coupled robot control with new position estimator

remote_control/opencv_viewer_example.py

@@ -0,0 +1,280 @@
+## License: Apache 2.0. See LICENSE file in root directory.
+## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.
+
+###############################################
+##      Open CV and Numpy integration        ##
+###############################################
+
+import pyrealsense2 as rs
+import numpy as np
+import cv2
+from cv2 import aruco
+from shapely.geometry import LineString
+
+import time
+
+DS5_product_ids = ["0AD1", "0AD2", "0AD3", "0AD4", "0AD5", "0AF6", "0AFE", "0AFF", "0B00", "0B01", "0B03", "0B07","0B3A"]
+
+def find_device_that_supports_advanced_mode() :
+    ctx = rs.context()
+    ds5_dev = rs.device()
+    devices = ctx.query_devices()
+    for dev in devices:
+        if dev.supports(rs.camera_info.product_id) and str(dev.get_info(rs.camera_info.product_id)) in DS5_product_ids:
+            if dev.supports(rs.camera_info.name):
+                print("Found device that supports advanced mode:", dev.get_info(rs.camera_info.name))
+            return dev
+    raise Exception("No device that supports advanced mode was found")
+
+class ArucoEstimator:
+    grid_columns = 10
+    grid_rows = 8
+    corner_marker_ids = {
+        'a': 0,
+        'b': 1,
+        'c': 2,
+        'd': 3
+    }
+
+    robot_marker_ids = [12]
+
+    robot_marker_estimates = dict([(id, None) for id in robot_marker_ids])
+    angles = []
+
+    corner_estimates = {
+        'a': (None, 0),     # (estimate, n_estimates)
+        'b': (None, 0),
+        'c': (None, 0),
+        'd': (None, 0)
+    }
+
+    def __init__(self):
+        if True:   # check if realsense camera is connected
+            # Configure depth and color streams
+            self.pipeline = rs.pipeline()
+            config = rs.config()
+            # config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
+            # config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8, 30)
+            config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
+            # config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
+
+            # Start streaming
+            self.pipeline.start(config)
+
+            camera_intrinsics = self.pipeline.get_active_profile().get_stream(
+                rs.stream.color).as_video_stream_profile().get_intrinsics()
+            self.camera_matrix = np.zeros((3, 3))
+            self.camera_matrix[0][0] = camera_intrinsics.fx
+            self.camera_matrix[1][1] = camera_intrinsics.fy
+            self.camera_matrix[0][2] = camera_intrinsics.ppx
+            self.camera_matrix[1][2] = camera_intrinsics.ppy
+            self.dist_coeffs = np.array(camera_intrinsics.coeffs)
+            # more info: https://dev.intelrealsense.com/docs/projection-in-intel-realsense-sdk-20
+        else:
+            # use other camera
+            self.cv_camera = cv2.VideoCapture(0)
+            self.pipeline = None
+
+        # array containing pose estimates for each marker
+        estimates = {}
+
+    def run_tracking(self):
+        try:
+            while True:
+                if self.pipeline:
+                    frames = self.pipeline.wait_for_frames()
+                    color_frame = frames.get_color_frame()
+                    if not color_frame:
+                        continue
+                    # Convert images to numpy arrays
+                    color_image = np.asanyarray(color_frame.get_data())
+                else:
+                    # Capture frame-by-frame
+                    ret, color_image = self.cv_camera.read()
+
+                gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
+
+                # aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_250)
+                aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)
+                parameters = aruco.DetectorParameters_create()
+                corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
+                frame = aruco.drawDetectedMarkers(color_image.copy(), corners, ids)
+
+                if ids is not None:
+                    for id, c in zip(ids, corners):
+                        res = aruco.estimatePoseSingleMarkers(c, 0.10, self.camera_matrix, self.dist_coeffs)
+                        rvecs = res[0]
+                        tvecs = res[1]
+
+                        self.update_estimate(id, rvecs, tvecs)
+
+                    frame = self.draw_grid_lines(frame, corners, ids)
+                    frame = self.draw_robot_pos(frame, corners, ids)
+
+                # Show images
+                cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
+                cv2.imshow('RealSense', frame)
+                cv2.waitKey(1)
+
+        finally:
+            # Stop streaming
+            self.pipeline.stop()
+
+
+            # import matplotlib.pyplot as plt
+
+            #  plt.plot(playboard.angles)
+            # plt.show()
+
+
+    def update_estimate(self, id, rvec, tvec):
+        # update the marker estimate with new data
+        if id in self.corner_marker_ids.values():
+            # for corner markers we compute the mean of all measurements s.t. the position stabilizes over time
+            # (we assume the markers do not move)
+            corner = next(filter(lambda key: self.corner_marker_ids[key] == id, self.corner_marker_ids.keys()))  # get corresponding corner to the detected marker
+            old_estimate = self.corner_estimates[corner][0]
+            n_estimates = self.corner_estimates[corner][1]
+            tvec_proj = tvec[0][0][0:2] # projection to get rid of z coordinate
+            tvec_proj = np.array((tvec_proj[0], -tvec_proj[1])) # flip y coordinate
+            if old_estimate is not None:
+                new_estimate = (n_estimates * old_estimate + tvec_proj) / (n_estimates + 1)  # weighted update
+            else:
+                new_estimate = tvec_proj # first estimate
+            self.corner_estimates[corner] = (new_estimate, n_estimates + 1)
+        elif id in self.robot_marker_ids:
+            # for robot markers we extract x and y position as well as the angle
+            # here we could also implement a filter
+            x = tvec[0][0][0]
+            y = -tvec[0][0][1]  # flip y coordinate
+
+            # compute angle
+            rot_mat, _ = cv2.Rodrigues(rvec[0][0])
+            pose_mat = cv2.hconcat((rot_mat, tvec[0][0]))
+            _, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
+            angle = -euler_angles[2][0] * np.pi / 180.0
+
+            self.angles.append(angle)
+            self.robot_marker_estimates[id[0]] = (x, y, angle)
+
+    def all_corners_detected(self):
+        # checks if all corner markers have been detected at least once
+        return not any([estimate[0] is None for estimate in self.corner_estimates.values()])
+
+    def get_pos_from_grid_point(self, x, y):
+        # returns the position for the given grid point based on the current corner estimates
+        assert x >= 0 and x < self.grid_columns
+        assert y >= 0 and y < self.grid_rows
+        assert self.all_corners_detected()
+
+        # compute column line
+        a = self.corner_estimates['a'][0]
+        b = self.corner_estimates['b'][0]
+        c = self.corner_estimates['c'][0]
+        d = self.corner_estimates['d'][0]
+        vab = b - a
+        vdc = c - d
+        column_line_top = a + (x + 0.5)/self.grid_columns * vab
+        column_line_bottom = d + (x + 0.5) / self.grid_columns * vdc
+
+        vad = d - a
+        vbc = c - b
+        row_line_top = a + (y + 0.5)/self.grid_rows * vad
+        row_line_bottom = b + (y + 0.5) / self.grid_rows * vbc
+
+        column_line = LineString([column_line_top, column_line_bottom])
+        row_line = LineString([row_line_top, row_line_bottom])
+
+        int_pt = column_line.intersection(row_line)
+        point_of_intersection = np.array([int_pt.x, int_pt.y])
+
+        return point_of_intersection
+
+    def get_grid_point_from_pos(self):
+        # return the nearest grid point for the given position estimate
+        pass
+
+    def print_corner_estimates(self):
+        for key, value in self.corner_estimates.items():
+            if value[0] is not None:
+                print("corner marker {} at pos {}".format(key, value[0]))
+        print()
+
+    def draw_corner_line(self, frame, corner_1, corner_2, corner_coords_dict):
+        # draws a line between the given markers onto the given frame
+        if corner_1 in corner_coords_dict and corner_2 in corner_coords_dict:
+            frame = cv2.line(frame, corner_coords_dict[corner_1], corner_coords_dict[corner_2], color=(0, 0, 255),
+                         thickness=2)
+        return frame
+
+    def draw_grid_lines(self, frame, corners, ids):
+        # draws a grid onto the given frame
+        board_corners_pixel_coords = {}
+        for corner, id in self.corner_marker_ids.items():
+            try:
+                ind, _ = np.where(ids == id)   # find index
+                ind = ind[0]
+                board_corners_pixel_coords[corner] = tuple(np.mean(corners[ind][0], axis=0))
+            except IndexError:
+                pass
+
+        frame = self.draw_corner_line(frame, 'a', 'b', board_corners_pixel_coords)
+        frame = self.draw_corner_line(frame, 'b', 'c', board_corners_pixel_coords)
+        frame = self.draw_corner_line(frame, 'c', 'd', board_corners_pixel_coords)
+        frame = self.draw_corner_line(frame, 'd', 'a', board_corners_pixel_coords)
+
+        if set(board_corners_pixel_coords.keys()) == set(self.corner_marker_ids.keys()): # all markers have been detected
+            # compute column line
+            a = np.array(board_corners_pixel_coords['a'])
+            b = np.array(board_corners_pixel_coords['b'])
+            c = np.array(board_corners_pixel_coords['c'])
+            d = np.array(board_corners_pixel_coords['d'])
+
+            vab = b - a
+            vdc = c - d
+            for x in range(1,self.grid_columns):
+                column_line_top = a + x / self.grid_columns * vab
+                column_line_bottom = d + x / self.grid_columns * vdc
+                frame = cv2.line(frame, tuple(column_line_top), tuple(column_line_bottom), color=(255, 0, 0),
+                         thickness=1)
+
+            vad = d - a
+            vbc = c - b
+            for y in range(1, self.grid_rows):
+                row_line_top = a + y / self.grid_rows * vad
+                row_line_bottom = b + y / self.grid_rows * vbc
+                frame = cv2.line(frame, tuple(row_line_top), tuple(row_line_bottom), color=(255, 0, 0),
+                             thickness=1)
+
+        return frame
+
+    def get_robot_state_estimate(self, id):
+        if id in self.robot_marker_estimates:
+            if self.robot_marker_estimates[id] is not None:
+                return self.robot_marker_estimates[id]
+            else:
+                print(f"error: no estimate available for robot {id}")
+        else:
+            print(f"error: invalid robot id {id}")
+
+    def draw_robot_pos(self, frame, corners, ids):
+        # draws information about the robot positions onto the given frame
+        robot_corners_pixel_coords = {}
+        for id, estimate in self.robot_marker_estimates.items():
+            try:
+                ind, _ = np.where(ids == id)   # find index
+                ind = ind[0]
+                robot_corners_pixel_coords[id] = tuple(np.mean(corners[ind][0], axis=0))
+            except IndexError:
+                pass
+
+        for id, coord in robot_corners_pixel_coords.items():
+            x = self.robot_marker_estimates[id][0]
+            y = self.robot_marker_estimates[id][1]
+            angle = self.robot_marker_estimates[id][2]
+            frame = cv2.putText(frame, "pos = ({:5.3f}, {:5.3f}), ang = {:5.3f}".format(x, y, angle), coord, cv2.FONT_HERSHEY_SIMPLEX, 0.50, (0,255,0))
+        return frame
+
+
+if __name__ == "__main__":
+    playboard = Board()

remote_control/roborally.py

@@ -26,34 +26,36 @@ import time
 
 from casadi_opt import OpenLoopSolver
 
-from marker_pos_angle.msg import id_pos_angle
+#from marker_pos_angle.msg import id_pos_angle
 
 from collections import OrderedDict
 
 from argparse import ArgumentParser
 
+import opencv_viewer_example
+
 MSGLEN = 64
 def myreceive(sock):
     chunks = []
     bytes_recd = 0
     while bytes_recd < MSGLEN:
-        chunk = sock.recv(min(MSGLEN - bytes_recd, 2048))
+        chunk = sock.recv(1)
         if chunk == b'':
             raise RuntimeError("socket connection broken")
         chunks.append(chunk)
         bytes_recd = bytes_recd + len(chunk)
 
-        if chunk[-1] == '\n':
+        if chunks[-1] == b'\n':
             break
 
     return b''.join(chunks)
 
 
 class Robot:
-    def __init__(self, id, ip):
+    def __init__(self, id, ip, grid_pos = (0,0,0)):
         self.pos = None
         self.orient = None
-        self.grid_pos = (0,0,0)
+        self.grid_pos = grid_pos
 
         self.id = id
 
@@ -111,8 +113,13 @@ class RemoteController:
 
         self.anim_stopped = False
 
-        self.robots = [Robot(11, '192.168.1.11'), Robot(14, '192.168.1.14')]
+#        self.robots = #[Robot(11, '192.168.1.11', (6, -3, np.pi)), Robot(12, '192.168.1.12', (6, -3, np.pi)),
-        #self.robots = [Robot(11, '192.168.1.101')]
+                      # Robot(13, '192.168.1.13', (6, -3, np.pi)), Robot(14, '192.168.1.14', (6, -2, np.pi))]
+        #self.robots = [Robot(13, '192.168.1.13', (6, -3, np.pi))]
+        #self.robots = []
+        #self.robots = [Robot(11, '192.168.1.11', (6,-3,0)), Robot(14, '192.168.1.14', (6,3,0))]
+        #self.robots = [Robot(11, '192.168.1.11'), Robot(14, '192.168.1.14')]
+        self.robots = [Robot(12, '192.168.1.12')]
         #self.robots = [Robot(15, '192.168.1.102')]
         #self.robots = [Robot(id, ip)]
 
@@ -148,42 +155,14 @@ class RemoteController:
         self.mutex = threading.Lock()
 
         # ROS subscriber for detected markers
-        marker_sub = rospy.Subscriber("/marker_id_pos_angle", id_pos_angle, self.measurement_callback)
+        self.estimator = opencv_viewer_example.ArucoEstimator()
+
+        self.estimator_thread = threading.Thread(target=self.estimator.run_tracking)
+        self.estimator_thread.start()
 
         # pid parameters
         self.controlling = False
 
-        # animation
-        self.fig = plt.figure()
-        self.ax = self.fig.add_subplot(2,2,1)
-        self.ax2 = self.fig.add_subplot(2, 2, 2)
-        self.ax3 = self.fig.add_subplot(2, 2, 4)
-
-        self.xdata, self.ydata = [], []
-        self.line, = self.ax.plot([],[], color='grey', linestyle=':')
-        self.line_sim, = self.ax.plot([], [])
-        self.line_ol, = self.ax.plot([],[], color='green', linestyle='--')
-        self.dirm, = self.ax.plot([], [])
-        self.dirs, = self.ax.plot([], [])
-
-        self.line_x, = self.ax2.plot([],[])
-        self.line_y, = self.ax3.plot([], [])
-
-        self.track_line_inner, = self.ax.plot([], [])
-        self.track_line_outer, = self.ax.plot([], [])
-
-        self.ax.set_xlabel('x-position')
-        self.ax.set_ylabel('y-position')
-        self.ax.grid()
-
-        self.ax2.set_xlabel('Zeit t')
-        self.ax2.set_ylabel('x-position')
-        self.ax2.grid()
-
-        self.ax3.set_xlabel('Zeit t')
-        self.ax3.set_ylabel('y-position')
-        self.ax3.grid()
-
         self.mstep = 2
         self.ols = OpenLoopSolver(N=20, T=1.0)
         self.ols.setup()
@@ -194,96 +173,10 @@ class RemoteController:
         # integrator
         self.r = scipy.integrate.ode(f_ode)
         self.omega_max = 5.0
-        self.control_scaling = 0.3
+        self.control_scaling = 0.1
         #self.omega_max = 13.32
 
 
-    def ani(self):
-
-        print("starting animation")
-        self.ani = anim.FuncAnimation(self.fig, init_func=self.ani_init, func=self.ani_update, interval=10, blit=True)
-        plt.ion()
-        plt.show(block=True)
-
-    def ani_init(self):
-        self.ax.set_xlim(-2, 2)
-        self.ax.set_ylim(-2, 2)
-        self.ax.set_aspect('equal', adjustable='box')
-
-        self.ax2.set_ylim(-2, 2)
-        self.ax2.set_xlim(0, 10)
-
-        self.ax3.set_ylim(-2, 2)
-        self.ax3.set_xlim(0, 10)
-
-        self.track_line_inner.set_data(self.track.inner_poly.exterior.xy)
-        self.track_line_outer.set_data(self.track.outer_poly.exterior.xy)
-
-        return self.line, self.line_sim, self.dirm, self.dirs, self.line_ol,\
-               self.track_line_inner, self.track_line_outer, self.line_x,self.line_y,
-
-    def ani_update(self, frame):
-        if self.anim_stopped:
-            self.ani.event_source.stop()
-            sys.exit(0)
-
-        #print("plotting")
-        self.mutex.acquire()
-        try:
-            # copy data for plot from global arrays
-            if self.tms is not None:
-                tm_local = deepcopy(self.tms)
-                xm_local = deepcopy(self.xms)
-
-                if len(tm_local) > 0:
-                    # plot path of the robot
-                    self.line.set_data(xm_local[:,0], xm_local[:,1])
-
-                    # compute and plot direction the robot is facing
-                    a = xm_local[-1, 0]
-                    b = xm_local[-1, 1]
-
-                    a2 = a + np.cos(xm_local[-1, 2]) * 0.2
-                    b2 = b + np.sin(xm_local[-1, 2]) * 0.2
-
-                    self.dirm.set_data(np.array([a, a2]), np.array([b, b2]))
-
-                    n_plot = 300
-                    if len(tm_local) > n_plot:
-                        # plot x and y coordinate
-                        self.line_x.set_data(tm_local[-n_plot:] - (tm_local[-1] - 10), xm_local[-n_plot:,0])
-                        self.line_y.set_data(tm_local[-n_plot:] - (tm_local[-1] - 10), xm_local[-n_plot:, 1])
-
-            ts_local = deepcopy(self.ts)
-            xs_local = deepcopy(self.xs)
-
-            if len(ts_local) > 0:
-                # plot simulated path of the robot
-                self.line_sim.set_data(xs_local[:,0], xs_local[:,1])
-
-                # compute and plot direction the robot is facing
-                a = xs_local[-1, 0]
-                b = xs_local[-1, 1]
-
-                a2 = a + np.cos(xs_local[-1, 2]) * 0.2
-                b2 = b + np.sin(xs_local[-1, 2]) * 0.2
-
-                self.dirs.set_data(np.array([a, a2]), np.array([b, b2]))
-
-            ol_x_local = deepcopy(self.ol_x)
-            ol_y_local = deepcopy(self.ol_y)
-
-            if ol_x_local is not None:
-                self.line_ol.set_data(ol_x_local, ol_y_local)
-            else:
-                self.line_ol.set_data([],[])
-
-        finally:
-            self.mutex.release()
-
-        return self.line, self.line_sim, self.dirm, self.dirs, self.line_ol, self.track_line_inner, self.track_line_outer,\
-               self.line_x, self.line_y, 
-
     def measurement_callback(self, data):
         #print(data)
 
@@ -330,7 +223,7 @@ class RemoteController:
 
                     inputs = data.split(b',')
                     cmd = inputs[0]
-                    cmd = cmd.strip()
+                    cmd = cmd.strip().decode()
 
                     if len(inputs) > 1:
                         if cmd in self.valid_cmds:
@@ -341,13 +234,14 @@ class RemoteController:
                                     clientsocket.sendall(b'Could not read robot id!\n')
 
                                 if robot_id in self.robot_ids:
-                                    if cmd == 'get position':
+                                    if cmd == b'get position':
                                         clientsocket.sendall(bytes('{}\n'.format(self.robot_ids[robot_id].grid_pos)))
-                                    elif cmd == 'set position':
+                                    elif cmd == b'set position':
                                         try:
                                             pos_data = ",".join(inputs[2:])
                                             new_grid_pos = tuple(map(lambda x: int(x[1]) if x[0] < 2 else float(x[1]), enumerate(pos_data.strip().strip('()').split(','))))
                                             self.robot_ids[robot_id].grid_pos = new_grid_pos
+                                            clientsocket.sendall(b'OK\n')
                                         except IndexError as e:
                                             print("could not set grid position!")
                                             clientsocket.sendall(bytes(
@@ -366,7 +260,7 @@ class RemoteController:
                         else:
                             clientsocket.sendall(b'Invalid command!\n')
                     else: # len(inputs) <= 1
-                        if 'quit' in inputs[0]:
+                        if b'quit' in inputs[0]:
                             clientsocket.close()
                             self.comm_socket.close()
                             connected = False
@@ -375,8 +269,6 @@ class RemoteController:
                             print("could not process command!")
                             clientsocket.sendall(b'Could not process request!\n')
 
-
-
                 except RuntimeError:
                     print("disconnected")
                     connected = False
@@ -418,7 +310,9 @@ class RemoteController:
             grid_pos = (new_x, new_y, new_angle)
             print("new grid pos for robot {}: {}\n".format(robot_id, grid_pos))
 
-            self.target = np.array((0.25 * grid_pos[0], 0.25 * grid_pos[1], grid_pos[2]))
+            target_pos = self.estimator.get_pos_from_grid_point(new_x, new_y)
+            self.target = np.array((target_pos[0], target_pos[1], grid_pos[2]))
+            #np.array((0.25 * grid_pos[0], 0.25 * grid_pos[1], grid_pos[2]))
 
             self.pid = False
             self.mpc = True
@@ -464,7 +358,8 @@ class RemoteController:
                             return
 
                 if self.mpc:
-                    x_pred = self.get_measurement_prediction(robot_id)
+                    #x_pred = self.get_measurement_prediction(robot_id)
+                    x_pred = self.get_measurement()
 
                     tmpc_start = time.time()
 
@@ -474,7 +369,8 @@ class RemoteController:
                     #print("error pos = ", error_pos)
                     #print("error_pos = {}, error_ang = {}".format(error_pos, error_ang))
 
-                    if error_pos > 0.075 or error_ang > 0.35:
+                    #if error_pos > 0.075 or error_ang > 0.35:
+                    if error_pos > 0.05 or error_ang > 0.2:
                         # solve mpc open loop problem
                         res = self.ols.solve(x_pred, self.target)
 
@@ -510,7 +406,7 @@ class RemoteController:
                         u1 = us1[i]
                         u2 = us2[i]
                         if self.robot_ids[robot_id].socket:
-                            self.robot_ids[robot_id].socket.send('({},{})\n'.format(u1, u2))
+                            self.robot_ids[robot_id].socket.send('({},{})\n'.format(u1, u2).encode())
                             if i < self.mstep:
                                 time.sleep(self.dt)
                         self.t = time.time() # save time the most recent control was applied
@@ -538,7 +434,7 @@ class RemoteController:
 
         return x_pred
 
-    def get_measurement(self):
+    def get_measurement_old(self):
         self.mutex.acquire()
         try:
             last_measurement = copy.deepcopy(self.xms[-1:])
@@ -546,6 +442,9 @@ class RemoteController:
             self.mutex.release()
         return last_measurement[0]
 
+    def get_measurement(self):
+        return np.array(self.estimator.get_robot_state_estimate(12))
+
     def pos_getter(self):
         while True:
             x_pred = self.get_measurement_prediction()
@@ -562,7 +461,7 @@ def main(args):
     ip = args.ip
 
 
-    rospy.init_node('controller_node', anonymous=True)
+    #rospy.init_node('controller_node', anonymous=True)
 
     rc = RemoteController(marker_id, ip)
 
@@ -576,6 +475,10 @@ def main(args):
  #   while not rc.track.track_complete:
  #       pass
 
+
+
+    time.sleep(1)
+
     #threading.Thread(target=rc.input_handling).start()
     controller_thread = threading.Thread(target=rc.controller)
     controller_thread.start()