Autonomous Drone Pathfinding & Collision Avoidance

Project Overview

This project focuses on developing an autonomous drone navigation system capable of efficient pathfinding and real-time collision avoidance in complex environments. Using Unreal Engine 5's simulation capabilities, I'm implementing and testing advanced algorithms that enable drones to navigate safely through dynamic environments with minimal human intervention.

Key Technologies

• Simulated LiDAR: Using virtual LiDAR sensors within Unreal Engine 5 to detect objects and map environments
• Reinforcement Learning: Training the drone to make optimal navigation decisions through reward-based learning
• Genetic Algorithms: Evolving pathfinding strategies to discover efficient solutions for complex navigation problems
• Real-time Processing: Implementing efficient algorithms for split-second decision making during flight

Technical Implementation

The project combines several advanced computational approaches:

• Environment Mapping: Creating 3D spatial awareness using point cloud data from simulated LiDAR
• Obstacle Detection: Real-time object classification and tracking to identify potential collision risks
• Path Planning: Using A* and RRT (Rapidly-exploring Random Tree) algorithms enhanced with reinforcement learning for optimal route finding
• Dynamic Adaptation: Implementing continuous path recalculation in response to changing environments

Development Process

The development follows an iterative approach:

1. Building a simulation environment in Unreal Engine 5 with various obstacle scenarios
2. Implementing basic pathfinding algorithms and testing their performance
3. Designing and training reinforcement learning models to improve decision-making
4. Applying genetic algorithms to evolve navigation strategies across multiple generations
5. Optimizing the system for real-time performance and reliability
6. Conducting comprehensive testing in increasingly complex environments

Current Status

This project is currently in active development, with the simulation environment established and basic pathfinding algorithms implemented. I'm now focusing on integrating reinforcement learning models and optimizing the collision avoidance system for more reliable performance.

Expected completion: May 2025

Applications & Future Work

The technologies developed in this project have potential applications in:

• Autonomous delivery drones operating in urban environments
• Search and rescue operations in disaster zones
• Agricultural monitoring and precision farming
• Structural inspection of buildings and infrastructure

Future work will focus on optimizing the algorithms for implementation on resource-constrained hardware and testing in more diverse environmental conditions.