Browse categories
Explore
Fiverr Pro
English
$
USD
I will develop ros2 based edge ai robotics system using jetson and rpi
AI, DeepStream, and Robotics solutions optimized for your edge devices
With 20+ years of industry experience, I develop ROS 2 + NVIDIA DeepStream AI solutions for robotics and computer vision. I specialize in real-time object detection, tracking, and action recognition, ...
About this Gig
Are you looking to integrate AI at the edge for your robotics application? I specialize in building hardware-accelerated Edge AI solutions using ROS2, perfectly tailored for real-time, low-latency robotic systems.
As a robotics and ROS2 expert, I will help you deploy smart robotic applications directly on edge hardware like Jetson Nano, Xavier, Raspberry Pi, Coral TPU, and more enabling offline inference, sensor fusion, and real-time control using advanced AI models.
What I Offer:
- ROS2-based robotic software architecture
- Integration of AI/ML models with ROS2 pipelines
- Hardware interfacing (sensors, actuators, cameras, etc.)
- Edge deployment on Jetson, Raspberry Pi, etc.
- Real-time control, object detection, pose estimation, voice commands
- Optimization of AI models for edge (TensorRT, ONNX, quantization)
- Custom launch files, nodes, and system integration
- Dockerized ROS2 environments for easy deployment
Technologies I Use:
- ROS2 Humble / Iron / Rolling
- OpenCV, TensorFlow Lite, PyTorch, YOLO, DeepStream
- DDS communication for distributed systems
- MQTT, WebRTC for remote monitoring/control
- RTOS or Ubuntu Core on embedded devices
Ideal For:
- Robotics startups & research labs
Platform:
NVIDIA Jetson
Sensors:
Accelerometer
•
Ultrasonic
•
Infrared
•
Camera
FAQ
Q1: What kind of hardware platforms do you support?
A: I support a wide range of edge devices including NVIDIA Jetson Nano/Xavier, Raspberry Pi 4, Intel NUC, Coral Dev Board, and other ARM/x86-based platforms.
Q2: Can you deploy AI models like YOLO or pose estimation on edge devices?
A: Yes! I can optimize and deploy models such as YOLOv5, YOLOv8, MoveNet, and custom CNNs using TensorRT, ONNX, or TensorFlow Lite for efficient edge inference.
Q3: Will you integrate the AI model into a ROS2 node?
A: Absolutely. I will create or modify ROS2 nodes to wrap your AI model, publish inference results, and integrate it with your robotic control pipeline.
Q4: Do you provide hardware interfacing like controlling motors or reading sensors?
A: Yes, I can interface your hardware (e.g., motors, cameras, LIDAR, IMUs, etc.) with ROS2 using custom or standard drivers.
Q5: I already have an AI model trained — can you deploy it on my hardware?
A: Definitely! Just share the model file and format, and I’ll handle the conversion, optimization, and ROS2 integration for edge deployment.
