From 97efd0d4267e29c7c2875ee8e30d89c6733d38c9 Mon Sep 17 00:00:00 2001 From: Rooholla-KhorramBakht Date: Tue, 30 Apr 2024 22:32:27 -0400 Subject: [PATCH] go2py bridge bug fix --- Go2Py/robot/interface/dds.py | 2 +- Go2Py/robot/interface/ros2.py | 75 ++- Go2Py/robot/model.py | 10 +- deploy/robot_ws/src/go2py_node/src/bridge.cpp | 19 +- examples/compliance_test.ipynb | 460 ++++++++++++++++++ .../go2_impact_test2_250.0.pkl | Bin 0 -> 988310 bytes .../go2_impact_test2_350.0.pkl | Bin 0 -> 1653034 bytes .../go2_impact_test2_450.0.pkl | Bin 0 -> 1793754 bytes .../go2_impact_test2_550.0.pkl | Bin 0 -> 1166450 bytes .../go2_impact_test2_650.0.pkl | Bin 0 -> 979062 bytes .../compliance-test/go2_impact_test2_650.pkl | Bin 0 -> 688054 bytes examples/go2_rotation.pkl | Bin 0 -> 1480313 bytes examples/go2_walking.pkl | Bin 0 -> 1480313 bytes examples/legged_inertial_ekf.ipynb | 192 +++++++- examples/lowlevel_ros2_interface.ipynb | 104 ++-- examples/vicon2gt-dataset-collector.ipynb | 140 ++++++ examples/vicon2gt.pkl | Bin 0 -> 728525 bytes examples/vicon2gt_1.pkl | Bin 0 -> 728525 bytes examples/vicon2gt_3.pkl | Bin 0 -> 728525 bytes examples/vicon2gt_recorder.py | 4 + 20 files changed, 920 insertions(+), 86 deletions(-) create mode 100644 examples/compliance_test.ipynb create mode 100644 examples/data/compliance-test/go2_impact_test2_250.0.pkl create mode 100644 examples/data/compliance-test/go2_impact_test2_350.0.pkl create mode 100644 examples/data/compliance-test/go2_impact_test2_450.0.pkl create mode 100644 examples/data/compliance-test/go2_impact_test2_550.0.pkl create mode 100644 examples/data/compliance-test/go2_impact_test2_650.0.pkl create mode 100644 examples/data/compliance-test/go2_impact_test2_650.pkl create mode 100644 examples/go2_rotation.pkl create mode 100644 examples/go2_walking.pkl create mode 100644 examples/vicon2gt-dataset-collector.ipynb create mode 100644 examples/vicon2gt.pkl create mode 100644 examples/vicon2gt_1.pkl create mode 100644 examples/vicon2gt_3.pkl create mode 100644 examples/vicon2gt_recorder.py diff --git a/Go2Py/robot/interface/dds.py b/Go2Py/robot/interface/dds.py index 7306c48..b811ba9 100644 --- a/Go2Py/robot/interface/dds.py +++ b/Go2Py/robot/interface/dds.py @@ -72,7 +72,7 @@ class GO2Real(): Retrieve the state of the robot """ while self.running: - for msg in self.lowstate_reader.take_iter(timeout=duration(milliseconds=100.)): + for msg in self.lowstate_reader.take_iter(timeout=duration(milliseconds=1.)): self.state = msg def getIMU(self): diff --git a/Go2Py/robot/interface/ros2.py b/Go2Py/robot/interface/ros2.py index bb989d5..b0ecec1 100644 --- a/Go2Py/robot/interface/ros2.py +++ b/Go2Py/robot/interface/ros2.py @@ -15,6 +15,9 @@ from geometry_msgs.msg import TwistStamped from unitree_go.msg import LowState, Go2pyLowCmd from nav_msgs.msg import Odometry from scipy.spatial.transform import Rotation +import tf2_ros +import numpy as np +from scipy.spatial.transform import Rotation as R @@ -142,6 +145,7 @@ class GO2Real(Node): quat = self.state.imu_state.quaternion rpy = self.state.imu_state.rpy temp = self.state.imu_state.temperature + # return accel, gyro, quat, temp return {'accel':accel, 'gyro':gyro, 'quat':quat, "rpy":rpy, 'temp':temp} def getFootContacts(self): @@ -153,13 +157,16 @@ class GO2Real(Node): """Returns the joint angles (q) and velocities (dq) of the robot""" if self.state is None: return None - motorStates = self.state.motor_state - _q, _dq = zip( - *[(motorState.q, motorState.dq) for motorState in motorStates[:12]] - ) - q, dq = np.array(_q), np.array(_dq) - - return {'q':q, 'dq':dq} + motor_state = np.array([[self.state.motor_state[i].q, + self.state.motor_state[i].dq, + self.state.motor_state[i].ddq, + self.state.motor_state[i].tau_est, + self.state.motor_state[i].temperature] for i in range(12)]) + return {'q':motor_state[:,0], + 'dq':motor_state[:,1], + 'ddq':motor_state[:,2], + 'tau_est':motor_state[:,3], + 'temperature':motor_state[:,4]} def getRemoteState(self): """A method to get the state of the wireless remote control. @@ -224,13 +231,13 @@ class GO2Real(Node): self.highcmd_publisher.publish(self.highcmd) def setCommandsLow(self, q_des, dq_des, kp, kd, tau_ff): - assert q_des.size == dq_des.size == kp.size == kd.size == tau_ff.size == 12, "q, dq, kp, kd, tau_ff should have size 12" + # assert q_des.size == dq_des.size == kp.size == kd.size == tau_ff.size == 12, "q, dq, kp, kd, tau_ff should have size 12" lowcmd = Go2pyLowCmd() - lowcmd.q = q_des.tolist() - lowcmd.dq = dq_des.tolist() - lowcmd.kp = kp.tolist() - lowcmd.kd = kd.tolist() - lowcmd.tau = tau_ff.tolist() + lowcmd.q = q_des + lowcmd.dq = dq_des + lowcmd.kp = kp + lowcmd.kd = kd + lowcmd.tau = tau_ff self.lowcmd_publisher.publish(lowcmd) self.latest_command_stamp = time.time() @@ -263,4 +270,44 @@ class GO2Real(Node): q = self.getIMU()['quat'] R = Rotation.from_quat([q[1], q[2], q[3], q[0]]).as_matrix() g_in_body = R.T@np.array([0.0, 0.0, -1.0]).reshape(3, 1) - return g_in_body \ No newline at end of file + return g_in_body + +class ROS2TFInterface(Node): + + def __init__(self, parent_name, child_name, node_name): + super().__init__(f'{node_name}_tf2_listener') + self.parent_name = parent_name + self.child_name = child_name + self.tfBuffer = tf2_ros.Buffer() + self.listener = tf2_ros.TransformListener(self.tfBuffer, self) + self.T = None + self.stamp = None + self.running = True + self.thread = threading.Thread(target=self.update_loop) + self.thread.start() + self.trans = None + + def update_loop(self): + while self.running: + try: + self.trans = self.tfBuffer.lookup_transform(self.parent_name, self.child_name, rclpy.time.Time(), rclpy.time.Duration(seconds=0.1)) + except (tf2_ros.LookupException, tf2_ros.ConnectivityException, tf2_ros.ExtrapolationException) as e: + pass + time.sleep(0.01) + + def get_pose(self): + if self.trans is None: + return None + else: + translation = [self.trans.transform.translation.x, self.trans.transform.translation.y, self.trans.transform.translation.z] + rotation = [self.trans.transform.rotation.x, self.trans.transform.rotation.y, self.trans.transform.rotation.z, self.trans.transform.rotation.w] + self.T = np.eye(4) + self.T[0:3, 0:3] = R.from_quat(rotation).as_matrix() + self.T[:3, 3] = translation + self.stamp = self.trans.header.stamp.nanosec * 1e-9 + self.trans.header.stamp.sec + return self.T + + def close(self): + self.running = False + self.thread.join() + self.destroy_node() \ No newline at end of file diff --git a/Go2Py/robot/model.py b/Go2Py/robot/model.py index 1903b6c..2385f26 100644 --- a/Go2Py/robot/model.py +++ b/Go2Py/robot/model.py @@ -178,12 +178,12 @@ class Go2Model: q (np.ndarray): A numpy array of size 19 representing the [x, y, z, qx, qy, qz, qw] and joint configurations in FR, FL, RR, RL order. dq (np.ndarray): A numpy array of size 18 representing the [vx, vy, vz, wx, wy, wz] and joint configurations in FR, FL, RR, RL order. """ - self.robot.centroidalMomentum(q_,dq_) - self.nle_ = self.robot.nle(q_, dq_)[self.dq_reordering_idx] - self.g_ = self.robot.gravity(q_)[self.dq_reordering_idx] - self.M_ = self.robot.mass(q_)[self.dq_reordering_idx,:] + self.robot.centroidalMomentum(q,dq) + self.nle_ = self.robot.nle(q, dq)[self.dq_reordering_idx] + self.g_ = self.robot.gravity(q)[self.dq_reordering_idx] + self.M_ = self.robot.mass(q)[self.dq_reordering_idx,:] self.M_ = self.M_[:,self.dq_reordering_idx] - self.Minv_ = pin.computeMinverse(self.robot.model, self.robot.data, q_)[self.dq_reordering_idx,:] + self.Minv_ = pin.computeMinverse(self.robot.model, self.robot.data, q)[self.dq_reordering_idx,:] self.Minv_ = self.Minv_[:,self.dq_reordering_idx] diff --git a/deploy/robot_ws/src/go2py_node/src/bridge.cpp b/deploy/robot_ws/src/go2py_node/src/bridge.cpp index cc1f1fd..f1ff7ce 100644 --- a/deploy/robot_ws/src/go2py_node/src/bridge.cpp +++ b/deploy/robot_ws/src/go2py_node/src/bridge.cpp @@ -40,22 +40,22 @@ class Custom: public rclcpp::Node // Go2 highlevel subscriber and publishers // the state_suber is set to subscribe "sportmodestate" topic - highstate_suber = this->create_subscription( - "sportmodestate", 10, std::bind(&Custom::highstate_callback, this, std::placeholders::_1)); + // highstate_suber = this->create_subscription( + // "sportmodestate", 10, std::bind(&Custom::highstate_callback, this, std::placeholders::_1)); // the req_puber is set to subscribe "/api/sport/request" topic with dt - highreq_puber = this->create_publisher("/api/sport/request", 10); + highreq_puber = this->create_publisher("/api/sport/request", 1); //Go2 lowlevel interface init_lowcmd(); - lowstate_suber = this->create_subscription( - "lowstate", 1, std::bind(&Custom::lowstate_callback, this, std::placeholders::_1)); + // lowstate_suber = this->create_subscription( + // "lowstate", 1, std::bind(&Custom::lowstate_callback, this, std::placeholders::_1)); lowcmd_suber = this->create_subscription( "/go2/lowcmd", 1, std::bind(&Custom::lowcmd_callback, this, std::placeholders::_1)); - lowcmd_puber = this->create_publisher("/lowcmd", 10); - api_publisher = this->create_publisher("/api/robot_state/request", 10); - status_publisher = this->create_publisher("/go2py/status", 10); + lowcmd_puber = this->create_publisher("/lowcmd", 1); + api_publisher = this->create_publisher("/api/robot_state/request", 1); + status_publisher = this->create_publisher("/go2py/status", 1); } private: @@ -311,7 +311,6 @@ void Custom::lowcmd_callback(unitree_go::msg::Go2pyLowCmd::SharedPtr data) lowcmd_msg.motor_cmd[i].kd = data->kd[i]; // Poinstion(rad) control kd gain lowcmd_msg.motor_cmd[i].tau = data->tau[i]; // Feedforward toque 1N.m get_crc(lowcmd_msg); //Compute the CRC and load it into the message - lowcmd_puber->publish(lowcmd_msg); //Publish lowcmd message } }else { @@ -323,9 +322,9 @@ void Custom::lowcmd_callback(unitree_go::msg::Go2pyLowCmd::SharedPtr data) lowcmd_msg.motor_cmd[i].kd = 0; // Poinstion(rad) control kd gain lowcmd_msg.motor_cmd[i].tau = 0.; // Feedforward toque 1N.m get_crc(lowcmd_msg); //Compute the CRC and load it into the message - lowcmd_puber->publish(lowcmd_msg); //Publish lowcmd message } } + lowcmd_puber->publish(lowcmd_msg); //Publish lowcmd message } void Custom::watchdog() diff --git a/examples/compliance_test.ipynb b/examples/compliance_test.ipynb new file mode 100644 index 0000000..4ea0895 --- /dev/null +++ b/examples/compliance_test.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pygame 2.5.2 (SDL 2.28.2, Python 3.8.18)\n", + "Hello from the pygame community. https://www.pygame.org/contribute.html\n" + ] + } + ], + "source": [ + "from Go2Py.robot.interface.ros2 import GO2Real, ros2_init, ROS2ExecutorManager\n", + "import time\n", + "ros2_init()\n", + "robot = GO2Real(mode='lowlevel')\n", + "ros2_exec_manager = ROS2ExecutorManager()\n", + "ros2_exec_manager.add_node(robot)\n", + "ros2_exec_manager.start()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from Go2Py.robot.model import Go2Model\n", + "model = Go2Model()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'q': array([ 0.19625151, 0.62121832, -1.45901692, -0.8040427 , 0.15673077,\n", + " -1.30231106, 0.76652366, 0.2306025 , -1.40467536, -0.80055571,\n", + " 0.02943856, -1.08567142]),\n", + " 'dq': array([ 0.10076362, 0. , -0.00202201, 0.03875524, -0.01162657,\n", + " -0.0161761 , 0.01937762, 0.00775105, 0.00808805, 0.05425733,\n", + " 0. , -0.03639622]),\n", + " 'ddq': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n", + " 'tau_est': array([-0.14842969, 0.04947656, -0.23707521, -0.14842969, -0.12369141,\n", + " 0.28449023, 0. , 0. , -0.04741504, 0.02473828,\n", + " -0.02473828, 0.04741504]),\n", + " 'temperature': array([35., 31., 30., 32., 30., 29., 35., 30., 29., 35., 30., 29.])}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "robot.getJointStates()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import socket\n", + "import struct\n", + "import threading\n", + "import time\n", + "class AtiFTSensor:\n", + " def __init__(self):\n", + " self.initialized_ = False\n", + " self.going_ = False\n", + " self.streaming_ = False\n", + " self.F_bias_ = [0.0] * 3\n", + " self.T_bias_ = [0.0] * 3\n", + " self.F_ = [0.0] * 3\n", + " self.T_ = [0.0] * 3\n", + " self.rdt_sequence_ = 0\n", + " self.ft_sequence_ = 0\n", + " self.status_ = 0\n", + " self.count_per_force_ = 1000000.0\n", + " self.count_per_torque_ = 1000000.0\n", + " self.force_torque_ = [0.0] * 6\n", + " self.mutex_ = threading.Lock()\n", + " self.socket_ = None\n", + " self.local_address_ = ('', 49152)\n", + " self.remote_address_ = ('192.168.4.1', 49152)\n", + "\n", + " def initialize(self):\n", + " if self.initialized_:\n", + " print(\"warning already initialized\")\n", + " return True\n", + " print(\"initializing\")\n", + " \n", + " try:\n", + " self.socket_ = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n", + " self.socket_.bind(self.local_address_)\n", + " self.socket_.connect(self.remote_address_)\n", + " except socket.error as e:\n", + " print(f\"Error: {e}\")\n", + " return False\n", + "\n", + " print(\"created sockets\")\n", + " self.initialized_ = True\n", + " return self.initialized_\n", + "\n", + " def read_ft(self):\n", + " msg = struct.pack('>HHH', 0x1234, 0x0002, 0)\n", + " self.socket_.send(msg)\n", + "\n", + " internal_going = True\n", + "\n", + " while internal_going:\n", + " data = self.socket_.recv(36) # Assuming message size is 48 bytes\n", + " if len(data) != 36:\n", + " print(f\"Received message of unexpected length {len(data)}\")\n", + "\n", + " self.mutex_.acquire()\n", + " # print(data)\n", + " # breakpoint()\n", + " (self.rdt_sequence_, self.ft_sequence_, self.status_, \n", + " self.Fx, self.Fy, self.Fz, \n", + " self.Tx, self.Ty, self.Tz) = struct.unpack('>3I6i', data)\n", + " \n", + " \n", + " self.F_[0] = self.Fx / self.count_per_force_ - self.F_bias_[0]\n", + " self.F_[1] = self.Fy / self.count_per_force_ - self.F_bias_[1]\n", + " self.F_[2] = self.Fz / self.count_per_force_ - self.F_bias_[2]\n", + " self.T_[0] = self.Tx / self.count_per_torque_ - self.T_bias_[0]\n", + " self.T_[1] = self.Ty / self.count_per_torque_ - self.T_bias_[1]\n", + " self.T_[2] = self.Tz / self.count_per_torque_ - self.T_bias_[2]\n", + "\n", + " if self.streaming_:\n", + " # Implement streaming logic here\n", + " pass\n", + "\n", + " internal_going = self.going_\n", + " self.mutex_.release()\n", + "\n", + " def set_bias(self, force=None, torque=None):\n", + " self.mutex_.acquire()\n", + " for i in range(3):\n", + " if force is None:\n", + " self.F_bias_[i] = self.F_[i]\n", + " else:\n", + " self.F_bias_[i] = force[i]\n", + "\n", + " if torque is None:\n", + " self.T_bias_[i] = self.T_[i]\n", + " else:\n", + " self.T_bias_[i] = torque[i]\n", + " self.mutex_.release()\n", + "\n", + " def reset_bias(self):\n", + " self.mutex_.acquire()\n", + " self.F_bias_ = [0.0] * 3\n", + " self.T_bias_ = [0.0] * 3\n", + " self.mutex_.release()\n", + "\n", + " def get_status(self):\n", + " self.mutex_.acquire()\n", + " status = (self.rdt_sequence_, self.ft_sequence_, self.status_)\n", + " self.mutex_.release()\n", + " return status\n", + "\n", + " def get_ft(self):\n", + " self.mutex_.acquire()\n", + " ft = (self.F_[:], self.T_[:])\n", + " self.mutex_.release()\n", + " return ft\n", + "\n", + " def get_ft_vector(self):\n", + " self.mutex_.acquire()\n", + " ft_vector = self.F_ + self.T_\n", + " self.mutex_.release()\n", + " return ft_vector\n", + "\n", + " def stop(self):\n", + " if self.initialized_:\n", + " self.mutex_.acquire()\n", + " self.going_ = False\n", + " self.mutex_.release()\n", + " self.socket_.close()\n", + " self.initialized_ = False\n", + "\n", + " def stream(self, stream):\n", + " self.mutex_.acquire()\n", + " self.streaming_ = stream\n", + " self.mutex_.release()\n", + "\n", + " def __del__(self):\n", + " self.stop()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "initializing\n", + "created sockets\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sensor = AtiFTSensor()\n", + "sensor.initialize()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.372439" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sensor.read_ft() \n", + "f, t = sensor.get_ft() # R\n", + "f[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fz_bias = 0.0\n", + "for i in range(300):\n", + " time.sleep(0.01)\n", + " sensor.read_ft() \n", + " f, t = sensor.get_ft()\n", + " fz_bias += f[2]\n", + "\n", + "fz_bias /=1000" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[72], line 21\u001b[0m\n\u001b[1;32m 19\u001b[0m T \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39meye(\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 20\u001b[0m T[\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m3\u001b[39m]\u001b[38;5;241m=\u001b[39mR\n\u001b[0;32m---> 21\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdateAllPose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstate\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mzeros\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m info \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mgetInfo()\n\u001b[1;32m 23\u001b[0m FR_nle \u001b[38;5;241m=\u001b[39m info[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnle\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m6\u001b[39m:][\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m3\u001b[39m]\n", + "File \u001b[0;32m~/projects/rooholla/locomotion/Go2Py/Go2Py/robot/model.py:213\u001b[0m, in \u001b[0;36mGo2Model.updateAllPose\u001b[0;34m(self, q, dq, T, v)\u001b[0m\n\u001b[1;32m 211\u001b[0m q_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mhstack([pin\u001b[38;5;241m.\u001b[39mSE3ToXYZQUATtuple(pin\u001b[38;5;241m.\u001b[39mSE3(T)), q[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mq_reordering_idx]])\n\u001b[1;32m 212\u001b[0m dq_ \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mhstack([v, dq[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mq_reordering_idx]])\n\u001b[0;32m--> 213\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdateKinematics\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mupdateDynamics(q_, dq_)\n", + "File \u001b[0;32m~/projects/rooholla/locomotion/Go2Py/Go2Py/robot/model.py:153\u001b[0m, in \u001b[0;36mGo2Model.updateKinematics\u001b[0;34m(self, q)\u001b[0m\n\u001b[1;32m 151\u001b[0m Jb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrobot\u001b[38;5;241m.\u001b[39mgetFrameJacobian(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrobot\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mgetFrameId(ef_frame), pin\u001b[38;5;241m.\u001b[39mReferenceFrame\u001b[38;5;241m.\u001b[39mLOCAL)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mef_Jw_[ef_frame]\u001b[38;5;241m=\u001b[39mJw[:, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdq_reordering_idx]\n\u001b[0;32m--> 153\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mef_Jb_[ef_frame]\u001b[38;5;241m=\u001b[39m\u001b[43mJb\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdq_reordering_idx\u001b[49m\u001b[43m]\u001b[49m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "import numpy as np\n", + "import pinocchio as pin\n", + "import time\n", + "vel = []\n", + "data_force = []\n", + "data_error = []\n", + "x0 = 0.23\n", + "z0 = -0.33\n", + "q = 12*[0.0] \n", + "dq = 12*[0.0] \n", + "kp = 12*[0.0] \n", + "kd = 12*[0.0] \n", + "tau = 12*[0.0] \n", + "for i in range(1000000):\n", + " state = robot.getJointStates()\n", + " Quat = robot.getIMU()['quat']\n", + " R = pin.Quaternion(np.hstack([Quat[1:],Quat[0]])).matrix()\n", + " R=np.eye(3)\n", + " T = np.eye(4)\n", + " T[0:3,0:3]=R\n", + " model.updateAllPose(state['q'], state['dq'], T, np.zeros(6))\n", + " info = model.getInfo()\n", + " FR_nle = info['nle'][6:][0:3]\n", + " FR_position = model.forwardKinematics(T, state['q'])['FR_foot'][0:3,-1]\n", + " FR_J = info['J_w']['FR_foot'][0:3, 6:][:,0:3]\n", + " FR_vel = FR_J@state['dq'][0:3].reshape(3,1)\n", + " \n", + " z_des = z0 \n", + " x_des = x0\n", + " task_kp = 650.0\n", + " fz = task_kp*(z_des - FR_position[2])\n", + " fx = 200*(x_des - FR_position[0]) + 4.5*(0-FR_vel[0])\n", + " Fx = np.array([fx[0],\n", + " 0,\n", + " fz]).reshape(3,1)\n", + " \n", + " tau_cmd = FR_J.T@Fx\n", + " data_error.append(z_des - FR_position[2])\n", + " sensor.read_ft() \n", + " f, t = sensor.get_ft() # R\n", + " data_force.append(f[2]-fz_bias)\n", + " \n", + "\n", + " kp[0] = 50.0\n", + " kd[0] = 2.0\n", + " # print(tau_cmd[1], x_des - FR_position[0], FR_vel[0])\n", + " # vel.append(FR_vel[0])\n", + " tau[1] = FR_nle[1]+tau_cmd[1].squeeze()\n", + " tau[2] = FR_nle[2]+tau_cmd[2].squeeze()\n", + " # tau[1] = 15.0* (0.7856252789497375-state['q'][1]) + 0.5* (0.-state['dq'][1])\n", + " robot.setCommands(q, dq, kp, kd, tau)\n", + " time.sleep(0.0007) " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "data_error = np.array(data_error)\n", + "data_force = np.array(data_force)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open(f'go2_impact_test2_{task_kp}.pkl', 'wb') as f:\n", + " pickle.dump({\n", + " 'P':kp,\n", + " 'z_error':data_error,\n", + " 'ATI_Fz':data_force\n", + " }, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + }, + { + 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", 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