🚀 Dynamic Window Approach (DWA) Path Planning Simulation

A fully visualized implementation of the Dynamic Window Approach (DWA) in Python using Pygame. Simulate and visualize obstacle avoidance and goal-reaching for mobile robots in 2D space — perfect for robotics beginners, path planning researchers, and AI robotics students!

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🧠 What is DWA?

Dynamic Window Approach is a real-time collision avoidance algorithm for mobile robots. It evaluates a set of feasible trajectories based on the robot's dynamic constraints, obstacle proximity, and goal direction.

This project:

• Predicts multiple future trajectories
• Scores each based on distance to goal, speed, and safety
• Selects the best trajectory and visualizes it
• Runs smoothly in a Pygame window with live user interaction!

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🖼️ Features

• 📍 Real-time path planning to a user-defined goal
• ⚠️ Dynamic obstacle addition with right-click
• 🧭 Visualized prediction trajectories
• 🚗 Simulated robot with circular collision model
• 🧪 Easy to tweak robot config for experiments

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🕹️ Demo

Controls:

• Left-click to set a new goal
• Right-click to add a new obstacle
• Window will auto-close once robot reaches goal

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🛠️ Requirements

• Python 3.7+
• Pygame
• NumPy

pip install pygame numpy

📦 Run the Simulation

python DWA.py

Customize Parameters

Easily adjust robot dynamics and cost weights in the Config class:

self.max_speed = 1.0
self.max_accel = 0.2
self.to_goal_cost_gain = 0.1
self.obstacle_cost_gain = 0.5
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🤖 Why Use This?

• ✅ Learn path planning through visualization
• ✅ Great teaching material for robotics classes
• ✅ Easy to hack and extend (e.g., LIDAR input, map loading)
• ✅ No ROS or external frameworks required

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