[Ashish080213]

PR Description: Dijkstra Path Planning Implementation for Autonomous Vehicles

This pull request introduces a Dijkstra-based global path planning algorithm to the Autonomous Vehicle Control Beginners Guide. The implementation provides a baseline optimal planner without heuristic bias.

Motivation

Dijkstra’s algorithm serves as a fundamental shortest-path baseline for autonomous navigation.
Adding this planner enables direct comparison with heuristic-based planners such as A*.

A* Algorithm Cost Function (In this repository)

Node-to-Node Cost is Fixed = 1
g(n) += Node-to-Node Cost
Heuristic Function h(n) -> Weighted Manhattan Distance = w * (|n_x - g_x| + |n_y - g_y|)
where:

• n: current node
• g: goal node

Total Cost f(n) = g(n) + h(n)
g(n) = cost from start node to current node n
h(n) = heuristic estimate from current node n to goal node g
When h(n) = 0, A* becomes Dijkstra

Key Changes from A* to Dijkstra

Node-to-Node Cost -> Euclidean Distance = sqrt( (n1_x - n2_x)^2 + (n1_y - n2_y)^2 )
g(n) += Node-to-Node Cost
Removed Heuristic Function
where:

• n1: current node
• n2: neighbouring node

Total Cost f(n) = g(n)
g(n) = cost from start node to current node
Added Unit Test File
Updated README File

Testing

Verified shortest-path optimality on grid-based maps.
Tested planner behavior on multiple start–goal configurations.

[ShisatoYano]

@Ashish080213 Thank you so much for the PR! This looks great. I'm a bit tied up right now, but I'll review it in detail and get back to you soon. Please bear with me for a bit!

[ShisatoYano]

Great work! I particularly liked how you wrote those descriptions in the issue and the PR in detail including algorithm and implementation policy. Merging this PR now. Thank you for contributing to my OSS!!

[Ashish080213]

Thank you for the review and for merging the PR. I appreciate the opportunity to contribute.