forked from Telos4/RoboRally
improved grid logic for robots + general cleanup
This commit is contained in:
parent
32892e5dcf
commit
e3753f7644
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@ -1,21 +1,13 @@
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## License: Apache 2.0. See LICENSE file in root directory.
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## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.
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###############################################
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## Open CV and Numpy integration ##
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###############################################
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import pyrealsense2 as rs
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import numpy as np
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import cv2
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from cv2 import aruco
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from shapely.geometry import LineString
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import time
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DS5_product_ids = ["0AD1", "0AD2", "0AD3", "0AD4", "0AD5", "0AF6", "0AFE", "0AFF", "0B00", "0B01", "0B03", "0B07","0B3A"]
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def find_device_that_supports_advanced_mode() :
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def find_device_that_supports_advanced_mode():
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ctx = rs.context()
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ds5_dev = rs.device()
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devices = ctx.query_devices()
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@ -26,6 +18,7 @@ def find_device_that_supports_advanced_mode() :
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return dev
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raise Exception("No device that supports advanced mode was found")
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class ArucoEstimator:
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grid_columns = 10
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grid_rows = 8
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@ -36,9 +29,6 @@ class ArucoEstimator:
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'd': 3
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}
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robot_marker_ids = [12]
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robot_marker_estimates = dict([(id, None) for id in robot_marker_ids])
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angles = []
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corner_estimates = {
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@ -48,8 +38,11 @@ class ArucoEstimator:
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'd': (None, 0)
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}
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def __init__(self):
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if True: # check if realsense camera is connected
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def __init__(self, robot_marker_ids=[]):
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self.robot_marker_ids = robot_marker_ids
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self.robot_marker_estimates = dict([(id, None) for id in self.robot_marker_ids])
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if False: # check if realsense camera is connected
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# Configure depth and color streams
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self.pipeline = rs.pipeline()
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config = rs.config()
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@ -75,9 +68,6 @@ class ArucoEstimator:
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self.cv_camera = cv2.VideoCapture(0)
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self.pipeline = None
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# array containing pose estimates for each marker
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estimates = {}
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def run_tracking(self):
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try:
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while True:
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@ -100,16 +90,27 @@ class ArucoEstimator:
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corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
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frame = aruco.drawDetectedMarkers(color_image.copy(), corners, ids)
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if ids is not None:
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if False:#ids is not None:
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for id, c in zip(ids, corners):
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res = aruco.estimatePoseSingleMarkers(c, 0.10, self.camera_matrix, self.dist_coeffs)
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rvecs = res[0]
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tvecs = res[1]
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rvecs = res[0][0][0]
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tvecs = res[1][0][0]
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self.update_estimate(id, rvecs, tvecs)
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frame = self.draw_grid_lines(frame, corners, ids)
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frame = self.draw_robot_pos(frame, corners, ids)
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else:
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# pretent we detected some markers
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pass
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self.update_estimate(0, rvec=np.array([0,0,0]), tvec=np.array([-1, 1, 0]))
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self.update_estimate(1, rvec=np.array([0, 0, 0]), tvec=np.array([1, 1.5, 0]))
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self.update_estimate(2, rvec=np.array([0, 0, 0]), tvec=np.array([1, -1.5, 0]))
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self.update_estimate(3, rvec=np.array([0, 0, 0]), tvec=np.array([-1, -1, 0]))
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#import random
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#self.update_estimate(12, rvec=np.array([0.0, 0.0, 0.0]), tvec=np.array([-1.0 + random.random() * 2, -1.0 + random.random() * 2, 0]))
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self.update_estimate(12, rvec=np.array([0.0, 0.0, 0.0]),
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tvec=np.array([1.2, 0.42, 0]))
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# Show images
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cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
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@ -117,15 +118,9 @@ class ArucoEstimator:
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cv2.waitKey(1)
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finally:
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# Stop streaming
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self.pipeline.stop()
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# import matplotlib.pyplot as plt
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# plt.plot(playboard.angles)
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# plt.show()
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if self.pipeline is not None:
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# Stop streaming
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self.pipeline.stop()
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def update_estimate(self, id, rvec, tvec):
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# update the marker estimate with new data
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@ -135,7 +130,7 @@ class ArucoEstimator:
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corner = next(filter(lambda key: self.corner_marker_ids[key] == id, self.corner_marker_ids.keys())) # get corresponding corner to the detected marker
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old_estimate = self.corner_estimates[corner][0]
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n_estimates = self.corner_estimates[corner][1]
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tvec_proj = tvec[0][0][0:2] # projection to get rid of z coordinate
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tvec_proj = tvec[0:2] # projection to get rid of z coordinate
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tvec_proj = np.array((tvec_proj[0], -tvec_proj[1])) # flip y coordinate
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if old_estimate is not None:
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new_estimate = (n_estimates * old_estimate + tvec_proj) / (n_estimates + 1) # weighted update
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@ -145,17 +140,17 @@ class ArucoEstimator:
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elif id in self.robot_marker_ids:
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# for robot markers we extract x and y position as well as the angle
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# here we could also implement a filter
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x = tvec[0][0][0]
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y = -tvec[0][0][1] # flip y coordinate
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x = tvec[0]
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y = -tvec[1] # flip y coordinate
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# compute angle
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rot_mat, _ = cv2.Rodrigues(rvec[0][0])
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pose_mat = cv2.hconcat((rot_mat, tvec[0][0]))
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rot_mat, _ = cv2.Rodrigues(rvec)
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pose_mat = cv2.hconcat((rot_mat, tvec))
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_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
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angle = -euler_angles[2][0] * np.pi / 180.0
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self.angles.append(angle)
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self.robot_marker_estimates[id[0]] = (x, y, angle)
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self.robot_marker_estimates[id] = (x, y, angle)
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def all_corners_detected(self):
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# checks if all corner markers have been detected at least once
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@ -308,4 +303,4 @@ class ArucoEstimator:
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if __name__ == "__main__":
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playboard = Board()
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estimator = ArucoEstimator()
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@ -52,10 +52,21 @@ def myreceive(sock):
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class Robot:
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def __init__(self, id, ip, grid_pos = (0,0,0)):
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self.pos = None
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self.orient = None
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self.grid_pos = grid_pos
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# dictionary mapping the current orientation and a turn command to the resulting orientation
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resulting_orientation = {
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'^': {'turn left': '<', 'turn right': '>', 'turn around': 'v'},
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'>': {'turn left': '^', 'turn right': 'v', 'turn around': '<'},
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'v': {'turn left': '>', 'turn right': '<', 'turn around': '^'},
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'<': {'turn left': 'v', 'turn right': '^', 'turn around': '>'},
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}
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# dictionary mapping an orientation to its opposite
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opposites = {'^': 'v', '>': '<', 'v': '^', '<': '>'}
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def __init__(self, id, ip, x=0, y=0, orientation='>'):
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self.x = x
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self.y = y
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self.orientation = orientation
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self.id = id
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@ -75,17 +86,52 @@ class Robot:
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self.u1 = 0.0
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self.u2 = 0.0
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def get_neighbor_coordinates(self, direction):
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# get the coordinates of the neighboring tile in the given direction
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if direction == '^':
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return (self.x, self.y - 1)
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elif direction == '>':
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return (self.x + 1, self.y)
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elif direction == 'v':
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return (self.x, self.y + 1)
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elif direction == '<':
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return (self.x - 1, self.y)
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else:
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print("error: unknown direction")
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sys.exit(1)
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def move(self, type):
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if type == 'forward':
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target_tile = self.get_neighbor_coordinates(self.orientation)
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self.x = target_tile[0]
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self.y = target_tile[1]
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elif type == 'backward':
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opposite_orientation = Robot.opposites[self.orientation]
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target_tile = self.get_neighbor_coordinates(opposite_orientation)
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self.x = target_tile[0]
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self.y = target_tile[1]
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elif 'turn' in type:
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self.orientation = Robot.resulting_orientation[self.orientation][type]
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else:
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print("error: invalid move")
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sys.exit(1)
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def connect(self):
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# connect to robot
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try:
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print("connecting to robot {} with ip {} ...".format(self.id, self.ip))
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self.socket.connect((self.ip, 1234)) # connect to robot
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#self.socket.connect((self.ip, 1234)) # connect to robot
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print("connected!")
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except socket.error:
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print("could not connect to robot {} with ip {}".format(self.id, self.ip))
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sys.exit(1)
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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return f"x: {self.x}, y: {self.y}, orienation: {self.orientation}"
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def f_ode(t, x, u):
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# dynamical model of the two-wheeled robot
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# TODO: find exact values for these parameters
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@ -110,9 +156,6 @@ def f_ode(t, x, u):
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class RemoteController:
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def __init__(self, id, ip):
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self.anim_stopped = False
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# self.robots = #[Robot(11, '192.168.1.11', (6, -3, np.pi)), Robot(12, '192.168.1.12', (6, -3, np.pi)),
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# Robot(13, '192.168.1.13', (6, -3, np.pi)), Robot(14, '192.168.1.14', (6, -2, np.pi))]
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#self.robots = [Robot(13, '192.168.1.13', (6, -3, np.pi))]
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@ -120,8 +163,6 @@ class RemoteController:
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#self.robots = [Robot(11, '192.168.1.11', (6,-3,0)), Robot(14, '192.168.1.14', (6,3,0))]
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#self.robots = [Robot(11, '192.168.1.11'), Robot(14, '192.168.1.14')]
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self.robots = [Robot(12, '192.168.1.12')]
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#self.robots = [Robot(15, '192.168.1.102')]
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#self.robots = [Robot(id, ip)]
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self.robot_ids = {}
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for r in self.robots:
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@ -136,7 +177,6 @@ class RemoteController:
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for robot in self.robots:
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robot.connect()
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#self.comm_socket.bind((socket.gethostname(), 1337))
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self.comm_socket.bind(('', 1337))
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self.comm_socket.listen(5)
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@ -147,15 +187,12 @@ class RemoteController:
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self.ts = np.array([])
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self.xs = []
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# variable for mpc open loop
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self.ol_x = None
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self.ol_y = None
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self.mutex = threading.Lock()
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# ROS subscriber for detected markers
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self.estimator = opencv_viewer_example.ArucoEstimator()
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self.estimator = opencv_viewer_example.ArucoEstimator(self.robot_ids.keys())
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self.estimator_thread = threading.Thread(target=self.estimator.run_tracking)
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self.estimator_thread.start()
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@ -173,41 +210,13 @@ class RemoteController:
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# integrator
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self.r = scipy.integrate.ode(f_ode)
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self.omega_max = 5.0
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self.control_scaling = 0.1
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self.control_scaling = 0.2
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#self.omega_max = 13.32
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def measurement_callback(self, data):
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#print(data)
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# detect robots
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if data.id in self.robot_ids:
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r = self.robot_ids[data.id]
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r.pos = (data.x, data.y) # only x and y component are important for us
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r.euler = data.angle
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# save measured position and angle for plotting
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measurement = np.array([r.pos[0], r.pos[1], r.euler])
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if r.tms_0 is None:
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r.tms_0 = time.time()
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r.xm_0 = measurement
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self.mutex.acquire()
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try:
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r.tms = np.array([0.0])
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r.xms = measurement
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finally:
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self.mutex.release()
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else:
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self.mutex.acquire()
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try:
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r.tms = np.vstack((r.tms, time.time() - r.tms_0))
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r.xms = np.vstack((r.xms, measurement))
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finally:
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self.mutex.release()
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def controller(self):
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print("waiting until all markers are detected...")
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while not self.estimator.all_corners_detected():
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pass
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print("starting control")
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running = True
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@ -275,181 +284,103 @@ class RemoteController:
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clientsocket.close()
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def mpc_control(self, robot_id, cmd):
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robot = self.robot_ids[robot_id] # get robot to be controlled
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grid_pos = robot.grid_pos # grid position of the robot
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robot = self.robot_ids[robot_id] # get robot to be controlled
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print("old grid pos for robot {}: {}".format(robot_id, grid_pos))
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print("robot grid pos before move: ", robot)
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robot.move(cmd)
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print("robot grid pos after move: ", robot)
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# compute new grid position and orientation
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if cmd == 'forward':
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new_x = int(round(grid_pos[0] + 1 * np.cos(grid_pos[2])))
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new_y = int(round(grid_pos[1] + 1 * np.sin(grid_pos[2])))
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new_angle = grid_pos[2]
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elif cmd == 'backward':
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new_x = int(round(grid_pos[0] - 1 * np.cos(grid_pos[2])))
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new_y = int(round(grid_pos[1] - 1 * np.sin(grid_pos[2])))
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new_angle = grid_pos[2]
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elif cmd == 'turn left':
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = np.unwrap([0, grid_pos[2] + np.pi / 2])[1]
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elif cmd == 'turn right':
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = np.unwrap([0, grid_pos[2] - np.pi / 2])[1]
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elif cmd == 'turn around':
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new_x = grid_pos[0]
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new_y = grid_pos[1]
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new_angle = np.unwrap([0, grid_pos[2] + np.pi])[1]
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else:
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print("unknown command!")
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sys.exit(1)
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if new_x != grid_pos[0] and new_y != grid_pos[1]:
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print("problem detected!")
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self.target = self.estimator.get_pos_from_grid_point(robot.x, robot.y, robot.orientation)
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grid_pos = (new_x, new_y, new_angle)
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print("new grid pos for robot {}: {}\n".format(robot_id, grid_pos))
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self.pid = False
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self.mpc = True
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target_pos = self.estimator.get_pos_from_grid_point(new_x, new_y)
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self.target = np.array((target_pos[0], target_pos[1], grid_pos[2]))
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#np.array((0.25 * grid_pos[0], 0.25 * grid_pos[1], grid_pos[2]))
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near_target = 0
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self.pid = False
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self.mpc = True
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while near_target < 5:
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# open loop controller
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events = pygame.event.get()
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near_target = 0
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while near_target < 5:
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# open loop controller
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events = pygame.event.get()
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for event in events:
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if event.type == pygame.KEYDOWN:
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if event.key == pygame.K_UP:
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self.controlling = True
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self.t = time.time()
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elif event.key == pygame.K_DOWN:
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self.controlling = False
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if self.robot_ids[robot_id].socket:
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self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
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elif event.key == pygame.K_0:
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self.target = np.array([0,0,0])
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elif event.key == pygame.K_PLUS:
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self.control_scaling += 0.1
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self.control_scaling = min(self.control_scaling, 1.0)
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print("control scaling = ", self.control_scaling)
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elif event.key == pygame.K_MINUS:
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self.control_scaling -= 0.1
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self.control_scaling = max(self.control_scaling, 0.1)
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print("control scaling = ", self.control_scaling)
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elif event.key == pygame.K_ESCAPE:
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print("quit!")
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self.controlling = False
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if self.robot_ids[robot_id].socket:
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self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
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self.anim_stopped = True
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return
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elif event.key == pygame.QUIT:
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print("quit!")
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self.controlling = False
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if self.robot_ids[robot_id].socket:
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self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
|
||||
self.anim_stopped = True
|
||||
return
|
||||
|
||||
if self.mpc:
|
||||
#x_pred = self.get_measurement_prediction(robot_id)
|
||||
x_pred = self.get_measurement()
|
||||
|
||||
tmpc_start = time.time()
|
||||
|
||||
error_pos = np.linalg.norm(x_pred[0:2] - self.target[0:2])
|
||||
angles_unwrapped = np.unwrap([x_pred[2], self.target[2]]) # unwrap angle to avoid jump in data
|
||||
error_ang = np.abs(angles_unwrapped[0] - angles_unwrapped[1])
|
||||
#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 > 0.05 or error_ang > 0.2:
|
||||
# solve mpc open loop problem
|
||||
res = self.ols.solve(x_pred, self.target)
|
||||
|
||||
#us1 = res[0]
|
||||
#us2 = res[1]
|
||||
us1 = res[0] * self.control_scaling
|
||||
us2 = res[1] * self.control_scaling
|
||||
#print("u = {}", (us1, us2))
|
||||
|
||||
# save open loop trajectories for plotting
|
||||
self.mutex.acquire()
|
||||
try:
|
||||
self.ol_x = res[2]
|
||||
self.ol_y = res[3]
|
||||
finally:
|
||||
self.mutex.release()
|
||||
|
||||
tmpc_end = time.time()
|
||||
#print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
|
||||
dt_mpc = time.time() - self.t
|
||||
if dt_mpc < self.dt: # wait until next control can be applied
|
||||
#print("sleeping for {} seconds...".format(self.dt - dt_mpc))
|
||||
time.sleep(self.dt - dt_mpc)
|
||||
else:
|
||||
us1 = [0] * self.mstep
|
||||
us2 = [0] * self.mstep
|
||||
|
||||
near_target += 1
|
||||
robot.grid_pos = grid_pos
|
||||
|
||||
# send controls to the robot
|
||||
for i in range(0, self.mstep): # option to use multistep mpc if len(range) > 1
|
||||
u1 = us1[i]
|
||||
u2 = us2[i]
|
||||
for event in events:
|
||||
if event.type == pygame.KEYDOWN:
|
||||
if event.key == pygame.K_UP:
|
||||
self.controlling = True
|
||||
self.t = time.time()
|
||||
elif event.key == pygame.K_DOWN:
|
||||
self.controlling = False
|
||||
if self.robot_ids[robot_id].socket:
|
||||
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
|
||||
self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
|
||||
elif event.key == pygame.K_0:
|
||||
self.target = np.array([0,0,0])
|
||||
elif event.key == pygame.K_PLUS:
|
||||
self.control_scaling += 0.1
|
||||
self.control_scaling = min(self.control_scaling, 1.0)
|
||||
print("control scaling = ", self.control_scaling)
|
||||
elif event.key == pygame.K_MINUS:
|
||||
self.control_scaling -= 0.1
|
||||
self.control_scaling = max(self.control_scaling, 0.1)
|
||||
print("control scaling = ", self.control_scaling)
|
||||
elif event.key == pygame.K_ESCAPE:
|
||||
print("quit!")
|
||||
self.controlling = False
|
||||
if self.robot_ids[robot_id].socket:
|
||||
self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
|
||||
self.anim_stopped = True
|
||||
return
|
||||
elif event.key == pygame.QUIT:
|
||||
print("quit!")
|
||||
self.controlling = False
|
||||
if self.robot_ids[robot_id].socket:
|
||||
self.robot_ids[robot_id].socket.send('(0.0,0.0)\n')
|
||||
self.anim_stopped = True
|
||||
return
|
||||
|
||||
if self.mpc:
|
||||
x_pred = self.get_measurement(robot_id)
|
||||
|
||||
def get_measurement_prediction(self, robot_id):
|
||||
# get measurement
|
||||
self.mutex.acquire()
|
||||
try:
|
||||
window = 3
|
||||
last_measurement = copy.deepcopy(self.robot_ids[robot_id].xms[-window:])
|
||||
#print("last_measurements = {}".format(last_measurement))
|
||||
#print("mean = {}".format(np.mean(last_measurement, axis=0)))
|
||||
last_measurement = np.mean(last_measurement, axis=0)
|
||||
last_time = copy.deepcopy(self.robot_ids[robot_id].tms[-1])
|
||||
finally:
|
||||
self.mutex.release()
|
||||
tmpc_start = time.time()
|
||||
|
||||
# prediction of state at time the mpc will terminate
|
||||
self.r.set_f_params(np.array([self.robot_ids[robot_id].u1 * self.omega_max, self.robot_ids[robot_id].u2 * self.omega_max]))
|
||||
error_pos = np.linalg.norm(x_pred[0:2] - self.target[0:2])
|
||||
angles_unwrapped = np.unwrap([x_pred[2], self.target[2]]) # unwrap angle to avoid jump in data
|
||||
error_ang = np.abs(angles_unwrapped[0] - angles_unwrapped[1])
|
||||
#print("error pos = ", error_pos)
|
||||
#print("error_pos = {}, error_ang = {}".format(error_pos, error_ang))
|
||||
|
||||
self.r.set_initial_value(last_measurement, last_time)
|
||||
#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)
|
||||
|
||||
x_pred = self.r.integrate(self.r.t + self.dt)
|
||||
#us1 = res[0]
|
||||
#us2 = res[1]
|
||||
us1 = res[0] * self.control_scaling
|
||||
us2 = res[1] * self.control_scaling
|
||||
#print("u = {}", (us1, us2))
|
||||
|
||||
return x_pred
|
||||
tmpc_end = time.time()
|
||||
#print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
|
||||
dt_mpc = time.time() - self.t
|
||||
if dt_mpc < self.dt: # wait until next control can be applied
|
||||
#print("sleeping for {} seconds...".format(self.dt - dt_mpc))
|
||||
time.sleep(self.dt - dt_mpc)
|
||||
else:
|
||||
us1 = [0] * self.mstep
|
||||
us2 = [0] * self.mstep
|
||||
|
||||
def get_measurement_old(self):
|
||||
self.mutex.acquire()
|
||||
try:
|
||||
last_measurement = copy.deepcopy(self.xms[-1:])
|
||||
finally:
|
||||
self.mutex.release()
|
||||
return last_measurement[0]
|
||||
near_target += 1
|
||||
|
||||
def get_measurement(self):
|
||||
return np.array(self.estimator.get_robot_state_estimate(12))
|
||||
# send controls to the robot
|
||||
for i in range(0, self.mstep): # option to use multistep mpc if len(range) > 1
|
||||
u1 = us1[i]
|
||||
u2 = us2[i]
|
||||
if self.robot_ids[robot_id].socket:
|
||||
#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
|
||||
|
||||
def pos_getter(self):
|
||||
while True:
|
||||
x_pred = self.get_measurement_prediction()
|
||||
|
||||
print("pos = ", x_pred)
|
||||
def get_measurement(self, robot_id):
|
||||
return np.array(self.estimator.get_robot_state_estimate(robot_id))
|
||||
|
||||
def main(args):
|
||||
parser = ArgumentParser()
|
||||
|
|
@ -460,9 +391,6 @@ def main(args):
|
|||
marker_id = int(args.id)
|
||||
ip = args.ip
|
||||
|
||||
|
||||
#rospy.init_node('controller_node', anonymous=True)
|
||||
|
||||
rc = RemoteController(marker_id, ip)
|
||||
|
||||
pygame.init()
|
||||
|
|
@ -471,21 +399,7 @@ def main(args):
|
|||
screenwidth = 640
|
||||
pygame.display.set_mode([screenwidth, screenheight])
|
||||
|
||||
# print("waiting until track is completely detected")
|
||||
# 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()
|
||||
|
||||
#time.sleep(10)
|
||||
#rc.ani()
|
||||
|
||||
rc.controller()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main(sys.argv)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user