Adversarial Drone Learning System

#ROBOTICS #AI #ADVERSARIAL

Project Overview

An interactive system featuring a custom-built drone and an anti-drone mechanism, both equipped with ZED 2i stereo cameras. The system implements adversarial learning to enhance evasion and interception capabilities through real-time interaction and adaptation.

Development Progress

Custom drone with ZED 2i camera integration

Anti-drone system with tracking capabilities

Adversarial training in action

Technical Details

System Components

Custom-built lightweight drone platform
ZED 2i stereo cameras for depth perception
Simulated interception system with toy gun
Real-time tracking and response system
Multi-agent learning framework

Technical Implementation

Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
Custom simulation environment for training
Real-time perception and tracking pipeline
Distributed computing architecture
Data collection and analysis framework

Code Implementation


# Multi-Agent Learning Implementation
class AdversarialSystem:
    def __init__(self):
        self.drone_agent = DroneAgent()
        self.anti_drone_agent = AntiDroneAgent()
        self.environment = SimulationEnv()
        
    def train_agents(self, episodes):
        for episode in range(episodes):
            state = self.environment.reset()
            done = False
            
            while not done:
                # Drone action selection
                drone_action = self.drone_agent.select_action(state)
                # Anti-drone response
                anti_drone_action = self.anti_drone_agent.select_action(state)
                
                # Environment step
                next_state, reward, done, info = self.environment.step(
                    drone_action, anti_drone_action
                )
                
                # Update agents
                self.drone_agent.update(state, drone_action, reward)
                self.anti_drone_agent.update(state, anti_drone_action, -reward)
                
                state = next_state

Maxence Boels