4. Robot Sensing and Perception
• Sensor types and applications
• Sensory data processing and fusion
• Robot vision and image processing
Robot Sensing and Perception:
Robot sensing and perception are important aspects of robotics and automation that enable robots to interact with the environment and perform various tasks. Robot sensing refers to the process of collecting data from the environment using various sensors, while perception involves the interpretation of this data to understand the environment and make decisions.
Sensor types and applications:
There are many types of sensors that can be used in robotics, including:
Inertial measurement units (IMUs): these sensors measure the orientation, velocity, and acceleration of the robot.
Ultrasonic sensors: these sensors use sound waves to detect obstacles and measure distances.
Laser range finders: these sensors use lasers to measure distances and create 2D or 3D maps of the environment.
Cameras: these sensors capture visual data and are used in robot vision and image processing.
Force sensors: these sensors measure the force applied to the robot or objects in contact with the robot.
Sensory data processing and fusion:
Sensory data processing involves filtering, smoothing, and
transforming raw sensor data into meaningful information that can be used for
decision-making. Sensor fusion involves combining data from multiple sensors to
improve the accuracy and reliability of the data. For example, combining data
from an IMU and a camera can provide more accurate position and orientation
information than either sensor alone.
Example for Sensor Fusion:
python code
import numpy as np
# Data from IMU
imu_data = np.array([0.1, 0.2, 0.3])
# Data from camera
camera_data = np.array([0.2, 0.3, 0.4])
# Weighting factors for sensor fusion
imu_weight = 0.6
camera_weight = 0.4
# Combine data from both sensors using weighted average
fusion_data = imu_weight * imu_data + camera_weight * camera_data
print(fusion_data)
Robot vision and image processing:
Robot vision involves using cameras and other sensors to
capture visual data and interpret it to understand the environment. Image
processing involves analysing this data to extract features, detect objects,
and recognize patterns. Some common applications of robot vision and image
processing include object recognition, navigation, and inspection.
Example for Object Detection:
python code
import cv2
# Load image
image = cv2.imread("object.jpg")
# Convert image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Load object template
template = cv2.imread("template.jpg", 0)
# Find object in image using template matching
result = cv2.matchTemplate(gray, template, cv2.TM_CCOEFF_NORMED)
# Set threshold for detection
threshold = 0.8
locations = np.where(result >= threshold)
# Draw bounding box around object
for loc in zip(*locations[::-1]):
cv2.rectangle(image, loc, (loc[0] + template.shape[1], loc[1] + template.shape[0]), (0, 255, 0), 2)
# Show image with bounding box
cv2.imshow("Object detection", image)
cv2.waitKey(0)
Also Read:
Sensors-Perception-Programming-Applications
Principles of Robotics and Automation
