Autonomous Grid Navigation with Finite State Machine

This project is a Python-based simulation that demonstrates an autonomous navigation system using a Finite State Machine (FSM). The agent navigates a grid environment, intelligently handling obstacles, intersections, and destination detection.

Features

• Finite State Machine Logic:

  • States include: Start, Stop, Slow Down, Forward, Turn, Avoid Obstacle, and Stop-End Trip.
  • Dynamic state transitions based on environmental inputs.

• Obstacle Detection and Avoidance:

  • Simulated LiDAR sensors detect obstacles within the grid.
  • The agent recalculates its path to avoid collisions.

• Grid Navigation:

  • Agent starts at a predefined location and navigates toward a destination in a 10x10 grid.

• Randomized Environment:

  • Obstacle positions and GPS commands are randomly generated to simulate real-world unpredictability.

How It Works

1. Initialize Environment:

   • The grid environment is set up with random obstacle placements.
   • The agent is initialized at a starting point with a target destination.

2. State Transition Logic:

   • The FSM transitions between states based on:
     • Obstacle proximity.
     • Reaching the destination.
     • GPS commands for turning.

3. Simulation Steps:

   • Each step updates the agent’s position, checks for obstacles, and determines the next state.

4. End of Simulation:

   • The simulation terminates when the agent reaches the destination or a maximum step limit is reached.