A-Star Algorithm

Interactive A* path planner for grid navigation with obstacle placement and route replay

Algorithms

# features

What It Does

Core capabilities of the A-Star path planning system.

A* Path Planning

Computes shortest paths on a dynamic grid using heuristic-based search.

Interactive Controls

Set start/end points, paint obstacles, and launch live route calculations.

Path Validation

Validates and visualizes each computed step with clear state feedback and messages.

Grid Resizing

Adjust grid dimensions at runtime to test algorithm behavior on different planning resolutions.

Path Cost Diagnostics

Tracks algorithm iteration costs and highlights when heuristic costs lead to faster route convergence.

# source

Project Source Code

Explore the primary logical modules.

EXPLORER

PathPlannerApp.tsx

srcPathPlannerApp.tsx

1type Cell = [number, number];
2                        
3                        function heuristic(a: Cell, b: Cell): number {
4                            const dx = a[0] - b[0];
5                            const dy = a[1] - b[1];
6                            return Math.sqrt(dx * dx + dy * dy);
7                        }
8                        
9                        function doAStar(
10                            start: Cell,
11                            end: Cell
12                        ) {
13                            // Explore open set using g-cost + heuristic
14                            const openSet: Cell[] = [start];
15                            ...
16                        }

# simulation

A* Path Planner Simulation

Interactive A-star visualization for obstacle-aware routing.

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# repositories

Source Code

GitHub repositories for this project.

A-Star Algorithm Repository

Access the complete source code on GitHub.

Quick Start

$ git clone https://github.com/prathapselvakumar/AMR-Coursework-2

$ cd AMR-Coursework-2

$ python -m venv .venv

$ . .venv/bin/activate

$ pip install -r requirements.txt

$ python a_star_algorithm.py