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Easy Drone Control Buttons
LLM chatbot for the drone

Inspiration

Our inspiration came from imagining a future where anyone can effortlessly control and interact with advanced drone technology. We were fascinated by the idea of integrating conversational AI with autonomous vehicles, envisioning a tool that could not only navigate but intelligently interpret its surroundings. Inspired by the endless possibilities of space exploration and pushing technological frontiers, Altivue was born.

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What It Does

Altivue is a personal patrolling drone system controlled seamlessly via a laptop interface. It combines advanced AI techniques—including YOLO v5 object detection and Retrieval-Augmented Generation—to interpret commands, detect, and identify objects in real-time. Altivue recognizes numerous objects such as cars, people, animals, and everyday items, allowing it to function as an intelligent security patrol, safety monitor, or exploratory vehicle.

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How We Built It

We integrated multiple cutting-edge technologies: • YOLO v5: For real-time, accurate object detection. • Tello Drone SDK: Providing robust flight capabilities and command responsiveness. • React, HTML, CSS: Crafting an intuitive, space-themed, visually engaging user interface. • Retrieval-Augmented Generation (RAG): Allowing contextual interpretation and execution of complex commands issued by the user, enhancing drone responsiveness and decision-making.

These technologies combined enabled us to build a cohesive, intelligent drone system that bridges the gap between autonomous flight and conversational robotics.

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Challenges We Ran Into

• Real-time Integration: Synchronizing YOLO’s object detection output with drone commands was challenging, requiring precise timing and network management.
• Network Stability: Maintaining a stable connection between our drone and the laptop interface, especially when multiple processes like video streaming and object detection were occurring simultaneously.
• Model Performance: Optimizing YOLO v5 to deliver reliable and accurate detections while balancing computational load, ensuring our drone remained responsive without latency.

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Accomplishments We’re Proud Of

• Successfully integrating advanced object detection with real-time drone control, demonstrating seamless command-and-response interaction.
• Building a robust, intuitive, and visually appealing interface using React, HTML, and CSS that significantly improved user interaction.
• Implementing Retrieval-Augmented Generation (RAG), substantially enhancing the drone’s capability to intelligently interpret and contextualize user commands.
• Delivering a working prototype within the intense time constraints of Fullyhacks 2025.

🚀 Altivue

Inspiration

Our inspiration came from imagining a future where anyone can effortlessly control and interact with advanced drone technology. We were fascinated by the idea of integrating conversational AI with autonomous vehicles, envisioning a tool that could intelligently interpret its surroundings. Driven by the limitless possibilities of space exploration and technological frontiers, Altivue was born.

What It Does

Altivue is a personal patrolling drone controlled seamlessly via a laptop interface. It combines advanced AI—including YOLO v5 object detection and Retrieval-Augmented Generation (RAG)—to interpret commands, detect, and identify objects in real-time. Altivue recognizes numerous objects such as cars, people, animals, and everyday items, allowing it to function as an intelligent security patrol, safety monitor, or exploratory vehicle.

How We Built It

We integrated multiple cutting-edge technologies:

YOLO v5: Real-time and accurate object detection.
Tello Drone SDK: Robust flight capabilities and command responsiveness.
React, HTML, CSS: A visually engaging, space-themed user interface.
Retrieval-Augmented Generation (RAG): Intelligent interpretation and contextualization of complex user commands.

These components formed a cohesive drone system bridging autonomous flight with conversational robotics.

Challenges We Ran Into

Real-time Integration: Synchronizing YOLO’s object detection output with drone commands required precise timing and network optimization.
Network Stability: Ensuring a stable connection between our drone and laptop interface, especially while handling video streaming and live object detection simultaneously.
Model Performance: Balancing YOLO v5 perform