Sensing and Perception

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:

            Introduction

Robotics and Automation

Sensors-Perception-Programming-Applications

Principles of Robotics and Automation

Hardware & Software in Robotics

sensing and perception

motion planning and control

manipulation and grasping

navigation and mapping

Human-robot interaction

Advanced topics

Applications of robotics

Questions and Answers

Research