Introduction to Data Structures

In the field of robotics and computer vision, data structures play a crucial role in organizing and managing data efficiently. They provide a way to store and process data in a structured manner, enabling easier manipulation and analysis.

For example, consider a scenario where a robot needs to navigate a complex environment. The robot needs to store a map of the environment, which can be represented as a grid. Each cell in the grid can have information about obstacles, paths, or other relevant data.

To represent this grid in a data structure, we can use a 2D array or a matrix. Each element in the array corresponds to a cell in the grid, and we can access and modify the elements based on their row and column indices.

1import numpy as np
2
3# Create a 3x3 matrix
4matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])
5
6# Access the element at row 1, column 2
7element = matrix[1,2]
8
9# Print the element
10print(element)

In this example, the matrix represents a 3x3 grid, and we access the element at row 1 and column 2, which has a value of 6.

Understanding different data structures and their applications is essential for effectively solving problems in robotics and computer vision. In the upcoming sections, we will explore various data structures such as arrays, graphs, stacks, queues, linked lists, and trees, as well as algorithms like BFS, DFS, sorting algorithms, and Dijkstra's algorithm that are widely used in these domains.

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import numpy as np

​

# Create a 3x3 matrix

matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])

​

# Access the element at row 1, column 2

element = matrix[1,2]

​

# Print the element

print(element)