AI-Controlled XYZ Plotter for Touchscreen

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an AI-Controlled XYZ Writing Plotter System with Camera ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for a developer to create an AI-controlled XYZ writing plotter system. Look no further; Zohaib is here to help you! My team has completed 50+ similar projects for automation and control systems. I will ensure the plotter interacts with the tablet accurately, using a capacitive stylus based on your commands. ➡️ Why Me? I can easily create your AI-controlled writing plotter system as I have 5 years of experience in automation, robotics, and software integration. My expertise includes hardware setup, software development, and system calibration. Additionally, I have a strong grip on relevant technologies like Python, C++, and IoT devices. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Programming ✅ C++ Development ✅ Hardware Integration ✅ Robotics Control ✅ System Calibration ✅ AI/LLM Integration ✅ Camera Setup ✅ Plotter Control ✅ User Interface Design ✅ Automation Solutions ✅ API Development ✅ Wireless Communication Waiting for your response! Best Regards, Zohaib

@Mahihassi

As a seasoned Electrical Engineer with a Master's in Embedded Systems, I am well-versed in all aspects of the project at hand, from hardware design to firmware development. My expertise with cutting-edge technologies such as ESP32-S3 Sense camera and AI/LLM systems make me an ideal fit for this project. Working at the intersection of AI and IoT development, I have ample experience in integrating natural-language-processing-capable AIs and LLMs into embedded systems. I've designed secure and scalable device-to-cloud pipelines using MQTT, REST, and WebSockets – skills that will contribute significantly to the communication and control layer between the AI/LLM, plotter, and camera. Additionally, my ability to think holistically about product development from concept to market-ready offering aligns perfectly with your requirement for a plug-and-play system. From creating detailed schematics to crafting optimized embedded coding for firmware development, my approach will ensure that the XYZ Plotter, Camera System, and PC Server are seamlessly synchronized for efficient operation. Consequently, by selecting me you are gaining an electrical engineer skilled not only in designing exceptional embedded system architectures but also in firmware development that facilitates success for marketers.

@umg536

Hello, With my extensive experience in developing sophisticated AI/ML, Embedded Systems, and IoT solutions, I am well-positioned to tackle the challenges of your AI-controlled XYZ plotter project. My proven ability to transform complex ideas into reliable systems that can seamlessly integrate various hardware, software, and data components will ensure a successful implementation of your requirements. Specifically, my expertise in a wide range of programming languages (including Python, C/C++, Java, C#, .NET), deep understanding of computer vision and machine learning algorithms (OpenCV, Neural Networks), as well as proficiency in Embedded Systems (FPGA/Verilog/VHDL, Raspberry Pi, Arduino) align perfectly with the needs of your project. I also have hands-on experience in building and integrating AI models like the one you require. Moreover, my track record in designing and implementing systems with capacitive touch technology combined with robotic movements will prove invaluable in ensuring the easy interaction between the XYZ plotter and the tablet touchscreen. I approach projects from a perspective of robustness and reliability which will be reflected in delivering a plug-and-play system that works flawlessly for you. Finally, I understand that effective communication is essential to any successful project completion; you can count on me for clear and timely updates throughout the development process. Thanks!

@diektech

Hi — Elias here from Miami. I understand the goal is a complete closed-loop system where an AI can see the tablet through the camera, decide what action is needed, physically tap/swipe/type with the plotter, then verify the result before continuing. The tricky part is calibration and repeatability. Camera coordinates, tablet screen coordinates, and plotter coordinates must remain accurately mapped; otherwise even strong AI vision will produce unreliable physical actions. I’d build a Windows control layer exposing high-level commands such as tap, swipe, home, stylus control, snapshot, calibration, and device status. The plotter can use USB initially for reliability, while the ESP32-S3 camera automatically connects/discovers the server over Wi-Fi. Calibration profiles would allow different tablet sizes without code changes. For AI, I’d use a vision-capable LLM with controlled tool/API calls following Observe → Decide → Act → Verify, with movement limits, retries, and error handling built in. I have experience with ESP32, hardware/device APIs, motion control, computer vision, Windows services, and LLM integration, and I’m comfortable receiving and physically testing the hardware. A few questions to better understand the scope: Q1 – Does the Bachin controller expose G-code over USB? Q2 – What tap accuracy do you require? Q3 – Should v1 support Android tablets only, or iPad too? Looking forward to hearing from you.

@RaspberryOculus

With 27 years of hands-on experience in Systems Architecture, Physical Layer design, and real-world problem-solving, I can confidently offer my expertise for your XYZ Plotter project. Having implemented robust systems for critical applications like Military Submarines and Combat Rovers, I'm deeply familiar with the nuances of reliable, high-accuracy hardware integration— a necessity for your AI-controlled plotter. Further, my proficiency spans diverse domains which include RF Communications (Anti-jamming & RF Communication), FPGAs, Embedded SoCs (ESP32-S3), Thermal Analysis, and more. My comprehensive skill set also includes Artificial Intelligence—a vital requirement as your system would leverage AI for interpreting commands and verifying actions. My work spaning Edge-AI inference are especially relevant here as I am familiar with implementing real-time inference under thermal constraints. Additionally, my specific knowledge in the software and electronics side will be exceptionally useful while developing wireless control options like the planned WiFi control in the project.

@ahmadayaz

After reviewing the project requirements for an AI-Controlled XYZ Writing Plotter + Camera System, I am confident in my ability to develop a solution that aligns with your goals. My expertise in software development, hardware integration, and AI technologies uniquely positions me to create a reliable and scalable system. I plan to use state-of-the-art technologies to enable the AI to control the plotter, interpret commands, and utilize the camera for verification. Close collaboration on hardware selection will ensure accuracy, reliability, and ease of use. Thorough testing and quality assurance will be conducted to guarantee optimal system performance. Upon completion, you can expect a fully operational system with comprehensive documentation, source code, setup instructions, and a video demonstration. I am dedicated to delivering a high-quality solution and look forward to the opportunity to collaborate on this innovative project.

@pauldinhdev

Hi, I am a embedded system engineer based in Canada. I read the brief carefully — the core challenges are reliable camera-to-screen calibration, a deterministic plotter motion/homing routine, and a robust observe->act->verify loop for an LLM to drive physical touches. I've built similar small-robot + vision integrations where an external model issued high-level commands and the system handled calibration, homing limits, and retry logic. For this project I'd focus first on accurate screen mapping (camera pose + calibration), then on conservative motion/homing and failure/retry states so the AI doesn't “drift” on the touchscreen. Key pieces I'd deliver: ESP32‑S3 snapshot service and auto-discovery, Windows server that exposes simple plotter commands (move/tap/swipe/home), persistent calibration storage, and a demo LLM integration that completes multi-step touch tasks and verifies results. A couple of quick questions: 1. Do you expect the tablet to be in a fixed enclosure during use, or will its position vary between sessions? 2. Any constraints on acceptable tare/placement error for taps (mm)? I'm available for a video call to walk through hardware choices and the test plan. Thanks, Paul

@shadabkhan92

Drawing upon my years of experience in AI and Embedded Systems, I am confident that I can deliver a reliable and efficient solution for your AI-Controlled XYZ Plotter project. My proficiency lies in developing a wide range of complex systems - from integrating AI models to working with hardware like ESP32/STM32 to ensure smooth functioning throughout. What differentiates me from other candidates is my end-to-end approach; not only can I create the server application that facilitates communication between the AI/LLM, plotter, and camera, but I can also design and manufacture custom IoT hardware if necessary. This comes in handy when implementing bespoke configurations like yours, where strict camera-to-screen and plotter calibration are vital. Moreover, given that your intended workflow involves real-time decision making through the AI interpreting high-level commands, my competence in using Python - a powerful language for machine learning and system control - will be invaluable. Whether it's making the stylus tap, swipe or type on-screen or even moving the plotter within its working limits reliably, my expertise covers them all. I assure you optimized calibrations, timely image requests for on-the-spot verification, and seamless interactions between the hardware and software components.

@maxscender

I can help you turn this into a functional, plug-and-play system rather than a collection of components that need babysitting. The core challenge isn't just wiring parts together—it's making the vision-to-action loop reliable enough that the AI doesn't "tap" blind. I'll focus on building a stable coordinate-mapping pipeline between camera view, screen coordinates, and plotter movement, so when the AI sees a button, the stylus lands on it correctly every time. I'll use standard protocols (G-code for the plotter, MQTT/HTTP for the camera) to keep the system modular and non-proprietary. The Windows server will be the single integration point, handling device discovery and reconnection automatically so you don't have to reconfigure anything on power-up. The AI integration will use a vision-capable LLM that can receive camera snapshots, interpret them, and issue plotter commands through a simple API—designed so the control layer can be swapped or upgraded as better models come out. The trickiest part is calibration drift: cameras shift, steppers skip. I'll build in a homing routine and periodic visual reference checks so the system self-corrects instead of slowly losing accuracy over time. I'm prepared to do the physical assembly and testing myself to make sure it actually works before you see it back.

@vishal1145

Your ESP32-S3 camera will drop frames during rapid plotter movements if you're polling for JPEG snapshots without implementing a buffered queue system. This will cause the AI to make decisions on stale screen data, leading to tap misalignment. Quick questions - are you planning to run the LLM locally (Ollama/LLaMA) or use a cloud API (OpenAI/Anthropic)? And what's your acceptable latency between command and physical action - under 2 seconds or can it be 5-10 seconds per cycle? Here is the architectural approach: - PLOTTER CONTROL: Build firmware for ESP32/STM32 that accepts G-code-style commands over serial/Wi-Fi with homing sequences and soft limits to prevent crashes. - CAMERA INTEGRATION: Mount ESP32-S3 Sense with fixed calibration markers, implement MJPEG streaming with auto-reconnect logic, and sync image capture timestamps with plotter position data. - AI ORCHESTRATION: Create Python server using FastAPI that bridges LLM vision API calls, manages plotter command queue, and handles screen-to-plotter coordinate transformation using OpenCV calibration matrices. I've built similar vision-guided robotic systems for manufacturing QA that required sub-millimeter precision under variable lighting. Let's schedule a 20-minute technical call to finalize hardware selection and discuss calibration strategy before you purchase components.

@juanpablomacf

The key challenge here is making the camera, touchscreen calibration, plotter motion and LLM control loop reliable as one system, rather than treating them as separate components. I’ve worked across Python/C++, ESP32 firmware, computer vision and LLM integrations, including hardware-control workflows where software decisions are translated into precise physical actions. I’d build the Windows control layer around a device abstraction/API, with automatic discovery, saved calibration profiles, homing/limits and camera verification after each action. I’d start by bench-testing the Bachin plotter and ESP32 camera, then establish coordinate calibration and reliable tap/swipe primitives before connecting the vision/LLM agent. Would you prefer the AI to use screenshot-based coordinate detection or a vision model with UI-element understanding? Should the camera stream snapshots only, or support a continuous low-rate preview? Do you already have a preferred tablet size for the first calibration? Juan Pablo

@Specgi

I am Usman Haider, a highly skilled programmer with extensive experience in the fields of Artificial Intelligence and Machine Learning, precisely what your project calls for. My expertise in powerful technologies like neural networks and conversational processing align perfectly with your requirements to enable normal language command interpretation and response through the plotter. Furthermore, my proficiency in data analytics possesses adequate potential to implement the critical camera-based verification steps replacing human intervention. Having worked with top organizations in diverse industries, I bring robust programming skills in languages like C++ and Python necessary for your Windows PC server development. My understanding of cloud platforms like Google Cloud Vision ensures smooth integration with your existing software while maintaining all essential calibration settings effectively. In short, if chosen for this task, I promise a plug-and-play system that not only controls an XYZ plotter accurately but also makes it communicate seamlessly with the camera system to ensure error-free observations and verifications.

@mayr81

You’re building a plug-and-play AI-controlled touchscreen interaction loop (AI/LLM → camera observation → plotter actions → camera verification). I can deliver the full Windows PC server that exposes clean plotter/camera control APIs, handles automatic discovery/reconnection for the ESP32-S3 Sense camera, and persists calibration/settings across restarts. For reliability and repeatability, I’ll implement: (1) plotter homing + movement limits, (2) screen-to-plotter calibration (mapping touchscreen pixel coordinates to XYZ steps), (3) a command execution layer that supports tap/swipe/button press/keyboard typing, (4) camera snapshot request/streaming with auto-reconnect, and (5) an orchestration workflow that repeatedly captures a frame, updates intent, issues plotter motions, then verifies via the next camera observation until completion. The AI/LLM integration will be structured so you can provide natural-language commands and the system converts them into deterministic UI action primitives. Architecture will be extensible for additional plotters/cameras via device identity and a modular driver interface. Source code, buildable projects, calibration artifacts, and a short usage guide will be included.

@jonasdedzo

Hello, I think you’re building much more than an XYZ plotter—you’re creating a complete AI-driven robotic interaction platform where an LLM can observe a touchscreen, make decisions, physically interact with it, and verify the results autonomously. I can design this as a modular system consisting of an ESP32-S3 camera service, a Windows control server, a plotter control layer, a calibration engine, and an LLM integration layer connected through a clean API. The workflow will support automatic device discovery, persistent calibration, robust reconnection logic, camera feedback, touchscreen coordinate mapping, and scalable multi-device support for future expansion. Since reliability is your highest priority, development will focus on deterministic hardware control, accurate camera-to-screen calibration, comprehensive logging, and recovery from communication failures before optimizing AI decision loops. I can also document the complete architecture and provide tested source code, firmware, deployment instructions, and demonstration videos. **Milestones:** * **M1:** Hardware integration, firmware, Windows server, device discovery, and calibration * **M2:** Plotter/camera control API, AI/LLM integration, autonomous touchscreen interaction, and validation * **M3:** End-to-end testing, optimization, documentation, video demonstration, packaging, and hardware return Looking forward to working with you. Thanks!

@johibkhan

Hello, I have more than 15 years of experience in C/C++, Python, embedded systems, IoT, AI automation, computer vision, and hardware/software integration. Relevant Embedded/IoT Project: https://www.freelancer.com/projects/data-analysis/UDS-Diagnostic-Test-Bench/details I can develop the complete AI-controlled XYZ plotter system, including ESP32-S3 camera firmware, Windows control software, plotter movement/tap/swipe control, touchscreen calibration, automatic device discovery/reconnection, and AI/LLM integration. The workflow will support: User Command → Camera Observation → AI Decision → Plotter Action → Camera Verification. I can also physically integrate and test the hardware, including accurate tapping, swiping, on-screen keyboard interaction, homing, calibration, and automatic startup. Before hardware purchase, I recommend confirming the exact XYZ plotter/controller so we can select the most reliable configuration. Full source code, firmware, documentation, tested builds, and demonstration will be provided. Thanks, Johib

@Ryan15222

Hi, I’m an embedded systems and automation engineer experienced with ESP32, Python/C++, computer vision, motion control, Wi-Fi/USB communication, and AI/LLM integration. This project is a strong match for my background. I can develop and physically test the complete AI-controlled touchscreen system, including: • ESP32-S3 Sense camera firmware with auto Wi-Fi connection/reconnection • XYZ plotter control via USB, with Wi-Fi option if practical • Homing, movement limits and stylus control • Screen-to-plotter and camera-to-screen calibration • Windows 10/11 control server/application • Automatic device discovery and persistent configuration • AI/LLM integration with vision capabilities • Tap, swipe, button selection and on-screen keyboard interaction • Closed-loop workflow: Observe → Plan → Act → Verify • Source code, compiled firmware/software and buildable projects • Setup documentation and demonstration video I would use a controlled API between the AI and hardware so the AI can request camera images, identify screen elements, generate plotter actions, and verify the result after each operation. I can physically assemble, calibrate, and test the supplied hardware and demonstrate the complete workflow before shipping the finished system back to you. Best regards,

@juanestebana17

Hello! @WHY I BEST FIT@ With several years of experience in software development, I specialize in Python, C++, and computer vision technologies required for your AI-controlled XYZ Plotter project. I have developed automation systems involving embedded hardware and camera integrations, ensuring reliable operation. @YOUR PROJECT@ I understand your goal of creating an AI-driven, plug-and-play system for precise touchscreen manipulation and verification. This involves real-time camera observation, accurate calibration, and control of hardware. @HOW TO COMPLETE@ I will develop robust control software in Python for your Windows PC, integrating computer vision to interpret camera feeds and control algorithms in C++ for precise plotter movements. I'll ensure wireless control and auto-reconnection features for robustness, and calibrate the system meticulously for accuracy. Thanks!

@longngoc2307

Hi - Truong here "AI-CONTROLLED TOUCHSCREEN PLOTTER" — you need the full loop working reliably: see the tablet, act on it, then verify the result. I’d split the system into three clear layers: ESP32 camera, Windows device-control server, and the AI action loop. Calibration would map camera coordinates to plotter coordinates, while homing and movement limits prevent bad commands from pushing the stylus outside the working area. The important edge case is recovery: after Wi-Fi, camera, or plotter disconnects, the system should rediscover the device and restore its saved calibration without manual IP setup. Which exact XYZ plotter/controller will be shipped for the initial build?

@rezamahi

I’m a Mechatronics & Robotics Engineer with 4+ years of hands-on experience and 27+ completed robotics, embedded, automation, and AI-integrated projects. I’ve also worked on a very similar type of AI-controlled physical interaction system, so this project is closely aligned with my experience. I can build the complete end-to-end workflow: Natural-language command → AI/LLM → Camera observation → Coordinate/action planning → XYZ plotter → Capacitive stylus → Camera verification I can handle the ESP32-S3 Sense firmware, Windows control/server application, plotter communication, homing and limits, touchscreen calibration, camera integration, automatic device discovery/reconnection, and AI/LLM integration. For the software architecture, I would design the system around clear device APIs so the AI can request a camera snapshot, identify the required screen interaction, convert screen coordinates into calibrated plotter coordinates, execute tap/swipe/type actions, and then verify the result with another camera observation. I’ll also focus heavily on reliability: persistent calibration/configuration, startup recovery, safe motion limits, automatic reconnection, device identity, and repeatable positioning. I’m comfortable physically assembling, calibrating, and testing the hardware rather than delivering only software. I can demonstrate tapping, swiping, keyboard interaction, AI-driven commands, verification, and automatic startup/reconnection before final delivery.