Vehicle Path Planning Using Genetic Algorithms

Objective

This project aims to optimize vehicle path planning for specific parking scenarios utilizing genetic algorithms to automate the maneuvering of vehicles into designated parking spots.

Technology Stack

• Programming Language: Python
• Libraries: NumPy, Matplotlib, SciPy

Key Features

• Population Initialization: Begins with a random initialization of the population using binary strings that represent control variables for steering and velocity.
• Fitness Evaluation: Utilizes a custom cost function that evaluates each solution based on the vehicle's final position, orientation, and velocity.
• Genetic Operations: Employs selection, crossover, and mutation to evolve the population through generations, enhancing solution quality iteratively.
• Control Interpolation: Implements cubic spline interpolation to ensure smooth transitions between control points generated by the genetic algorithm.
• Visualization: Generates detailed plots using Matplotlib to visualize the vehicle's trajectory, steering angles, and velocity profiles during simulations.

Simulation Details

• Spatial Constraints: The vehicle must navigate complex spatial constraints without collisions.
• Integration Method: Uses the Euler method to simulate real-time dynamics of the vehicle under various control inputs.
• Performance Monitoring: Plots are dynamically generated to monitor the trajectory and control variable changes, aiding in the optimization process.

Performance

• The algorithm is designed to efficiently find optimal or near-optimal solutions within pre-defined generational or time constraints.
• Demonstrates the successful automated parking of vehicles, validating the genetic algorithm's effectiveness.

Output

• Final State Visualization: At the end of each successful simulation, the final state values and trajectory plots are displayed.
• Data Export: Control variables are saved to a file upon achieving a successful parking scenario, which can be used for further analysis or implementation.

Usage

• Research and Development: Ideal for R&D teams working on autonomous vehicle navigation systems.
• Adaptability: The framework can be adapted or extended to other types of dynamic control simulations that require optimization of operational parameters.