title: 🔭 Raspberry Pi & AIOT Projects tags: [drone, collaboration, git, getting_started, aiot, embedded] last_updated: October 18, 2025 keywords: ai, drone, aiot, raspberry pi, jetson, firmware, embedded systems, iot summary: “Comprehensive overview of AIOT (AI + IoT) projects including Raspberry Pi, NVIDIA Jetson, drone development, and embedded AI systems.” sidebar: mydoc_sidebar permalink: mydoc_raspi.html folder: mydoc —

AIOT Project Portfolio Overview

This section documents my experience with AI-enhanced IoT (AIOT) systems, embedded development, and edge computing projects.

🚁 Drone & UAV Projects

Autonomous Drone Navigation System

• Platform: Raspberry Pi 4 + ArduPilot
• Features:
  • Computer vision-based obstacle avoidance
  • GPS-denied indoor navigation using SLAM
  • Real-time video streaming and object detection
• Tech Stack: Python, OpenCV, MAVLink, ROS2
• Repository: [Link to be added]

Drone Swarm Coordination

• Objective: Multi-drone collaborative mapping and surveillance
• Hardware: Multiple Raspberry Pi-based drones with custom flight controllers
• AI Components:
  • Distributed decision making algorithms
  • Formation flying coordination
  • Collaborative SLAM mapping

🤖 Raspberry Pi AI Projects

Edge AI Vision System

• Hardware: Raspberry Pi 4 + Intel Neural Compute Stick 2
• Capabilities:
  • Real-time object detection and classification
  • Face recognition and tracking
  • Gesture recognition for IoT device control
• Models: YOLOv5, MobileNet, custom trained models
• Performance: 15-20 FPS inference on Pi 4

Smart Home Automation Hub

• Role: Central AIOT controller for smart home ecosystem
• Features:
  • Voice control integration (offline speech recognition)
  • Predictive energy management using ML
  • Security system with AI-powered anomaly detection
• Protocols: MQTT, Zigbee, WiFi, Bluetooth LE

🚀 NVIDIA Jetson Projects

Jetson Nano Edge Computing Platform

• Project: Autonomous robot navigation and manipulation
• AI Workloads:
  • Real-time path planning using reinforcement learning
  • Object manipulation with computer vision
  • Natural language command processing
• Performance: 60+ FPS inference with TensorRT optimization

Industrial IoT Monitoring System

• Application: Factory equipment predictive maintenance
• Hardware: Jetson Xavier NX + industrial sensors
• AI Features:
  • Vibration pattern analysis for fault detection
  • Thermal imaging anomaly detection
  • Production quality control using computer vision

🔧 Firmware & Embedded Development

Custom Flight Controller Firmware

• Platform: STM32-based flight controller
• Language: C/C++ with FreeRTOS
• Features:
  • Sensor fusion (IMU, GPS, barometer)
  • PID control loops for stabilization
  • MAVLink communication protocol
  • Custom autopilot modes

IoT Sensor Network Protocol

• Objective: Low-power, long-range sensor communication
• Technology: LoRaWAN with custom mesh networking
• Applications:
  • Environmental monitoring
  • Agricultural sensor networks
  • Smart city infrastructure

📊 AIOT System Architecture

Edge-Cloud Hybrid Processing

[Sensors] → [Edge Device (Pi/Jetson)] → [Local Processing] → [Cloud Analytics]
     ↓              ↓                        ↓                    ↓
[Data Collection] [AI Inference]        [Real-time Control]  [Big Data ML]

Real-time Data Pipeline

• Edge Processing: Immediate response for critical decisions
• Cloud Processing: Historical analysis and model training
• Hybrid Approach: Balance between latency and computational power

🛠️ Development Tools & Frameworks

Embedded AI Stack

• Frameworks: TensorFlow Lite, PyTorch Mobile, OpenVINO
• Optimization: TensorRT, Neural Compute Stick, Coral TPU
• Languages: Python, C++, Rust for embedded systems
• OS: Ubuntu Core, Yocto Linux, custom embedded Linux

DevOps for Edge Devices

• Containerization: Docker for Pi/Jetson deployment
• Orchestration: K3s (lightweight Kubernetes) for edge clusters
• CI/CD: GitHub Actions with cross-compilation
• Monitoring: Prometheus + Grafana for edge device metrics

🎯 Current Focus Areas

1. Federated Learning on Edge Devices

• Goal: Distributed ML training across IoT device networks
• Challenges: Limited compute resources, network constraints
• Progress: Prototype implementation on Raspberry Pi cluster

2. AI-Powered Drone Delivery System

• Collaboration: Integration with logistics planning algorithms
• Innovation: Dynamic route optimization using reinforcement learning
• Testing: Simulation environment with realistic physics

3. Smart Agriculture AIOT Platform

• Sensors: Soil moisture, weather stations, crop cameras
• AI: Crop health monitoring, irrigation optimization
• Impact: Precision agriculture with reduced resource usage

📈 Metrics & Achievements

• Deployed Systems: 15+ AIOT devices in production
• Inference Speed: Optimized models running at 20-60 FPS on edge devices
• Power Efficiency: 40% reduction in power consumption through AI optimization
• Reliability: 99.5% uptime for critical AIOT monitoring systems

🔮 Future Roadmap

Next Quarter Goals

1. 5G Edge Computing: Integrate 5G connectivity for ultra-low latency applications
2. Neuromorphic Computing: Explore Intel Loihi for event-driven AI processing
3. Digital Twin Integration: Create digital twins of physical AIOT systems

Long-term Vision

• Autonomous Robot Ecosystems: Self-organizing robot networks
• Sustainable AI: Carbon-neutral AIOT deployments
• Human-AI Collaboration: Intuitive interfaces for AIOT system interaction

📚 Technical Documentation Links

• Raspberry Pi Setup Guide
• Jetson Development Environment
• Drone Programming Tutorials
• AIOT Architecture Patterns

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