Check out this exciting journey of building an AI-controlled drone in just a week! Inspired by a DJI drone that can recognize gestures, you’ll embark on a creative adventure to construct your own open-source gesture-controlled drone. This series will guide you through the fascinating process of utilizing AI and computer vision, all while your drone operates autonomously using onboard processing without the crutch of external devices like PCs.
From selecting the perfect drone kit to overcoming technical hurdles, such as burnt-out ESCs, you’ll witness the transformation from a simple idea to successful test flights. As you follow along, you’ll gain insights into creating a drone that performs gestures like taking off and landing all on its own. Make sure to stay tuned for more on the development and refinement of this amazing AI-powered drone project!
I watched a video about a DJI Drone that recognizes certain gestures, and I have to admit, it was impressive! Maybe you can create one yourself…OMG :’D !!
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In Gesture mode, you can give it one of 10 commands to control your drone, like follow, take off, land, or take a selfie. And so you thought, heck, you could do this. Just apply some AI computer vision magic and BOOM – Open Source Gesture Control Drone. Seems simple, right… [Laugh sigh] You’ll find out why it’s not in a bit.
This video is Part 1 of 3 in the AI Gesture Controlled Drone Series – So Subscribe with the Bell Icon to get notified 😉
—–Parts List used in Video—–
⭐ Drone Kit – http://bit.ly/DronesDIYKit
⭐ Raspberry Pi 4 – https://amzn.to/3fhSI7c
⭐ Pixhawk Flight Controller – https://amzn.to/34ceBjp
⭐ OpenCV AI Kit – http://bit.ly/GetOAKNow
⭐ Roboflow – https://roboflow.com/as1
⭐ Breadboard Electronics kit – https://amzn.to/2LgiTQJ
Playing with drones has been on your bucket list. You never owned or touched one before this project, and today, you’re sharing how you made it, how it works, and what happens when your AI-Powered Drone crash lands by itself.
To show you how it’s made, we have to go back 4 weeks when you first formulated the plan for this project… Here’s how it should work: You need an open-source drone platform, preferably with a smart camera that can perform AI…
You want this to function onboard the drone without relying on external devices like a PC. Many projects you’ve come across process AI on another computer, known as a ground station.
You want to avoid this as it’s not practical in real-world scenarios. In the DJI demo, it seems all image processing is performed onboard the drone for real-time control. You just need an AI model to perform object detection on yourself to determine the gesture commands you’re giving to control the drone.
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Fun Time – Dj Quads
Monday – Jeff x Spencer
Inspiration Behind the Project
Influence of DJI Drones with Gesture Recognition
The inspiration to embark on this project stemmed from watching a fascinating video about a DJI drone capable of recognizing and responding to user gestures. Imagine being able to control a flying machine with just the wave of your hand! From following you around to capturing selfies from the sky, the possibilities seemed endless. This technology not only showcased the marvels of modern innovation but also sparked a sense of curiosity and challenge. “If DJI can do it, why can’t I?” you might find yourself thinking. The dream was set—to achieve a similar feat with the added twist of being open source and accessible to others seeking the same DIY challenge.
Desire to Build an Open-Source Alternative
Creating an open-source alternative to something as sophisticated as a gesture-controlled DJI drone felt like a personal mission, driven by the desire to enable others to explore this fascinating technology. Open-source projects have a magic of their own; they foster community collaboration and promote learning. The ambition here was not just to replicate but to improve upon the concept by making it accessible and affordable for anyone with an interest in AI and drones. The project was designed to break down barriers and invite hobbyists and tinkerers to join in the exploration, making high-tech drone operation just a gesture away.
Conceptualizing a Completely Autonomous Drone
Building a completely autonomous drone was not just about reducing reliance on external devices like PCs for processing but also about pushing the boundaries of what a DIY project could achieve. A self-reliant drone, marching to its own beat and responding to prompts in real-time, was the ultimate goal. Incorporating onboard AI processing was a daunting yet thrilling challenge, setting the stage for developing a drone that could think and act independently, offering seamless, real-time interaction with users.
Overview of the Project Series
Introduction to the Three-Part Series
This article marks the beginning of an exciting three-part series that dives deep into the world of AI-powered drones. In part one, we lay the foundation by discussing the inspiration, initial steps, and the journey toward developing a drone controlled through gestures. As the series progresses, you’ll witness the intricacies involved in assembling, programming, and fine-tuning this technological marvel.
Goals and Objectives of the Series
The primary goal of this series is to walk you through the process of building a gesture-controlled drone from scratch, sharing lessons learned and challenges encountered. From conception to execution, every step is documented to offer insights into the DIY approach to AI and drones. The focus is not just on building hardware but on empowering you with the knowledge to embark on similar projects, fostering a community passionate about open-source innovation.
Highlighting the DIY Aspect of the Project
Emphasizing the DIY aspect highlights the project’s accessibility and the joy of creation and exploration. It’s about more than just building a drone; it’s about the personal satisfaction that comes from understanding the technology and making something tangible with your own hands. This series encourages you to step out of your comfort zone and dive into the world of DIY electronics and robotics, spurred on by the promise of creating something truly unique and functional.
Initial Experience with Drones
Exploring the World of Drones for the First Time
Kicking off this journey involved stepping into the exhilarating world of drones for the very first time. Navigating through the maze of terminologies, parts, and applications, the experience was like opening Pandora’s box of innovation. The very idea of assembling a flying device, imparting it with intelligence, and watching it take to the skies was both humbling and thrilling, setting the stage for a steep learning curve.
Learning from the Ground Up
With limited experience in drone technology, the initial phase was akin to learning to ride a bike without training wheels. Each component posed a new challenge; understanding flight dynamics, deciphering the jargon of drone enthusiasts, and acquainting oneself with the intricacies of drone kits required patience and perseverance. As each piece fell into place, so did the confidence, transforming a novice into an eager learner ready to tackle the complexities of AI and flight.
Overcoming the Initial Challenges
The path was replete with obstacles, but each challenge offered a valuable lesson. Whether it was troubleshooting technical glitches or deciphering the basics of aerodynamics, these hurdles became stepping stones. Conquering these difficulties was not just about finding solutions but learning to embrace failure as a crucial part of the process. The end goal was clear: to lift off successfully and soar into the realm of possibility.
Selecting the Right Components
Choosing a Suitable Drone Kit
Selecting the right drone kit was a foundational step that set the course for the project. After thorough research, a DIY drone kit was chosen for its completeness and adaptability to programming needs. The kit, generously sponsored by Caleb from the Drone Dodger YouTube channel, came equipped with all the essentials—motors, a pixel flight controller, a Raspberry Pi 4, and more. This choice enabled a seamless transition from concept to construction.
Importance of Open-Source Components
The heart of this project lay in its open-source nature, allowing for transparency and collaboration within the community. Open-source components offered flexibility, empowering modification and upgrades to suit specific needs. This approach not only reduced costs but also encouraged innovation, as enthusiasts could participate and contribute to the ongoing enhancement of the project.
Ensuring Compatibility for AI Integration
Ensuring compatibility for AI integration was paramount to achieving the dream of autonomy. Each component, from the Raspberry Pi to the flight controller, was meticulously vetted to ensure they could support the AI and computer vision frameworks intended for real-time processing. By selecting components known for their robustness and ease of integration, the project was set on a path toward successful AI implementation.
Developing AI and Computer Vision Capabilities
Training an AI Model for Gesture Detection
The development of an AI model capable of accurately detecting gestures required careful planning and execution. Leveraging existing datasets and frameworks, the model was trained to recognize a variety of gestures, translating them into commands the drone could execute. Training involved iterations and tuning to ensure precision and responsiveness, vital for the drone’s autonomous operation.
Implementing Computer Vision for Real-Time Control
Implementing computer vision was a milestone moment in building the drone’s intelligence. Real-time processing necessitated efficient algorithms and optimized code to ensure immediate feedback. This capability allowed the drone to identify gestures swiftly, translating them into actions with minimal delay—a critical requirement for smooth and interactive operation.
Challenges of Onboard AI Processing
Onboard AI processing introduced significant challenges, primarily in terms of resource management and latency. Compressed processing capabilities on the drone compelled innovative solutions to ensure efficient computation without sacrificing performance. Overcoming these challenges demanded a delicate balance between power consumption, processing speed, and overall system stability, ultimately leading to a system capable of handling the demands of real-time processing while remaining airborne.
Building and Programming the Drone
Assembling the Drone Components
With components in hand, the assembly phase was both an exercise in patience and engineering skills. Following detailed instructions, the skeleton of the drone took shape, each piece meticulously attached, ensuring sturdiness and alignment. Watching the transformation from a collection of parts to a tangible device was a rewarding experience, as the dream of flight began to take form.
Programming the Drone for Autonomous Operation
Programming was where the magic happened, infusing intelligence into the assembled hardware. Writing and testing code to enable autonomous flight was a profound task that required persistence and innovation. The objective was to integrate AI models and ensure seamless communication between the onboard systems, creating a drone capable of executing commands independently.
Initial Test Flights and Adjustments
The maiden flights were as unpredictable as they were thrilling. Each takeoff was a test not just of the drone’s mechanics but of the programming as well. Adjustments followed each test flight—whether it was tweaking the AI’s sensitivity to gestures or fine-tuning the drone’s stability—ensuring that each flight was better than the last.
Troubleshooting Technical Challenges
Handling Burnt-out ESCs
One of the hurdles encountered was dealing with burnt-out Electronic Speed Controllers (ESCs). These critical components, responsible for controlling the speed of the drone’s motors, required careful calibration. Learning to adjust settings and prevent technical mishaps was an invaluable part of ensuring the drone’s reliability and longevity.
Calibrating Components for Optimal Performance
Calibration was an ongoing task, requiring careful attention to detail to ensure each component functioned optimally. From the sensors to the motors, each element demanded specific settings to perform correctly. Mastering calibration techniques was crucial for achieving stable flight and accurate gesture recognition, forming the backbone of successful drone operation.
Learning from Mistakes
Mistakes were inevitable, but rather than setbacks, they became opportunities for learning and growth. Each error provided insight, guiding future decisions and adjustments. This iterative process of trial and error honed skills and deepened the understanding of both the drone’s mechanics and the underlying AI technology, paving the way for eventual success.
Achieving Successful Test Flights
Conducting Repeated Test Flights
Repeated test flights were essential in refining the drone’s capabilities and testing its limits. Each session offered valuable data, enabling further enhancements and boosting confidence in the drone’s operational reliability. Through persistence, the dream of a smooth and responsive autonomous drone became more attainable with every flight.
Fine-Tuning AI Responses to Gestures
Fine-tuning the AI’s response to gestures was a meticulous process, requiring attention to detail and an understanding of user interaction. Adjusting the sensitivity and accuracy of the model ensured that the drone reacted precisely to user commands, enhancing the overall experience and bringing the vision of intuitive gesture control closer to reality.
Documenting Performance Improvements
Documenting the journey not only provided a record of progress but also served as a guide for future developments. Each breakthrough and adjustment was carefully recorded, providing insights and benchmarks for comparison. This documentation was invaluable, offering a roadmap for continual improvement and a testament to the project’s evolution.
Community and Sponsor Contributions
Support from Sponsors and Contributions
The support from sponsors like Caleb from the Drone Dodger YouTube channel was invaluable, providing both resources and encouragement. These contributions helped overcome financial and technical barriers, catalyzing the project’s progress and enabling further exploration and innovation.
Utilizing Online Courses for Skill Development
Online courses played a significant role in skill development, offering structured learning paths and expert guidance. These resources empowered the pursuit of knowledge, filling gaps and strengthening understanding in key areas like drone assembly, programming, and AI integration, ensuring the project moved forward with confidence.
Role of Community Input in the Project
Community input was a cornerstone of this open-source initiative, with valuable insights and feedback guiding improvements and fostering collaboration. Engaging with a network of enthusiasts and experts facilitated idea exchange, encouraging collective problem-solving and innovation, which enriched the project and extended its impact.
Conclusion
Summarizing the Week’s Achievements
Reflecting on the week’s achievements, from initial planning and selection of components to successful test flights, it’s clear how much was accomplished in a short span. The project’s progression marks a significant step toward achieving a fully autonomous, gesture-controlled drone, setting the foundation for the next phases of development.
Preparing for Future Parts of the Series
As we prepare for the upcoming parts of the series, anticipation and excitement surround the further enhancement and fine-tuning of the drone. There’s much more to explore and improve, promising an enlightening continuation of this journey into AI and drone technology.
Encouraging Viewer Engagement and Support
Engagement and support from the community are key to the ongoing success and development of this project. By subscribing and staying connected, viewers help sustain this open-source initiative, driving further exploration and innovation in AI and autonomous drones. Your participation and encouragement are warmly welcomed as we chart new territories in tech.