├── .gitignore ├── docker_ws ├── build.sh └── Dockerfile.tello ├── camera_calibration_tools ├── vid2photo.py ├── generator.py ├── calibration_generator.py ├── Identificator.py └── camera_calibration.py ├── requirements.txt ├── tracking.sh ├── controllers ├── BangBang_controller.py └── PID_controller.py ├── README.md ├── PID_parameters_handler.py ├── tuning ├── plotAssistant.py ├── SafeOptTunerAssistant.py ├── measurementAssistant.py ├── tuning_x_axis_with_SafeOpt.py ├── tuning_z_axis_with_SafeOpt.py └── tuning_y_axis_with_SafeOpt.py ├── safeopt_example.py ├── GUI └── gui_drawer.py ├── feedback.py ├── followMeDrone.py └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | /venv/ 2 | /.idea/ 3 | __pycache__ 4 | 5 | -------------------------------------------------------------------------------- /docker_ws/build.sh: -------------------------------------------------------------------------------- 1 | docker build -t tello:orientamento -f Dockerfile.tello . -------------------------------------------------------------------------------- /camera_calibration_tools/vid2photo.py: -------------------------------------------------------------------------------- 1 | 2 | import cv2 3 | 4 | name = "videodrone.mp4" 5 | rootname = name.split(".")[0] 6 | cap = cv2.VideoCapture(name) 7 | counter = 0 8 | each = 3 9 | length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 10 | for i in range(length): 11 | ret, frame = cap.read() 12 | if i % each == 0: 13 | cv2.imwrite("./videophotodump/img"+"_{0}".format(i) + ".png", frame) 14 | 15 | cap.release() -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cycler==0.10.0 2 | decorator==4.4.2 3 | djitellopy==1.5 4 | drawnow==0.72.5 5 | future==0.18.2 6 | GPy==1.9.9 7 | kiwisolver==1.2.0 8 | matplotlib==3.2.1 9 | numpy==1.18.4 10 | opencv-contrib-python==4.2.0.34 11 | pandas==1.0.3 12 | paramz==0.9.5 13 | Pillow==8.2.0 14 | pygame==1.9.6 15 | pyparsing==2.4.7 16 | python-dateutil==2.8.1 17 | pytz==2020.1 18 | safeopt==0.16 19 | scipy==1.5.2 20 | six==1.14.0 21 | -------------------------------------------------------------------------------- /tracking.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | IMAGE=tello:orientamento 3 | echo $IMAGE 4 | 5 | xhost + 6 | docker run --rm -it --privileged --ipc host --net host \ 7 | -e QT_X11_NO_MITSHM=1 \ 8 | -e DISPLAY=$DISPLAY \ 9 | -v /tmp/.X11-unix/:/tmp/.X11-unix \ 10 | -v ~/.Xauthority:/root/.Xauthority \ 11 | -e XAUTHORITY=/root/.Xauthority \ 12 | -v .:/root/tello_ws\ 13 | --name tello_tracking \ 14 | $IMAGE bash 15 | -------------------------------------------------------------------------------- /camera_calibration_tools/generator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | from cv2 import aruco 4 | import matplotlib.pyplot as plt 5 | import matplotlib as mpl 6 | 7 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) # dictionary selection 8 | ID = 42 # ID selection 9 | 10 | img = aruco.drawMarker(aruco_dict,ID, 150) # Marker generation 11 | 12 | plt.imshow(img, cmap = mpl.cm.gray, interpolation = "nearest") 13 | plt.savefig("./marker42.jpg") # saving a printable copy of the marker 14 | plt.show() 15 | -------------------------------------------------------------------------------- /controllers/BangBang_controller.py: -------------------------------------------------------------------------------- 1 | class BangBangController: 2 | def __init__(self, setpoint, tolerance, speed): 3 | self.setpoint = setpoint 4 | self.tolerance = tolerance 5 | self.speed = speed 6 | 7 | def compute_action(self, error): 8 | if error> self.tolerance: 9 | output = -self.speed 10 | 11 | elif error < - self.tolerance: 12 | 13 | output = self.speed 14 | else: 15 | output = 0 16 | 17 | return output 18 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # follow-me-drone 2 | my bachelor's degree thesis project about controlling a DJI Tello drone 3 | 4 | # i'm working on the docs, stay tuned ;) 5 | until then you can take a look at my jupyter notebooks here https://github.com/Nunzio03/follow-me-drone-notebooks 6 | 7 | # Docs for the future me :) 8 | 9 | ## Windows 10 | Install python3.7, create a venv, activate it, `python -m pip install --upgrade pip setuptools wheel` 11 | Then install requirements 12 | 13 | ## Linux 14 | Dockerfile available but on my linux pc i have some delay on the communication, probably due to my network card tho... -------------------------------------------------------------------------------- /camera_calibration_tools/calibration_generator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from mpl_toolkits.mplot3d import Axes3D 3 | import pandas as pd 4 | import cv2, PIL 5 | from cv2 import aruco 6 | import matplotlib.pyplot as plt 7 | import matplotlib as mpl 8 | 9 | 10 | workdir = "./" 11 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 12 | board = aruco.CharucoBoard_create(7, 5, 1, .8, aruco_dict) 13 | imboard = board.draw((2000, 2000)) 14 | cv2.imwrite(workdir + "chessboard.jpg", imboard) 15 | fig = plt.figure() 16 | ax = fig.add_subplot(1,1,1) 17 | plt.imshow(imboard, cmap = mpl.cm.gray, interpolation = "nearest") 18 | ax.axis("off") 19 | plt.show() -------------------------------------------------------------------------------- /docker_ws/Dockerfile.tello: -------------------------------------------------------------------------------- 1 | FROM python:3.7.17-bullseye 2 | 3 | RUN pip install GPy==1.9.9 4 | RUN pip install matplotlib==3.2.1 5 | RUN pip install cycler==0.10.0 6 | RUN pip install decorator==4.4.2 7 | RUN pip install djitellopy==1.5 8 | RUN pip install drawnow==0.72.5 9 | RUN pip install future==0.18.2 10 | RUN pip install kiwisolver==1.2.0 11 | RUN pip install safeopt==0.16 12 | RUN pip install numpy==1.18.4 13 | RUN pip install opencv-contrib-python==4.2.0.34 14 | RUN pip install pandas==1.0.3 15 | RUN pip install paramz==0.9.5 16 | RUN pip install Pillow==8.2.0 17 | RUN pip install pygame==1.9.6 18 | RUN pip install pyparsing==2.4.7 19 | RUN pip install python-dateutil==2.8.1 20 | RUN pip install pytz==2020.1 21 | RUN pip install scipy==1.5.2 22 | RUN pip install six==1.14.0 23 | -------------------------------------------------------------------------------- /camera_calibration_tools/Identificator.py: -------------------------------------------------------------------------------- 1 | # import numpy as np 2 | import cv2 3 | from cv2 import aruco 4 | # import matplotlib.pyplot as plt 5 | # import matplotlib as mpl 6 | video_capture = cv2.VideoCapture(0) 7 | while True: 8 | ret, frame = video_capture.read() 9 | 10 | cv2.imshow("video", frame) 11 | 12 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 13 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 14 | parameters = aruco.DetectorParameters_create() 15 | corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters) 16 | frame_markers = aruco.drawDetectedMarkers(frame.copy(), corners, ids) 17 | width = 2400 18 | dim = (width, int(width*3/4)) 19 | frame_markers = cv2.resize(frame_markers, dim, interpolation=cv2.INTER_AREA) 20 | 21 | 22 | cv2.imshow("markers", frame_markers) 23 | 24 | 25 | if cv2.waitKey(1) & 0xFF == ord('q'): # quit from script 26 | break 27 | 28 | -------------------------------------------------------------------------------- /PID_parameters_handler.py: -------------------------------------------------------------------------------- 1 | from controllers.PID_controller import PIDController 2 | 3 | 4 | class PIDTuner: 5 | def __init__(self, PIDx: PIDController, PIDy: PIDController, PIDz: PIDController): 6 | self.PIDx, self.PIDy, self.PIDz = PIDx, PIDy, PIDz 7 | self.current_pid = PIDx 8 | self.current_parameter = 'p' 9 | 10 | def get_pid(self, key): 11 | if key & 0xFF == ord("x"): 12 | self.current_pid = self.PIDx 13 | 14 | elif key & 0xFF == ord("y"): 15 | self.current_pid = self.PIDy 16 | 17 | elif key & 0xFF == ord("z"): 18 | self.current_pid = self.PIDz 19 | return self.current_pid 20 | 21 | def get_parameter(self, key): 22 | if key & 0xFF == ord("p"): 23 | 24 | self.current_parameter = 'p' 25 | 26 | elif key & 0xFF == ord("i"): 27 | self.current_parameter = 'i' 28 | 29 | elif key & 0xFF == ord("d"): 30 | self.current_parameter = 'd' 31 | return self.current_parameter 32 | 33 | def tune(self, key): 34 | if key & 0xFF == ord("0"): 35 | self.current_pid.set_gain(self.current_parameter, 0) 36 | elif key & 0xFF == ord("8"): 37 | self.current_pid.increase_gain(self.current_parameter, 0.001) 38 | elif key & 0xFF == ord("2"): 39 | self.current_pid.increase_gain(self.current_parameter, -0.001) 40 | -------------------------------------------------------------------------------- /tuning/plotAssistant.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | from drawnow import * 3 | import threading 4 | import time 5 | import math 6 | 7 | 8 | class PlotMeasurements (threading.Thread): 9 | def __init__(self, nome, t, x, y, z, ref): 10 | threading.Thread.__init__(self) 11 | self.t = t 12 | self.x = x 13 | self.y = y 14 | self.z = z 15 | self.ref = ref 16 | self.nome = nome 17 | plt.ion() 18 | font = {'family': 'normal', 19 | 'weight': 'bold', 20 | 'size': 12} 21 | 22 | plt.rc('font', **font) 23 | 24 | 25 | def plot(self): 26 | 27 | plt.plot(self.t, self.x, color=(1,0.1,0.1), label="x axis") 28 | plt.plot(self.t, self.y, color=(0.1,0.5,0.2,1), label="y_axis") 29 | plt.plot(self.t, self.z, color="blue", label="z_axis") 30 | plt.plot(self.t, self.ref, color="black", label="reference") 31 | plt.xlabel("Time [s]") 32 | plt.ylabel("Error [cm]") 33 | plt.legend(loc='best') 34 | 35 | def run(self): 36 | 37 | while True: 38 | 39 | drawnow(self.plot) 40 | 41 | 42 | class PlotAssistant: 43 | def __init__(self): 44 | self.t, self.x, self.y, self.z, self.ref = list(), list(), list(), list(), list() 45 | self.threadPlot = PlotMeasurements("plot measurements", self.t, self.x, self.y, self.z, self.ref) 46 | self.threadPlot.start() 47 | self.start = time.time() 48 | 49 | def insertMeasurement(self, ex, ey, ez): 50 | t = time.time() - self.start 51 | self.t.append(t) 52 | self.x.append(ex) 53 | self.y.append(ey) 54 | self.z.append(ez) 55 | self.ref.append(0) 56 | -------------------------------------------------------------------------------- /safeopt_example.py: -------------------------------------------------------------------------------- 1 | 2 | import GPy 3 | import numpy as np 4 | import math 5 | import safeopt 6 | 7 | 8 | # Measurement noise 9 | noise_var = 0.025 ** 2 10 | 11 | # Bounds on the inputs variable 12 | bounds = [(-5., 5.), (-5., 5.), (-5., 5.)] # riguarda gli input 13 | 14 | # Define Kernel 15 | kernel = GPy.kern.RBF(input_dim=len(bounds), variance=2., lengthscale=1.0, 16 | ARD=True) 17 | 18 | # Initial safe point 19 | x0 = np.zeros((1, len(bounds))) 20 | print(x0) 21 | x0 = np.array([[0.3, -0.3, 0]]) 22 | print(x0) 23 | 24 | # Generate function with safe initial point at x=0 25 | def sample_safe_fun(): 26 | while True: 27 | fun = safeopt.sample_gp_function(kernel, bounds, noise_var, 10) 28 | if fun([0,0,0], noise=False) > 0.5: 29 | break 30 | return fun 31 | 32 | def mia_funzione(arr): 33 | 34 | return math.cos(1.5+arr[0]+arr[1]+arr[2]) 35 | 36 | # Define the objective function 37 | fun = sample_safe_fun() 38 | 39 | # The statistical model of our objective function 40 | gp = GPy.models.GPRegression(x0, fun(x0), kernel, noise_var=noise_var) 41 | 42 | # The optimization routine 43 | opt = safeopt.SafeOptSwarm(gp, 0., bounds=bounds, threshold=0.2) 44 | # parameter_set = safeopt.linearly_spaced_combinations(bounds, 100) 45 | # opt = safeopt.SafeOpt(gp, parameter_set, 0., lipschitz=None, threshold=0.2) 46 | # Obtain next query point 47 | i=0 48 | while i<20: 49 | x_next = opt.optimize() 50 | # Get a measurement from the real system 51 | print(x_next) 52 | y_meas = mia_funzione(x_next) 53 | # Add this to the GP model 54 | opt.add_new_data_point(x_next, y_meas) 55 | i+=1 56 | print(x_next, y_meas) 57 | print("massimo") 58 | print(opt.get_maximum()) 59 | -------------------------------------------------------------------------------- /GUI/gui_drawer.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | 3 | 4 | class GuiDrawer: 5 | def __init__(self): 6 | pass 7 | 8 | @staticmethod 9 | def draw_errors(image, horizontal_error, vertical_error, frontal_error, distance): 10 | 11 | cv2.putText(image, "error x:" + str(horizontal_error), (50, 20), 5, 1, (0, 0, 250)) 12 | cv2.putText(image, "error y:" + str(vertical_error), (50, 40), 5, 1, (0, 0, 250)) 13 | cv2.putText(image, "error z:" + str(frontal_error), (50, 60), 5, 1, (0, 0, 250)) 14 | cv2.putText(image, "distance:" + str(distance), (50, 80), 5, 1, (0, 125, 255)) 15 | 16 | @staticmethod 17 | def draw_setpoint(image, setpointX, setpointY, setpointZ): 18 | cv2.circle(image, (int(setpointX), int(setpointY)), 12, (0, 0, 255), 3) 19 | cv2.putText(image, "setPoint Z:" + str(setpointZ) + "cm", (10, 260), 5, 1, (0, 255, 0)) 20 | 21 | @staticmethod 22 | def draw_current_PID(image, current_pid, current_parameter): 23 | cv2.putText(image, "PID:" + str(current_pid)+ " editing "+current_parameter, (10, 200), 5, 1, (0, 255, 0)) 24 | 25 | @staticmethod 26 | def draw_controller_output(image, out_x, out_y, out_z): 27 | cv2.putText(image, "action x:" + str(round(out_x, 3)), (450, 20), 5, 1, (250, 50, 50)) 28 | cv2.putText(image, "action y:" + str(round(out_y, 3)), (450, 40), 5, 1, (250, 50, 50)) 29 | cv2.putText(image, "action z:" + str(round(out_z, 3)), (450, 60), 5, 1, (250, 50, 50)) 30 | 31 | @staticmethod 32 | def draw_battery_level(image, battery): 33 | cv2.putText(image, "battery:" + str(battery).strip("\r\n")+"%", (10, 700), 5, 1, (0, 255, 0)) 34 | 35 | @staticmethod 36 | def draw_fitness_value(image, fitness): 37 | cv2.putText(image, "fitness:" + str(fitness), (700, 700), 5, 1, (0, 255, 0)) 38 | 39 | @staticmethod 40 | def draw_expiration_time(image, t): 41 | cv2.putText(image, "elapsed:" + str(t), (700, 350), 5, 1, (0, 255, 0)) 42 | -------------------------------------------------------------------------------- /tuning/SafeOptTunerAssistant.py: -------------------------------------------------------------------------------- 1 | import GPy 2 | import numpy as np 3 | import math 4 | import safeopt 5 | 6 | 7 | class Tuner: 8 | def __init__(self, noise_var, p0, i0, d0, pmax, imax, dmax): 9 | 10 | # Measurement noise 11 | self.noise_var = noise_var # 0.025 ** 2 12 | self.pmax, self.imax, self.dmax = pmax, imax, dmax 13 | 14 | # Bounds on the inputs variable 15 | bounds = [(0, self.pmax), (0, self.imax), (0, self.dmax)] # riguarda gli input 16 | 17 | # Define Kernel 18 | kernel = GPy.kern.RBF(input_dim=len(bounds), variance=2., lengthscale=1.0, 19 | ARD=True) 20 | 21 | # Initial safe point 22 | 23 | self.x0 = np.array([[p0 / pmax, i0 / imax, d0 / dmax]]) 24 | self.x_next = self.x0 25 | 26 | # Generate function with safe initial point at x=0 27 | def sample_safe_fun(): 28 | while True: 29 | fun = safeopt.sample_gp_function(kernel, bounds, noise_var, 10) 30 | if fun([0, 0, 0], noise=False) > 0.5: 31 | break 32 | return fun 33 | 34 | # Define the objective function 35 | fun = sample_safe_fun() 36 | 37 | # The statistical model of our objective function 38 | gp = GPy.models.GPRegression(self.x0, fun(self.x0), kernel, noise_var=noise_var) 39 | 40 | # The optimization routine 41 | self.opt = safeopt.SafeOptSwarm(gp, 0., bounds=bounds, threshold=0.2) 42 | 43 | # parameter_set = safeopt.linearly_spaced_combinations(bounds, 100) 44 | # opt = safeopt.SafeOpt(gp, parameter_set, 0., lipschitz=None, threshold=0.2) 45 | # Obtain next query point 46 | 47 | def get_param(self): 48 | 49 | self.x_next = self.opt.optimize() 50 | return [self.x_next[0], self.x_next[1], self.x_next[2]] 51 | 52 | def optimize(self, measurement): 53 | y_meas = measurement 54 | self.opt.add_new_data_point(self.x_next, y_meas) 55 | 56 | def get_best_param(self): 57 | print(self.opt.get_maximum()) 58 | -------------------------------------------------------------------------------- /controllers/PID_controller.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | 4 | def same_sign(a, b): 5 | if a == 0 or b == 0: 6 | return True 7 | elif a/abs(a) == b/abs(b): 8 | return True 9 | else: 10 | return False 11 | 12 | 13 | class PIDController: 14 | def __init__(self, identifier): 15 | self.kp, self.ki, self.kd = 0, 0, 0 16 | self.identifier = identifier 17 | self.previous_error = 0 18 | self.integral = 0 19 | self.start_time = time.time() 20 | self.is_in_saturation = False 21 | self.previous_output = 0 22 | 23 | def increase_gain(self, parameter, value): 24 | if parameter == "p": 25 | self.kp += value 26 | elif parameter == "i": 27 | self.ki += value 28 | elif parameter == "d": 29 | self.kd += value 30 | 31 | def set_PID_safeopt(self, param): 32 | self.kp = param[0] 33 | self.ki = param[1] 34 | self.kd = param[2] 35 | self.previous_error = 0 36 | self.integral = 0 37 | self.start_time = time.time() 38 | self.is_in_saturation = False 39 | self.previous_output = 0 40 | 41 | def set_gain(self, parameter, value): 42 | if parameter == "p": 43 | self.kp = value 44 | elif parameter == "i": 45 | self.ki = value 46 | elif parameter == "d": 47 | self.kd = value 48 | 49 | def compute_action(self, error): 50 | delay_pid = time.time() - self.start_time 51 | self.start_time = time.time() 52 | 53 | if not(self.is_in_saturation and same_sign(error, self.previous_output)): 54 | self.integral = self.integral + error * delay_pid 55 | 56 | derivative = (error - self.previous_error) / delay_pid 57 | 58 | output = self.kp * error + self.ki * self.integral + self.kd * derivative 59 | 60 | self.previous_error = error 61 | if output > 100: 62 | output = 100 63 | self.is_in_saturation = True 64 | elif output < -100: 65 | output = -100 66 | self.is_in_saturation = True 67 | else: 68 | self.is_in_saturation = False 69 | self.previous_output = output 70 | return output 71 | 72 | def __str__(self): 73 | return f'{self.identifier} :[ P:{self.kp}, I:{self.ki}, D:{self.kd} ]' 74 | -------------------------------------------------------------------------------- /camera_calibration_tools/camera_calibration.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2, PIL, os 3 | from cv2 import aruco 4 | import matplotlib.pyplot as plt 5 | 6 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 7 | board = aruco.CharucoBoard_create(7, 5, 1, .8, aruco_dict) 8 | datadir = "./drone calibration photos/" 9 | 10 | images = np.array([datadir + f for f in os.listdir(datadir) if f.endswith(".png") ]) 11 | 12 | 13 | def read_chessboards(images): 14 | #reading the chessboard 15 | print("POSE ESTIMATION STARTED...") 16 | allCorners = [] 17 | allIds = [] 18 | decimator = 0 19 | # SUB PIXEL CORNER DETECTION CRITERION 20 | criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.00001) 21 | 22 | for im in images: 23 | # print("=> Processing image {0}".format(im)) 24 | print('.', end="") 25 | frame = cv2.imread(im) 26 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 27 | corners, ids, rejectedImgPoints = cv2.aruco.detectMarkers(gray, aruco_dict) 28 | 29 | if len(corners)>0: 30 | # SUB PIXEL DETECTION 31 | for corner in corners: 32 | cv2.cornerSubPix(gray, corner, 33 | winSize = (3,3), 34 | zeroZone = (-1,-1), 35 | criteria = criteria) 36 | res2 = cv2.aruco.interpolateCornersCharuco(corners,ids,gray,board) 37 | if res2[1] is not None and res2[2] is not None and len(res2[1])>3 and decimator%1==0: 38 | allCorners.append(res2[1]) 39 | allIds.append(res2[2]) 40 | 41 | decimator+=1 42 | 43 | imsize = gray.shape 44 | return allCorners,allIds,imsize 45 | 46 | allCorners,allIds,imsize=read_chessboards(images) 47 | 48 | 49 | def calibrate_camera(allCorners,allIds,imsize): 50 | """ 51 | Calibrates the camera using the dected corners. 52 | """ 53 | 54 | cameraMatrixInit = np.array([[ 1000., 0., imsize[0]/2.], 55 | [ 0., 1000., imsize[1]/2.], 56 | [ 0., 0., 1.]]) 57 | 58 | distCoeffsInit = np.zeros((5,1)) 59 | flags = (cv2.CALIB_USE_INTRINSIC_GUESS + cv2.CALIB_RATIONAL_MODEL + cv2.CALIB_FIX_ASPECT_RATIO) 60 | 61 | (ret, camera_matrix, distortion_coefficients0, 62 | rotation_vectors, translation_vectors, 63 | stdDeviationsIntrinsics, stdDeviationsExtrinsics, 64 | perViewErrors) = cv2.aruco.calibrateCameraCharucoExtended( 65 | charucoCorners=allCorners, 66 | charucoIds=allIds, 67 | board=board, 68 | imageSize=imsize, 69 | cameraMatrix=cameraMatrixInit, 70 | distCoeffs=distCoeffsInit, 71 | flags=flags, 72 | criteria=(cv2.TERM_CRITERIA_EPS & cv2.TERM_CRITERIA_COUNT, 10000, 1e-9)) 73 | 74 | return ret, camera_matrix, distortion_coefficients0, rotation_vectors, translation_vectors 75 | 76 | 77 | ret, mtx, dist, rvecs, tvecs = calibrate_camera(allCorners,allIds,imsize) 78 | print("\n CALIBRATION COMPLETED") 79 | -------------------------------------------------------------------------------- /feedback.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from cv2 import aruco 3 | import math 4 | 5 | 6 | class MarkerDetector: 7 | 8 | def __init__(self, aruco_dict, mtx, dist): 9 | self.aruco_dict = aruco_dict 10 | self.parameters = aruco.DetectorParameters_create() 11 | self.mtx = mtx 12 | self.dist = dist 13 | 14 | def detect(self, frame, target): 15 | gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 16 | 17 | corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, self.aruco_dict, parameters=self.parameters) 18 | # SUB PIXEL DETECTION 19 | criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.0001) 20 | 21 | for corner in corners: 22 | cv2.cornerSubPix(gray, corner, winSize=(3, 3), zeroZone=(-1, -1), criteria=criteria) 23 | 24 | size_of_marker = 0.15 # side lenght of the marker in meters 25 | rvecs, tvecs, _objPoints = aruco.estimatePoseSingleMarkers(corners, size_of_marker, self.mtx, self.dist) 26 | 27 | length_of_axis = 0.1 28 | imaxis = aruco.drawDetectedMarkers(frame.copy(), corners, ids) 29 | #print(ids, corners) 30 | if tvecs is not None: 31 | target_id = None 32 | for i in range(len(ids)): 33 | if ids[i] == target: 34 | target_id = i 35 | 36 | if target_id is not None: 37 | imaxis = aruco.drawAxis(imaxis, self.mtx, self.dist, rvecs[target_id], tvecs[target_id], length_of_axis) 38 | x = (corners[target_id][0][0][0] + corners[target_id][0][2][0]) / 2 39 | y = (corners[target_id][0][0][1] + corners[target_id][0][2][1]) / 2 40 | square_side_dimension_px = math.sqrt(math.pow(corners[target_id][0][3][1] - corners[target_id][0][0][1], 2) + math.pow(corners[target_id][0][3][0] - corners[target_id][0][0][0], 2)) 41 | 42 | return imaxis, x, y, square_side_dimension_px 43 | else: 44 | return frame, None, None, None 45 | else: 46 | return frame, None, None, None 47 | 48 | def detect_and_compute_error_values(self, frame, set_point_x, set_point_y, set_point_z, target): 49 | 50 | imaxis, x, y, square_side_dimension_px = self.detect(frame, target) 51 | if x is not None: 52 | horizontal_error, vertical_error, frontal_error = compute_error_values(x, y, square_side_dimension_px, set_point_x, set_point_y, set_point_z) 53 | return imaxis, horizontal_error, vertical_error, frontal_error 54 | else: 55 | return imaxis, None, None, None 56 | 57 | 58 | def distance_estimator(square_side_dimension_px): 59 | return 1.129e+04 * math.pow(square_side_dimension_px, -0.9631) + (-11.26) 60 | 61 | 62 | def compute_error_values(x, y, square_side_dimension_px, set_point_x, set_point_y, set_point_z): 63 | 64 | frontal_distance_cm = int(distance_estimator(square_side_dimension_px)) 65 | cm_pix_ratio = 15 / square_side_dimension_px # 15 [cm] / marker dimension [pixel] 66 | horizontal_error = int((set_point_x - x) * cm_pix_ratio) 67 | vertical_error = int((set_point_y - y) * cm_pix_ratio) 68 | frontal_error = frontal_distance_cm - set_point_z 69 | return horizontal_error, vertical_error, frontal_error 70 | -------------------------------------------------------------------------------- /tuning/measurementAssistant.py: -------------------------------------------------------------------------------- 1 | import time 2 | import os 3 | import matplotlib.pyplot as plt 4 | from drawnow import * 5 | import threading 6 | 7 | 8 | class PlotMeasurements (threading.Thread): 9 | def __init__(self, nome, x, y): 10 | threading.Thread.__init__(self) 11 | self.x = x 12 | self.y = y 13 | self.nome = nome 14 | plt.ion() 15 | 16 | def plot(self): 17 | 18 | plt.plot(self.x, self.y) 19 | 20 | def run(self): 21 | 22 | while True: 23 | 24 | drawnow(self.plot) 25 | 26 | 27 | class MeasurementAssistant: 28 | def __init__(self, name, tolerance, t_threshold, parameters0): 29 | localtime = time.localtime() 30 | self.folder = name+str(localtime.tm_mday)+str(localtime.tm_mon)+"_"+str(localtime.tm_hour)+str(localtime.tm_min) 31 | self.name = name 32 | self.tolerance = tolerance 33 | self.t_threshold = t_threshold 34 | self.previousParameters = parameters0 35 | 36 | self.x, self.y = list(), list() 37 | 38 | self.in_position = False 39 | self.n_overshoots = 0 40 | self.last_sign = None 41 | self.start_round_time = time.time() 42 | self.time_normalization = self.start_round_time 43 | self.round_counter = 0 44 | self.filename = "round" + str(self.round_counter) 45 | self.parameter_fitness_filename = "plot.csv" 46 | print(os.getcwd()) 47 | try: 48 | os.chdir("measurement logs") 49 | print(os.getcwd()) 50 | print("i'm in logs") 51 | 52 | except: 53 | pass 54 | try: 55 | os.mkdir(self.folder) 56 | print(os.getcwd()) 57 | print("i'm in my folder") 58 | except: 59 | pass 60 | try: 61 | os.chdir("..") 62 | print(os.getcwd()) 63 | print("i'm in the project folder") 64 | except: 65 | pass 66 | 67 | def write_measurement(self, value): 68 | t = time.time() 69 | self.x.append(t) 70 | self.y.append(value) 71 | 72 | f = open("measurement logs/"+self.folder+"/"+self.filename+".csv", "a") 73 | f.write(str(t-self.time_normalization)+";"+ str(value)+ '\n') 74 | f.close() 75 | 76 | self.count_oveshoots(value) 77 | 78 | self.arrived_routine(value, t) 79 | 80 | def new_round(self, parameters, fitness): 81 | 82 | f = open("measurement logs/"+self.folder+"/"+self.parameter_fitness_filename, "a") 83 | f.write(str(self.previousParameters[0])+","+str(self.previousParameters[1])+","+str(self.previousParameters[2]) 84 | +","+str(fitness)+'\n') 85 | f = open("measurement logs/"+self.folder+"/"+self.filename+".csv", "a") 86 | 87 | f.write("Fitness:" +str(fitness)+'\n') 88 | 89 | f.close() 90 | self.round_counter += 1 91 | self.filename = "round" + str(self.round_counter) 92 | f = open("measurement logs/" + self.folder + "/" + self.filename + ".csv", "a") 93 | f.write("______________________NEW ROUND________________________"+'\n') 94 | f.write(str(parameters) + '\n') 95 | f.close() 96 | self.x.clear(), self.y.clear() 97 | 98 | self.start_round_time = time.time() 99 | self.previousParameters = parameters 100 | 101 | def compute_abs_mean(self): 102 | 103 | s = 0 104 | if len(self.y) > 0: 105 | for n in self.y: 106 | s += abs(n) 107 | s = s/len(self.y) 108 | return s 109 | 110 | def compute_abs_sum(self): 111 | s = 0 112 | if len(self.y) > 0: 113 | for n in self.y: 114 | s += abs(n) 115 | return s 116 | 117 | def is_in_setpoint(self): 118 | if self.in_position: 119 | 120 | if time.time() - self.start_arrived_time >= self.t_threshold: 121 | self.in_position = False 122 | return True 123 | else: 124 | return False 125 | 126 | def arrived_routine(self, value, t): 127 | if abs(value) < self.tolerance: 128 | if not self.in_position: 129 | self.start_arrived_time = t 130 | self.in_position = True 131 | 132 | else: 133 | self.in_position = False 134 | 135 | def count_oveshoots(self, value): 136 | if value != 0: 137 | sign = value/abs(value) 138 | if sign != self.last_sign: 139 | self.n_overshoots +=1 140 | self.last_sign = sign 141 | 142 | def fitness(self): 143 | elapsed_time = time.time() - self.start_round_time 144 | a = 0.7*elapsed_time+0.2*self.n_overshoots+0.1*self.compute_abs_sum() 145 | return 8000/(80+a) 146 | -------------------------------------------------------------------------------- /followMeDrone.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import time 4 | from cv2 import aruco 5 | from djitellopy import Tello 6 | from GUI.gui_drawer import GuiDrawer 7 | 8 | from controllers.PID_controller import PIDController as PID 9 | from feedback import MarkerDetector 10 | from PID_parameters_handler import PIDTuner 11 | from controllers.BangBang_controller import BangBangController 12 | # from tuning.plotAssistant import PlotAssistant 13 | 14 | start_time = time.time() 15 | 16 | # measurementplot= PlotAssistant() 17 | 18 | DRONE_SPEED_X = 25 19 | DRONE_SPEED_Y = 30 20 | DRONE_SPEED_Z = 25 21 | 22 | 23 | SET_POINT_X = 960 / 2 24 | SET_POINT_Y = 720 / 2 25 | SET_POINT_Z_cm = 170 26 | 27 | # pid section 28 | pidX = PID('x') 29 | pidY = PID('y') 30 | pidZ = PID('z') 31 | 32 | # pid setup 33 | pidX.set_PID_safeopt([0.57531093, 0.02, 0.17326167]) 34 | pidY.set_PID_safeopt([0.7, 0.02, 0.27326167]) 35 | pidZ.set_PID_safeopt([0.5, 0.02, 0.15]) 36 | # auxiliary controllers 37 | cx = BangBangController(SET_POINT_X, 20, 30) 38 | cy = BangBangController(SET_POINT_Y, 20, 30) 39 | cz = BangBangController(SET_POINT_Z_cm, 20, 25) 40 | # pid keys 41 | tuner = PIDTuner(pidX, pidY, pidZ) 42 | drawer = GuiDrawer() 43 | current_pid, current_parameter = pidX, 'p' 44 | 45 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 46 | 47 | # video source and calibration parameters setup 48 | 49 | video_capture = cv2.VideoCapture(0) 50 | drone = Tello() 51 | drone.connect() 52 | battery_level = drone.get_battery() 53 | drone.streamon() 54 | 55 | # detection 56 | 57 | pc_mtx = np.array([[1.73223258e+03, 0.00000000e+00, 1.27300230e+03], 58 | [0.00000000e+00, 1.73223258e+03, 1.03042217e+03], 59 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 60 | 61 | pc_dist = np.array([[-3.89477169e+00], 62 | [-9.72135202e-02], 63 | [1.04299558e-02], 64 | [8.40170760e-05], 65 | [-2.17736443e+00], 66 | [-3.89139953e+00], 67 | [-1.90794581e-01], 68 | [-1.85298591e+00], 69 | [0.00000000e+00], 70 | [0.00000000e+00], 71 | [0.00000000e+00], 72 | [0.00000000e+00], 73 | [0.00000000e+00], 74 | [0.00000000e+00]]) 75 | 76 | drone_mtx = np.array([[1.74213359e+03, 0.00000000e+00, 1.27150514e+03], 77 | [0.00000000e+00, 1.74213359e+03, 1.02516982e+03], 78 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 79 | 80 | drone_dist = np.array([[-1.69684883e+00], 81 | [-6.85717812e+00], 82 | [9.93624014e-03], 83 | [6.20144084e-04], 84 | [-1.18739065e+01], 85 | [-1.69460711e+00], 86 | [-6.99110211e+00], 87 | [-1.13633464e+01], 88 | [0.00000000e+00], 89 | [0.00000000e+00], 90 | [0.00000000e+00], 91 | [0.00000000e+00], 92 | [0.00000000e+00], 93 | [0.00000000e+00]]) 94 | 95 | mtx, dist = drone_mtx, drone_dist 96 | 97 | detector = MarkerDetector(aruco_dict, mtx, dist) 98 | 99 | # loop start 100 | drone.takeoff() 101 | while True: 102 | action_z, action_y, action_x = 0, 0, 0 103 | # ret, frame = video_capture.read() 104 | 105 | frame = drone.get_frame_read().frame 106 | 107 | image, horizontal_error, vertical_error, frontal_error = detector.detect_and_compute_error_values(frame, 108 | SET_POINT_X, 109 | SET_POINT_Y, 110 | SET_POINT_Z_cm, 111 | 42) 112 | 113 | if horizontal_error is not None: 114 | # measurementplot.insertMeasurement(horizontal_error, vertical_error, frontal_error) 115 | drawer.draw_errors(image, horizontal_error, vertical_error, frontal_error, frontal_error+SET_POINT_Z_cm) 116 | action_x = int(-pidX.compute_action(horizontal_error)) 117 | action_y = int(pidY.compute_action(vertical_error)) 118 | action_z = int(pidZ.compute_action(frontal_error)) 119 | # action_x = cx.compute_action(horizontal_error) 120 | # action_y = cy.compute_action(-vertical_error) 121 | # action_z = cz.compute_action(-frontal_error) 122 | drawer.draw_controller_output(image, action_x, action_y, action_z) 123 | 124 | drone.send_rc_control(0, action_z, action_y, action_x) # turn with yaw 125 | 126 | drawer.draw_current_PID(image, current_pid, current_parameter) 127 | drawer.draw_setpoint(image, SET_POINT_X, SET_POINT_Y, SET_POINT_Z_cm) 128 | if time.time() - start_time >= 5: 129 | battery_level = drone.get_battery() 130 | start_time = time.time() 131 | drawer.draw_battery_level(image, battery_level) 132 | 133 | window_dimension = 0.3 134 | 135 | width = 2400*window_dimension 136 | ratio = 16 / 9 137 | dim = (int(width), int(width / ratio)) 138 | image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) 139 | 140 | cv2.imshow("markers", image) 141 | 142 | # key hadling 143 | key_pressed = cv2.waitKey(1) 144 | 145 | if key_pressed & 0xFF == ord('q'): # quit from script 146 | drone.land() 147 | drone.get_battery() 148 | break 149 | 150 | else: 151 | current_pid = tuner.get_pid(key_pressed) 152 | current_parameter = tuner.get_parameter(key_pressed) 153 | tuner.tune(key_pressed) 154 | -------------------------------------------------------------------------------- /tuning/tuning_x_axis_with_SafeOpt.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import time 4 | from cv2 import aruco 5 | from djitellopy import Tello 6 | from GUI.gui_drawer import GuiDrawer 7 | 8 | from controllers.PID_controller import PIDController as PID 9 | from feedback import MarkerDetector 10 | from controllers.BangBang_controller import BangBangController 11 | from tuning import measurementAssistant, SafeOptTunerAssistant 12 | 13 | start_time = time.time() 14 | 15 | DRONE_SPEED_X = 25 16 | DRONE_SPEED_Y = 30 17 | DRONE_SPEED_Z = 25 18 | 19 | 20 | SET_POINT_X = 960 / 2 21 | SET_POINT_Y = 720 / 2 22 | SET_POINT_Z_cm = 200 23 | 24 | # pid section 25 | pidX = PID('x') 26 | 27 | # pid setup 28 | pid_safeopt_param = [0.3, 0.02, 0.3] 29 | pid_safeopt_max = [0.8, 0.2, 0.4] 30 | 31 | # pid_safeopt_param = [0.61385034, 0.0002, 0.289428] 32 | # (ObsAr([0.66659107, 0.02 , 0.3 ]), ObsAr([16.12644424])) 33 | # (ObsAr([0.61385034, 0.02 , 0.289428 ]), ObsAr([18.31138782])) 34 | # (ObsAr([0.72632365, 0.002 , 0.13493126]), ObsAr([18.8394745])) 35 | # (ObsAr([0.57531093, 0.02 , 0.17326167]), ObsAr([20.36025728])) 36 | # (ObsAr([1. , 0. , 0.54845742]), ObsAr([18.35382209])) pid_safeopt_max = [0.8, 0.2, 0.4] 37 | pidX.set_PID_safeopt(pid_safeopt_param) 38 | # auxiliary controllers 39 | cx = BangBangController(SET_POINT_X, 25, DRONE_SPEED_X) 40 | cy = BangBangController(SET_POINT_Y, 25, DRONE_SPEED_Y) 41 | cz = BangBangController(SET_POINT_Z_cm, 25, DRONE_SPEED_Z) 42 | # pid keys 43 | 44 | drawer = GuiDrawer() 45 | current_pid, current_parameter = pidX, 'p' 46 | 47 | # measurements 48 | measure42 = measurementAssistant.MeasurementAssistant("x_axis42", 25, 3, pid_safeopt_param) 49 | measure33 = measurementAssistant.MeasurementAssistant("x_axis33", 25, 3, pid_safeopt_param) 50 | # safeopt assistant 51 | 52 | safeoptassistant = SafeOptTunerAssistant.Tuner(0.0025 ** 2, pid_safeopt_param[0], pid_safeopt_param[1], 53 | pid_safeopt_param[2], pid_safeopt_max[0], pid_safeopt_max[1], 54 | pid_safeopt_max[2]) 55 | target = 33 56 | tuning_mode = False 57 | last_fitness = 0 58 | success = False 59 | 60 | 61 | def switchTarget(): 62 | if target == 33: 63 | return 42 64 | elif target == 42: 65 | return 33 66 | 67 | 68 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 69 | 70 | # video source and calibration parameters setup 71 | 72 | # video_capture = cv2.VideoCapture(0) 73 | drone = Tello() 74 | drone.connect() 75 | battery_level = drone.get_battery() 76 | drone.streamon() 77 | 78 | # detection 79 | 80 | pc_mtx = np.array([[1.73223258e+03, 0.00000000e+00, 1.27300230e+03], 81 | [0.00000000e+00, 1.73223258e+03, 1.03042217e+03], 82 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 83 | 84 | pc_dist = np.array([[-3.89477169e+00], 85 | [-9.72135202e-02], 86 | [1.04299558e-02], 87 | [8.40170760e-05], 88 | [-2.17736443e+00], 89 | [-3.89139953e+00], 90 | [-1.90794581e-01], 91 | [-1.85298591e+00], 92 | [0.00000000e+00], 93 | [0.00000000e+00], 94 | [0.00000000e+00], 95 | [0.00000000e+00], 96 | [0.00000000e+00], 97 | [0.00000000e+00]]) 98 | 99 | drone_mtx = np.array([[1.74213359e+03, 0.00000000e+00, 1.27150514e+03], 100 | [0.00000000e+00, 1.74213359e+03, 1.02516982e+03], 101 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 102 | 103 | drone_dist = np.array([[-1.69684883e+00], 104 | [-6.85717812e+00], 105 | [9.93624014e-03], 106 | [6.20144084e-04], 107 | [-1.18739065e+01], 108 | [-1.69460711e+00], 109 | [-6.99110211e+00], 110 | [-1.13633464e+01], 111 | [0.00000000e+00], 112 | [0.00000000e+00], 113 | [0.00000000e+00], 114 | [0.00000000e+00], 115 | [0.00000000e+00], 116 | [0.00000000e+00]]) 117 | 118 | mtx, dist = drone_mtx, drone_dist 119 | 120 | detector = MarkerDetector(aruco_dict, mtx, dist) 121 | 122 | # loop start 123 | drone.takeoff() 124 | while True: 125 | 126 | # ret, frame = video_capture.read() 127 | 128 | frame = drone.get_frame_read().frame 129 | 130 | image, horizontal_error, vertical_error, frontal_error = detector.detect_and_compute_error_values(frame, 131 | SET_POINT_X, 132 | SET_POINT_Y, 133 | SET_POINT_Z_cm, 134 | target) 135 | action_x, action_y, action_z = 30, 0, 0 136 | 137 | if horizontal_error is not None: 138 | drawer.draw_errors(image, horizontal_error, vertical_error, frontal_error, frontal_error+SET_POINT_Z_cm) 139 | 140 | # action_y = int(pidY.compute_action(vertical_error)) 141 | # action_z = int(pidZ.compute_action(frontal_error)) 142 | 143 | action_y = cy.compute_action(-vertical_error) 144 | action_z = cz.compute_action(-frontal_error) 145 | 146 | if tuning_mode: 147 | action_x = int(-pidX.compute_action(horizontal_error)) 148 | else: 149 | action_x = cx.compute_action(horizontal_error) 150 | 151 | if target == 33: 152 | measure33.write_measurement(horizontal_error) 153 | if measure33.is_in_setpoint(): 154 | target = switchTarget() 155 | start_time = time.time() 156 | measure33.new_round(pid_safeopt_param, last_fitness) 157 | measure42.new_round(pid_safeopt_param, last_fitness) 158 | tuning_mode = True 159 | last_fitness = 0 160 | 161 | elif target == 42: 162 | 163 | print(time.time() - start_time) 164 | measure42.write_measurement(horizontal_error) 165 | if measure42.is_in_setpoint(): 166 | last_fitness = measure42.fitness() 167 | safeoptassistant.optimize(last_fitness) 168 | pid_safeopt_param = safeoptassistant.get_param() 169 | pidX.set_PID_safeopt(pid_safeopt_param) 170 | target = switchTarget() 171 | tuning_mode = False 172 | 173 | if time.time() - start_time >= 15: 174 | start_time = time.time() 175 | if target == 42: 176 | safeoptassistant.optimize(0) 177 | pid_safeopt_param = safeoptassistant.get_param() 178 | pidX.set_PID_safeopt(pid_safeopt_param) 179 | target = switchTarget() 180 | tuning_mode = False 181 | last_fitness = 0 182 | 183 | drone.send_rc_control(0, action_z, action_y, action_x) # turn with yaw 184 | if target == 42: 185 | drawer.draw_fitness_value(image, measure42.fitness()) 186 | 187 | drawer.draw_controller_output(image, action_x, action_y, action_z) 188 | drawer.draw_current_PID(image, current_pid, current_parameter) 189 | drawer.draw_setpoint(image, SET_POINT_X, SET_POINT_Y, SET_POINT_Z_cm) 190 | 191 | if time.time() - start_time >= 5: 192 | battery_level = drone.get_battery() 193 | 194 | drawer.draw_battery_level(image, battery_level) 195 | drawer.draw_expiration_time(image, time.time()-start_time) 196 | 197 | width = 2200/2 198 | ratio = 16 / 9 199 | dim = (int(width), int(width / ratio)) 200 | image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) 201 | 202 | cv2.imshow("markers", image) 203 | 204 | # key hadling 205 | key_pressed = cv2.waitKey(1) 206 | 207 | if key_pressed & 0xFF == ord('q'): # quit from script 208 | drone.land() 209 | drone.get_battery() 210 | print(safeoptassistant.get_best_param()) 211 | break 212 | -------------------------------------------------------------------------------- /tuning/tuning_z_axis_with_SafeOpt.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import time 4 | from cv2 import aruco 5 | from djitellopy import Tello 6 | from GUI.gui_drawer import GuiDrawer 7 | 8 | from controllers.PID_controller import PIDController as PID 9 | from feedback import MarkerDetector 10 | from controllers.BangBang_controller import BangBangController 11 | from tuning import measurementAssistant, SafeOptTunerAssistant 12 | 13 | start_time = time.time() 14 | 15 | DRONE_SPEED_X = 25 16 | DRONE_SPEED_Y = 30 17 | DRONE_SPEED_Z = 25 18 | 19 | 20 | SET_POINT_X = 960 / 2 21 | SET_POINT_Y = 720 / 2 22 | SET_POINT_Z_cm = 200 23 | 24 | # pid section 25 | pidZ = PID('z') 26 | 27 | # pid setup 28 | pid_safeopt_param = [0.3, 0.02, 0.3] 29 | pid_safeopt_max = [0.8, 0.2, 0.4] 30 | 31 | # pid_safeopt_param = [0.61385034, 0.0002, 0.289428] 32 | # (ObsAr([0.66659107, 0.02 , 0.3 ]), ObsAr([16.12644424])) 33 | # (ObsAr([0.61385034, 0.02 , 0.289428 ]), ObsAr([18.31138782])) 34 | # (ObsAr([0.72632365, 0.002 , 0.13493126]), ObsAr([18.8394745])) 35 | # (ObsAr([0.57531093, 0.02 , 0.17326167]), ObsAr([20.36025728])) 36 | # (ObsAr([1. , 0. , 0.54845742]), ObsAr([18.35382209])) pid_safeopt_max = [0.8, 0.2, 0.4] 37 | pidZ.set_PID_safeopt(pid_safeopt_param) 38 | # auxiliary controllers 39 | cx = BangBangController(SET_POINT_X, 25, DRONE_SPEED_X) 40 | cy = BangBangController(SET_POINT_Y, 25, DRONE_SPEED_Y) 41 | cz = BangBangController(SET_POINT_Z_cm, 25, DRONE_SPEED_Z) 42 | # pid keys 43 | 44 | drawer = GuiDrawer() 45 | current_pid, current_parameter = pidZ, 'p' 46 | 47 | # measurements 48 | measure42 = measurementAssistant.MeasurementAssistant("z_axis42", 25, 3, pid_safeopt_param) 49 | measure33 = measurementAssistant.MeasurementAssistant("z_axis33", 25, 3, pid_safeopt_param) 50 | # safeopt assistant 51 | 52 | safeoptassistant = SafeOptTunerAssistant.Tuner(0.0025 ** 2, pid_safeopt_param[0], pid_safeopt_param[1], 53 | pid_safeopt_param[2], pid_safeopt_max[0], pid_safeopt_max[1], 54 | pid_safeopt_max[2]) 55 | target = 42 56 | tuning_mode = False 57 | last_fitness = 0 58 | success = False 59 | 60 | 61 | def switchSetPoint(): 62 | if SET_POINT_Z_cm == 200: 63 | return 150 64 | elif SET_POINT_Z_cm == 150: 65 | return 200 66 | 67 | 68 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 69 | 70 | # video source and calibration parameters setup 71 | 72 | # video_capture = cv2.VideoCapture(0) 73 | drone = Tello() 74 | drone.connect() 75 | battery_level = drone.get_battery() 76 | drone.streamon() 77 | 78 | # detection 79 | 80 | pc_mtx = np.array([[1.73223258e+03, 0.00000000e+00, 1.27300230e+03], 81 | [0.00000000e+00, 1.73223258e+03, 1.03042217e+03], 82 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 83 | 84 | pc_dist = np.array([[-3.89477169e+00], 85 | [-9.72135202e-02], 86 | [1.04299558e-02], 87 | [8.40170760e-05], 88 | [-2.17736443e+00], 89 | [-3.89139953e+00], 90 | [-1.90794581e-01], 91 | [-1.85298591e+00], 92 | [0.00000000e+00], 93 | [0.00000000e+00], 94 | [0.00000000e+00], 95 | [0.00000000e+00], 96 | [0.00000000e+00], 97 | [0.00000000e+00]]) 98 | 99 | drone_mtx = np.array([[1.74213359e+03, 0.00000000e+00, 1.27150514e+03], 100 | [0.00000000e+00, 1.74213359e+03, 1.02516982e+03], 101 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 102 | 103 | drone_dist = np.array([[-1.69684883e+00], 104 | [-6.85717812e+00], 105 | [9.93624014e-03], 106 | [6.20144084e-04], 107 | [-1.18739065e+01], 108 | [-1.69460711e+00], 109 | [-6.99110211e+00], 110 | [-1.13633464e+01], 111 | [0.00000000e+00], 112 | [0.00000000e+00], 113 | [0.00000000e+00], 114 | [0.00000000e+00], 115 | [0.00000000e+00], 116 | [0.00000000e+00]]) 117 | 118 | mtx, dist = drone_mtx, drone_dist 119 | 120 | detector = MarkerDetector(aruco_dict, mtx, dist) 121 | 122 | # loop start 123 | #drone.takeoff() 124 | while True: 125 | 126 | # ret, frame = video_capture.read() 127 | 128 | frame = drone.get_frame_read().frame 129 | 130 | image, horizontal_error, vertical_error, frontal_error = detector.detect_and_compute_error_values(frame, 131 | SET_POINT_X, 132 | SET_POINT_Y, 133 | SET_POINT_Z_cm, 134 | target) 135 | action_x, action_y, action_z = 0, 0, 0 136 | 137 | if horizontal_error is not None: 138 | drawer.draw_errors(image, horizontal_error, vertical_error, frontal_error, frontal_error+SET_POINT_Z_cm) 139 | 140 | # action_y = int(pidY.compute_action(vertical_error)) 141 | # action_z = int(pidZ.compute_action(frontal_error)) 142 | action_x = cx.compute_action(horizontal_error) 143 | action_y = cy.compute_action(-vertical_error) 144 | #action_z = cz.compute_action(-frontal_error) 145 | 146 | if tuning_mode: 147 | action_z = int(pidZ.compute_action(frontal_error)) 148 | else: 149 | action_z = cz.compute_action(frontal_error) 150 | 151 | if SET_POINT_Z_cm == 200: 152 | measure33.write_measurement(frontal_error) 153 | if measure33.is_in_setpoint(): 154 | target = switchSetPoint() 155 | start_time = time.time() 156 | measure33.new_round(pid_safeopt_param, last_fitness) 157 | measure42.new_round(pid_safeopt_param, last_fitness) 158 | tuning_mode = True 159 | last_fitness = 0 160 | 161 | elif SET_POINT_Z_cm == 150: 162 | 163 | print(time.time() - start_time) 164 | measure42.write_measurement(horizontal_error) 165 | if measure42.is_in_setpoint(): 166 | last_fitness = measure42.fitness() 167 | safeoptassistant.optimize(last_fitness) 168 | pid_safeopt_param = safeoptassistant.get_param() 169 | pidZ.set_PID_safeopt(pid_safeopt_param) 170 | target = switchSetPoint() 171 | tuning_mode = False 172 | 173 | if time.time() - start_time >= 7: 174 | start_time = time.time() 175 | if SET_POINT_Z_cm == 150: 176 | safeoptassistant.optimize(0) 177 | pid_safeopt_param = safeoptassistant.get_param() 178 | pidZ.set_PID_safeopt(pid_safeopt_param) 179 | target = switchSetPoint() 180 | tuning_mode = False 181 | last_fitness = 0 182 | 183 | drone.send_rc_control(0, action_z, action_y, action_x) # turn with yaw 184 | if SET_POINT_Z_cm == 150: 185 | drawer.draw_fitness_value(image, measure42.fitness()) 186 | 187 | drawer.draw_controller_output(image, action_x, action_y, action_z) 188 | drawer.draw_current_PID(image, current_pid, current_parameter) 189 | drawer.draw_setpoint(image, SET_POINT_X, SET_POINT_Y, SET_POINT_Z_cm) 190 | 191 | if time.time() - start_time >= 5: 192 | battery_level = drone.get_battery() 193 | 194 | drawer.draw_battery_level(image, battery_level) 195 | drawer.draw_expiration_time(image, time.time()-start_time) 196 | 197 | width = 2200/2 198 | ratio = 16 / 9 199 | dim = (int(width), int(width / ratio)) 200 | image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) 201 | 202 | cv2.imshow("markers", image) 203 | 204 | # key hadling 205 | key_pressed = cv2.waitKey(1) 206 | 207 | if key_pressed & 0xFF == ord('q'): # quit from script 208 | drone.land() 209 | drone.get_battery() 210 | print(safeoptassistant.get_best_param()) 211 | break 212 | -------------------------------------------------------------------------------- /tuning/tuning_y_axis_with_SafeOpt.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import cv2 3 | import time 4 | from cv2 import aruco 5 | from djitellopy import Tello 6 | from GUI.gui_drawer import GuiDrawer 7 | 8 | from controllers.PID_controller import PIDController as PID 9 | from feedback import MarkerDetector 10 | from PID_parameters_handler import PIDTuner 11 | from controllers.BangBang_controller import BangBangController 12 | from tuning import measurementAssistant, SafeOptTunerAssistant 13 | 14 | 15 | start_time = time.time() 16 | 17 | DRONE_SPEED_X = 25 18 | DRONE_SPEED_Y = 30 19 | DRONE_SPEED_Z = 25 20 | 21 | 22 | SET_POINT_X = 960 / 2 23 | SET_POINT_Y = 720 / 2 24 | SET_POINT_Z_cm = 310 25 | 26 | # pid section 27 | pidX = PID('x') 28 | pidY = PID('y') 29 | pidZ = PID('z') 30 | 31 | # pid setup 32 | pid_safeopt_param = [0.5, 0.002, 0.002] 33 | pid_safeopt_max = [1, 0.2, 0.7] 34 | 35 | # pid_safeopt_param = [0.61385034, 0.0002, 0.289428] 36 | # (ObsAr([0.66659107, 0.02 , 0.3 ]), ObsAr([16.12644424])) 37 | # (ObsAr([0.61385034, 0.02 38 | # , 0.289428 ]), ObsAr([18.31138782])) 39 | # (ObsAr([0.72632365, 0.002 , 0.13493126]), ObsAr([18.8394745])) 40 | # (ObsAr([0.57531093, 0.02 , 0.17326167]), ObsAr([20.36025728])) 41 | pidY.set_PID_safeopt(pid_safeopt_param) 42 | 43 | # auxiliary controllers 44 | cx = BangBangController(SET_POINT_X, 25, DRONE_SPEED_X) 45 | cy = BangBangController(SET_POINT_Y, 25, DRONE_SPEED_Y) 46 | cz = BangBangController(SET_POINT_Z_cm, 50, DRONE_SPEED_Z) 47 | # pid keys 48 | pidSetter = PIDTuner(pidX, pidY, pidZ) 49 | drawer = GuiDrawer() 50 | current_pid, current_parameter = pidY, 'p' 51 | 52 | # measurements 53 | measure42 = measurementAssistant.MeasurementAssistant("y_axis42", 25, 3, pid_safeopt_param) 54 | measure33 = measurementAssistant.MeasurementAssistant("y_axis33", 25, 3, pid_safeopt_param) 55 | # safeopt assistant 56 | 57 | safeoptassistant = SafeOptTunerAssistant.Tuner(0.0025 ** 2, pid_safeopt_param[0], pid_safeopt_param[1], 58 | pid_safeopt_param[2], pid_safeopt_max[0], pid_safeopt_max[1], 59 | pid_safeopt_max[2]) 60 | target = 33 61 | tuning_mode = False 62 | last_fitness = 0 63 | 64 | 65 | def switchTarget(): 66 | if target == 33: 67 | return 42 68 | elif target == 42: 69 | return 33 70 | 71 | 72 | success = False 73 | 74 | aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) 75 | 76 | # video source and calibration parameters setup 77 | 78 | # video_capture = cv2.VideoCapture(0) 79 | drone = Tello() 80 | drone.connect() 81 | battery_level = drone.get_battery() 82 | drone.streamon() 83 | 84 | # detection 85 | 86 | pc_mtx = np.array([[1.73223258e+03, 0.00000000e+00, 1.27300230e+03], 87 | [0.00000000e+00, 1.73223258e+03, 1.03042217e+03], 88 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 89 | 90 | pc_dist = np.array([[-3.89477169e+00], 91 | [-9.72135202e-02], 92 | [1.04299558e-02], 93 | [8.40170760e-05], 94 | [-2.17736443e+00], 95 | [-3.89139953e+00], 96 | [-1.90794581e-01], 97 | [-1.85298591e+00], 98 | [0.00000000e+00], 99 | [0.00000000e+00], 100 | [0.00000000e+00], 101 | [0.00000000e+00], 102 | [0.00000000e+00], 103 | [0.00000000e+00]]) 104 | 105 | drone_mtx = np.array([[1.74213359e+03, 0.00000000e+00, 1.27150514e+03], 106 | [0.00000000e+00, 1.74213359e+03, 1.02516982e+03], 107 | [0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]) 108 | 109 | drone_dist = np.array([[-1.69684883e+00], 110 | [-6.85717812e+00], 111 | [9.93624014e-03], 112 | [6.20144084e-04], 113 | [-1.18739065e+01], 114 | [-1.69460711e+00], 115 | [-6.99110211e+00], 116 | [-1.13633464e+01], 117 | [0.00000000e+00], 118 | [0.00000000e+00], 119 | [0.00000000e+00], 120 | [0.00000000e+00], 121 | [0.00000000e+00], 122 | [0.00000000e+00]]) 123 | 124 | mtx, dist = drone_mtx, drone_dist 125 | 126 | detector = MarkerDetector(aruco_dict, mtx, dist) 127 | 128 | # loop start 129 | # drone.takeoff() 130 | while True: 131 | 132 | # ret, frame = video_capture.read() 133 | 134 | frame = drone.get_frame_read().frame 135 | 136 | image, horizontal_error, vertical_error, frontal_error = detector.detect_and_compute_error_values(frame, 137 | SET_POINT_X, 138 | SET_POINT_Y, 139 | SET_POINT_Z_cm, 140 | target) 141 | if target == 33 and drone.get_height(): 142 | action_x, action_y, action_z = 0, 30, 0 143 | elif target == 42: 144 | action_x, action_y, action_z = 0, -30, 0 145 | else: 146 | action_x, action_y, action_z = 0, 0, 0 147 | 148 | if horizontal_error is not None: 149 | drawer.draw_errors(image, horizontal_error, vertical_error, frontal_error, frontal_error+SET_POINT_Z_cm) 150 | 151 | # action_y = int(pidY.compute_action(vertical_error)) 152 | # action_z = int(pidZ.compute_action(frontal_error)) 153 | action_x = cx.compute_action(horizontal_error) 154 | # action_y = cy.compute_action(-vertical_error) 155 | action_z = cz.compute_action(-frontal_error) 156 | 157 | if tuning_mode: 158 | action_y = int(pidY.compute_action(vertical_error)) 159 | else: 160 | action_y = cx.compute_action(-vertical_error) 161 | 162 | if target == 33: 163 | measure33.write_measurement(vertical_error) 164 | if measure33.is_in_setpoint(): 165 | target = switchTarget() 166 | start_time = time.time() 167 | measure33.new_round(pid_safeopt_param, last_fitness) 168 | measure42.new_round(pid_safeopt_param, last_fitness) 169 | tuning_mode = True 170 | last_fitness = 0 171 | 172 | elif target == 42: 173 | 174 | print(time.time() - start_time) 175 | measure42.write_measurement(vertical_error) 176 | if measure42.is_in_setpoint(): 177 | last_fitness = measure42.fitness() 178 | safeoptassistant.optimize(last_fitness) 179 | pid_safeopt_param = safeoptassistant.get_param() 180 | pidY.set_PID_safeopt(pid_safeopt_param) 181 | target = switchTarget() 182 | tuning_mode = False 183 | 184 | if time.time() - start_time >= 15: 185 | start_time = time.time() 186 | if target == 42: 187 | safeoptassistant.optimize(0) 188 | pid_safeopt_param = safeoptassistant.get_param() 189 | pidX.set_PID_safeopt(pid_safeopt_param) 190 | target = switchTarget() 191 | tuning_mode = False 192 | last_fitness = 0 193 | 194 | drone.send_rc_control(0, action_z, action_y, action_x) # turn with yaw 195 | 196 | if target == 42: 197 | drawer.draw_fitness_value(image, measure42.fitness()) 198 | 199 | drawer.draw_controller_output(image, action_x, action_y, action_z) 200 | drawer.draw_current_PID(image, current_pid, current_parameter) 201 | drawer.draw_setpoint(image, SET_POINT_X, SET_POINT_Y, SET_POINT_Z_cm) 202 | 203 | if time.time() - start_time >= 5: 204 | battery_level = drone.get_battery() 205 | 206 | drawer.draw_battery_level(image, battery_level) 207 | drawer.draw_expiration_time(image, time.time()-start_time) 208 | 209 | width = 2200/2 210 | ratio = 16 / 9 211 | dim = (int(width), int(width / ratio)) 212 | image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) 213 | 214 | cv2.imshow("markers", image) 215 | 216 | # key hadling 217 | key_pressed = cv2.waitKey(1) 218 | 219 | if key_pressed & 0xFF == ord('q'): # quit from script 220 | drone.land() 221 | drone.get_battery() 222 | print(safeoptassistant.get_best_param()) 223 | break 224 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------