# Object Tracking Robot The **Object Tracking Robot** is an advanced robotic system capable of **detecting and following a target object** in real-time. Using **computer vision and sensor data**, this robot dynamically adjusts its movements to track an object, making it an excellent project for learning **machine vision, control algorithms, and real-time processing**. #### **Key Features:** * **Real-Time Object Detection:** Uses **camera vision or sensor-based tracking** to detect and follow objects. * **Adaptive Motion Control:** Adjusts speed and direction based on object movement. * **Machine Learning & AI Integration:** Can incorporate **AI-based vision models** for improved accuracy. * **Wide Applications:** Used in **security surveillance, automated delivery, and assistive robotics**. [**Discover More About the Object Tracking Robot**](/smd-applications/robotics/differential-robot-projects/object-tracking-robot.md) **code** {% code lineNumbers="true" %} ```python #Import the neccesary libraries import argparse import cv2 from smd.red import * import os # Construct the argument parse parser = argparse.ArgumentParser( description='Script to run object detection network') parser.add_argument("--prototxt", default = "MobileNetSSD.prototxt", help = 'Path to text network file') parser.add_argument("--weights", default="MobileNetSSD.caffemodel", help='Path to weights') parser.add_argument("--thr", default=0.2, type=float, help="Confidence threshold to filter out weak detections") parser.add_argument("--close", default=0.35, type=float, help="How much robot is getting close. Lower values robot will stop further away") parser.add_argument("--object", default='person', help="What object robot should follow. Objects are:" "background, aeroplane, bicycle, bird, boat, bottle, bus, " "car, cat, chair, cow, diningtable, dog, horse, motorbike, person, " "pottedplant, sheep, sofa, train, tvmonitor") args = parser.parse_args() # Labels of Network. classNames = { 0: 'background', 1: 'aeroplane', 2: 'bicycle', 3: 'bird', 4: 'boat', 5: 'bottle', 6: 'bus', 7: 'car', 8: 'cat', 9: 'chair', 10: 'cow', 11: 'diningtable', 12: 'dog', 13: 'horse', 14: 'motorbike', 15: 'person', 16: 'pottedplant', 17: 'sheep', 18: 'sofa', 19: 'train', 20: 'tvmonitor' } # Open camera device. cap = cv2.VideoCapture(0) cap.set(cv2.CAP_PROP_BUFFERSIZE, 1) frame_width = int(cap.get(3)) frame_height = int(cap.get(4)) size = (frame_width, frame_height) result = cv2.VideoWriter('recording.avi', cv2.VideoWriter_fourcc(*'MJPG'), 5, size) #Load the Caffe model net = cv2.dnn.readNetFromCaffe(args.prototxt, args.weights) usb_port = "/dev/ttyUSB0" # We need to read and write data to usb port jetson nano normally doesn't allow it os.system('sudo chmod a+rw ' + usb_port) class Robot: def __init__(self): # SMD setup self.port = usb_port self.m = Master(self.port) self.m.attach(Red(0)) self.m.attach(Red(1)) self.m.set_connected_modules(0,["Servo_1"]) self.servo_pos = 90 #Current servo position self.last_error = 0 #Last error of motors used for PID self.las_dir = 0 #Last direction object is detected self.last_error_servo = 0 #Last error of servo used for PID # Motor setup # SMD 0 is left motor # SMD 1 is right motor self.m.set_shaft_cpr(0, 6533) self.m.set_shaft_rpm(0, 100) self.m.set_shaft_cpr(1, 6533) self.m.set_shaft_rpm(1, 100) self.m.set_operation_mode(0, OperationMode.Velocity) self.m.set_operation_mode(1, OperationMode.Velocity) self.m.enable_torque(0, True) self.m.enable_torque(1, True) self.m.set_servo(0, 1, 90) # Robot parameters self.rpm_left = 0 # Initial RPM for the left motor self.rpm_right = 0 # Intial RPM for the right motor # Drives the motors given error def motor_drive(self, error): kp = 0.2 kd = 0.1 # PID calculation for motors max 100 min -100 self.rpm_left = min(100,max(-100,int(kp*error + 80 + kd*(self.last_error - error)))) self.rpm_right = min(100,max(-100,int(-kp * error + 80 - kd*(self.last_error - error)))) self.last_error = error # Motor speeds print("left speed:{},right speed:{}".format(self.rpm_left, self.rpm_right)) self.m.set_velocity(1, self.rpm_left) self.m.set_velocity(0, -self.rpm_right) # Stops the motors def stop(self): self.m.set_velocity(1,0) self.m.set_velocity(0,0) #Calculates ratio between frame and the total are of object used for estimateing distance to object def calc_object_ratio_to_frame(self, xl, xr, yl, yr): return ((xr - xl) * (yr - yl))/(640*480) # AI object detection def detect(self): # Capture frame-by-frame ret, frame = cap.read() frame_resized = cv2.resize(frame,(300,300)) # Resize frame for prediction # We perform a mean subtraction (127.5, 127.5, 127.5) to normalize the input; # after executing this command our "blob" now has the shape: (1, 3, 300, 300) blob = cv2.dnn.blobFromImage(frame_resized, 0.007843, (300, 300), (127.5, 127.5, 127.5), False) # Set to network the input blob net.setInput(blob) # Prediction of network detections = net.forward() # Size of frame resize (300x300) cols = frame_resized.shape[1] rows = frame_resized.shape[0] # For get the class and location of object detected, # There is a fix index for class, location and confidence # value in @detections array . for i in range(detections.shape[2]): confidence = detections[0, 0, i, 2] #Confidence of prediction if confidence > args.thr: # Filter prediction class_id = int(detections[0, 0, i, 1]) # Class label # Object location xLeftBottom = int(detections[0, 0, i, 3] * cols) yLeftBottom = int(detections[0, 0, i, 4] * rows) xRightTop = int(detections[0, 0, i, 5] * cols) yRightTop = int(detections[0, 0, i, 6] * rows) # Factor for scale to original size of frame heightFactor = frame.shape[0]/300.0 widthFactor = frame.shape[1]/300.0 # Scale object detection to frame xLeftBottom = int(widthFactor * xLeftBottom) yLeftBottom = int(heightFactor * yLeftBottom) xRightTop = int(widthFactor * xRightTop) # Draw rectangle and name of the object to the frame yRightTop = int(heightFactor * yRightTop) if class_id in classNames: label = classNames[class_id] + ": " + str(confidence) labelSize, baseLine = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1) yLeftBottom = max(yLeftBottom, labelSize[1]) cv2.rectangle(frame, (xLeftBottom, yLeftBottom), (xRightTop, yRightTop),(0, 255, 0)) cv2.rectangle(frame, (xLeftBottom, yLeftBottom - labelSize[1]), (xLeftBottom + labelSize[0], yLeftBottom + baseLine), (255, 255, 255), cv2.FILLED) cv2.putText(frame, label, (xLeftBottom, yLeftBottom), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0)) # Save the frame for the video result.write(frame) # Check if object is what we are looking for if classNames[class_id] == args.object: return class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom # If we don't find our object return 0's return 0,0,0,0,0,0 # Look for object using the servo in the latest direction object is detected def look_for_object(self, dir): self.m.set_servo(1, 5, 120) if dir == 0: for i in reversed(range(0,10)): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) while True: for i in range(0,19): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) for i in reversed(range(0,19)): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) else: for i in range(9,19): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) while True: for i in reversed(range(0,19)): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) for i in range(0,19): class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() if confidence != 0 and classNames[class_id] == args.object: return i*10 self.m.set_servo(0,1,i*10) # Turn the body of the robot to the specifed degree def turn(self, deg): d = 0.5 deg -= 90 self.m.set_velocity(0, deg*d) self.m.set_velocity(1, deg*d) self.m.set_servo(0, 1, 95) self.servo_pos = 95 time.sleep(1) self.m.set_velocity(0, 0) self.m.set_velocity(1, 0) # If a object is close track it with only the servo def track_object_close(self, error): kp = 0.02 kd = 0.001 # PID calculation for servo amx 180 min 0 change = kp*error + kd*(self.last_error_servo - error) self.servo_pos = min(180, max(0, self.servo_pos + change)) self.last_error_servo = error # Servo position print(f"Servo:{self.servo_pos}") self.m.set_servo(0, 1, int(self.servo_pos)) # If object is getting out of frame turn the body if self.servo_pos > 95: self.las_dir = 1 else: self.las_dir = 0 if self.servo_pos > 160: self.turn(self.servo_pos) elif self.servo_pos < 20: self.turn(self.servo_pos) # Main loop of our object def run(self): while True: # Object detection class_id, confidence, yRightTop, yLeftBottom, xRightTop, xLeftBottom = self.detect() # If no object is detected look for it if confidence == 0: self.stop() deg = self.look_for_object(self.las_dir) self.turn(deg) continue # Calculates the error, error is difference between left and right contours to the frame lenght rightDif = 640 - xRightTop leftDif = xLeftBottom error = rightDif - leftDif ratio = self.calc_object_ratio_to_frame(xLeftBottom,xRightTop,yLeftBottom,yRightTop) # Error is multiplied with ratio to make robot take faster turns at close and slower at distance error *= ratio # Determine if object is left or right from the error if error > 0: self.las_dir = 1 else: self.las_dir = 0 # If object is close enough stop and start tracking if ratio < args.close: self.m.set_servo(0, 1, 95) self.motor_drive(error) else: self.stop() self.track_object_close(error/ratio) robo = Robot() try: robo.run() except KeyboardInterrupt: robo.stop() cap.release() result.release() ``` {% endcode %} --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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