State &X, const Input &) { return H * X; };
auto dhdx = [H](const State &) { return H; };
@@ -140,7 +130,7 @@
HybridEkf<>::Output Z(0, 0, 0);
// Use true_X to track what we think the true robot state is.
State true_X = ekf_.X_hat();
- Eigen::Matrix<double, 3, 10> H;
+ Eigen::Matrix<double, 3, 7> H;
H.setZero();
auto h = [H](const State &X, const Input &) { return H * X; };
auto dhdx = [H](const State &) { return H; };
@@ -171,9 +161,6 @@
EXPECT_NEAR(ekf_.X_hat(StateIdx::kLeftVelocity) * 0.8,
ekf_.X_hat(StateIdx::kRightVelocity),
ekf_.X_hat(StateIdx::kLeftVelocity) * 0.1);
- EXPECT_EQ(0.0, ekf_.X_hat(StateIdx::kLeftVoltageError));
- EXPECT_EQ(0.0, ekf_.X_hat(StateIdx::kRightVoltageError));
- EXPECT_EQ(0.0, ekf_.X_hat(StateIdx::kAngularError));
const double ending_p_norm = ekf_.P().norm();
// Due to lack of corrections, noise should've increased.
EXPECT_GT(ending_p_norm, starting_p_norm * 1.10);
@@ -193,7 +180,7 @@
TEST_P(HybridEkfOldCorrectionsTest, CreateOldCorrection) {
HybridEkf<>::Output Z;
Z.setZero();
- Eigen::Matrix<double, 3, 10> H;
+ Eigen::Matrix<double, 3, 7> H;
H.setZero();
auto h_zero = [H](const State &X, const Input &) { return H * X; };
auto dhdx_zero = [H](const State &) { return H; };
@@ -231,7 +218,7 @@
expected_X_hat(0, 0) = Z(0, 0);
expected_X_hat(1, 0) = Z(1, 0) + modeled_X_hat(0, 0);
expected_X_hat(2, 0) = Z(2, 0);
- EXPECT_LT((expected_X_hat.topRows<7>() - ekf_.X_hat().topRows<7>()).norm(),
+ EXPECT_LT((expected_X_hat - ekf_.X_hat()).norm(),
1e-3)
<< "X_hat: " << ekf_.X_hat() << " expected " << expected_X_hat;
// The covariance after the predictions but before the corrections should
@@ -249,7 +236,7 @@
TEST_F(HybridEkfTest, DiscardTooOldCorrection) {
HybridEkf<>::Output Z;
Z.setZero();
- Eigen::Matrix<double, 3, 10> H;
+ Eigen::Matrix<double, 3, 7> H;
H.setZero();
auto h_zero = [H](const State &X, const Input &) { return H * X; };
auto dhdx_zero = [H](const State &) { return H; };
@@ -304,11 +291,11 @@
}
// Tests that encoder updates cause everything to converge properly in the
-// presence of voltage error.
+// presence of an initial velocity error.
TEST_F(HybridEkfTest, PerfectEncoderUpdatesWithVoltageError) {
State true_X = ekf_.X_hat();
- true_X(StateIdx::kLeftVoltageError, 0) = 2.0;
- true_X(StateIdx::kRightVoltageError, 0) = 2.0;
+ true_X(StateIdx::kLeftVelocity, 0) = 0.2;
+ true_X(StateIdx::kRightVelocity, 0) = 0.2;
Input U(5.0, 5.0);
for (int ii = 0; ii < 1000; ++ii) {
true_X = Update(true_X, U);
@@ -328,11 +315,11 @@
// Tests encoder/gyro updates when we have some errors in our estimate.
TEST_F(HybridEkfTest, PerfectEncoderUpdateConverges) {
- // In order to simulate modelling errors, we add an angular_error and start
- // the encoder values slightly off.
+ // In order to simulate modelling errors, we start the encoder values slightly
+ // off.
State true_X = ekf_.X_hat();
- true_X(StateIdx::kAngularError, 0) = 1.0;
true_X(StateIdx::kLeftEncoder, 0) += 2.0;
+ true_X(StateIdx::kLeftVelocity, 0) = 0.1;
true_X(StateIdx::kRightEncoder, 0) -= 2.0;
// After enough time, everything should converge to near-perfect (if there
// were any errors in the original absolute state (x/y/theta) state, then we
@@ -350,7 +337,7 @@
dt_config_.robot_radius / 2.0,
U, t0_ + (ii + 1) * dt_config_.dt);
}
- EXPECT_NEAR((true_X - ekf_.X_hat()).norm(), 0.0, 1e-5)
+ EXPECT_NEAR((true_X - ekf_.X_hat()).norm(), 0.0, 1e-4)
<< "Expected non-x/y estimates to converge to correct. "
"Estimated X_hat:\n"
<< ekf_.X_hat() << "\ntrue X:\n"
@@ -359,11 +346,11 @@
// Tests encoder/gyro updates in a realistic-ish scenario with noise:
TEST_F(HybridEkfTest, RealisticEncoderUpdateConverges) {
- // In order to simulate modelling errors, we add an angular_error and start
- // the encoder values slightly off.
+ // In order to simulate modelling errors, we start the encoder values slightly
+ // off.
State true_X = ekf_.X_hat();
- true_X(StateIdx::kAngularError, 0) = 1.0;
true_X(StateIdx::kLeftEncoder, 0) += 2.0;
+ true_X(StateIdx::kLeftVelocity, 0) = 0.1;
true_X(StateIdx::kRightEncoder, 0) -= 2.0;
Input U(10.0, 5.0);
for (int ii = 0; ii < 100; ++ii) {
@@ -377,7 +364,7 @@
U, t0_ + (ii + 1) * dt_config_.dt);
}
EXPECT_NEAR(
- (true_X.bottomRows<9>() - ekf_.X_hat().bottomRows<9>()).squaredNorm(),
+ (true_X.bottomRows<6>() - ekf_.X_hat().bottomRows<6>()).squaredNorm(),
0.0, 2e-3)
<< "Expected non-x/y estimates to converge to correct. "
"Estimated X_hat:\n" << ekf_.X_hat() << "\ntrue X:\n" << true_X;
@@ -411,7 +398,7 @@
// Check that we die when only one of h and dhdx are provided:
EXPECT_DEATH(ekf_.Correct({1, 2, 3}, &U, {}, {},
[](const State &) {
- return Eigen::Matrix<double, 3, 10>::Zero();
+ return Eigen::Matrix<double, 3, 7>::Zero();
},
{}, t0_ + ::std::chrono::seconds(1)),
"make_h");
diff --git a/frc971/control_loops/drivetrain/localizer.h b/frc971/control_loops/drivetrain/localizer.h
index af07089..f0d81cb 100644
--- a/frc971/control_loops/drivetrain/localizer.h
+++ b/frc971/control_loops/drivetrain/localizer.h
@@ -95,9 +95,12 @@
}
void ResetPosition(double x, double y, double theta) override {
+ const double left_encoder = ekf_.X_hat(StateIdx::kLeftEncoder);
+ const double right_encoder = ekf_.X_hat(StateIdx::kRightEncoder);
ekf_.ResetInitialState(
::aos::monotonic_clock::now(),
- (Ekf::State() << x, y, theta, 0, 0, 0, 0, 0, 0, 0).finished(),
+ (Ekf::State() << x, y, theta, left_encoder, 0, right_encoder, 0)
+ .finished(),
ekf_.P());
};
@@ -110,12 +113,8 @@
double right_velocity() const override {
return ekf_.X_hat(StateIdx::kRightVelocity);
}
- double left_voltage_error() const override {
- return ekf_.X_hat(StateIdx::kLeftVoltageError);
- }
- double right_voltage_error() const override {
- return ekf_.X_hat(StateIdx::kRightVoltageError);
- }
+ double left_voltage_error() const override { return 0.0; }
+ double right_voltage_error() const override { return 0.0; }
TrivialTargetSelector *target_selector() override {
return &target_selector_;