Advanced Topics

9. Advanced topics in robotics and automation

•  Machine learning and artificial intelligence for robotics

•   Swarm robotics and collective intelligence

•  Robotic control systems and optimization

Advanced topics in robotics and automation:

As robotics and automation continue to evolve, there are several advanced topics that are becoming increasingly important in the field. These include machine learning and artificial intelligence for robotics, swarm robotics and collective intelligence, and robotic control systems and optimization.

Machine learning and artificial intelligence for robotics:

Machine learning and artificial intelligence techniques are being increasingly applied to robotics to improve robot perception, decision-making, and control. These techniques enable robots to adapt to changing environments and learn from experience, making them more efficient and effective in their tasks.

Example for Image Classification using Machine Learning:

python code

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

import numpy as np

# Load training and testing data

(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()

# Normalize pixel values

x_train = x_train.astype('float32') / 255.0

x_test = x_test.astype('float32') / 255.0

# Define model architecture

model = keras.Sequential([

    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),

    layers.MaxPooling2D((2, 2)),

    layers.Conv2D(64, (3, 3), activation='relu'),

    layers.MaxPooling2D((2, 2)),

    layers.Conv2D(64, (3, 3), activation='relu'),

    layers.Flatten(),

    layers.Dense(64, activation='relu'),

    layers.Dense(10)

])

# Compile model

model.compile(optimizer='adam',

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

              metrics=['accuracy'])

# Train model

model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# Evaluate model on test data

test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)

print('Test accuracy:', test_acc)

 

Swarm robotics and collective intelligence:

Swarm robotics involves the coordination of large groups of robots to perform complex tasks through collective intelligence. Swarm robotics draws inspiration from social animals, such as ants and bees, that work together to achieve common goals. This approach can be used in a variety of applications, such as search and rescue, environmental monitoring, and agriculture.Python 

Example for Swarm Robotics Simulation:

python code

import numpy as np

import matplotlib.pyplot as plt

# Define swarm size and parameters

n = 20

alpha = 1

beta = 2

gamma = 0.1

# Initialize positions and velocities

positions = np.random.uniform(size=(n, 2))

velocities = np.zeros((n, 2))

# Simulate swarm motion

for t in range(100):

    # Compute distances between agents

    distances = np.sqrt(((positions[:, None, :] - positions) ** 2).sum(-1))

        # Compute alignment and cohesion terms

    alignment = velocities / np.linalg.norm(velocities, axis=1)[:, None]

    cohesion = positions.mean(axis=0) - positions

        # Compute velocity updates

    velocity_updates = alpha * alignment + beta * cohesion / (distances + 1e-6)[:, None] ** 2

        # Apply velocity updates

    velocities += velocity_updates

        # Apply random perturbations

    velocities += np.random.normal(scale=gamma, size=(n, 2))

        # Clip velocities

    velocities = np.clip(velocities, -1, 1)

        # Apply velocity to positions

    positions += velocities

        # Plot swarm motion

    plt.clf()

 

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