Delaware, USA
Mon - Fri : 09.00 AM - 04.00 PM
+1 786 630 29 64
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Completed Project

AI-Powered Object
Detection Robot

An autonomous robotic platform equipped with real-time computer vision and deep learning capabilities for intelligent object recognition and interaction.

Explore Features Request Similar
AI-Powered Object Detection Robot
6
Camera Modules
15
Object Classes
98%
Detection Rate
10
Weeks to Build

System Architecture

Vision Input

Multi-angle camera array with depth sensing and infrared capabilities

Edge Processing

NVIDIA Jetson Nano running optimized neural network inference pipelines

Motion Control

6-axis servo system with PID controllers for precise robotic movement

Command Center

Web-based control panel with live video feed and telemetry dashboard

Core Capabilities

Real-Time Detection

YOLOv5-based detection running at 30 FPS identifying 15+ object classes with bounding box visualization.

Path Planning

A* and RRT algorithms for autonomous navigation around obstacles with dynamic re-routing capabilities.

Object Manipulation

Gripper control with force feedback sensors for safe and precise pick-and-place operations.

Continuous Learning

On-device transfer learning allows the robot to recognize new objects with minimal training samples.

Multi-Camera Fusion

Stereoscopic vision combined with depth sensors for accurate 3D spatial mapping of the environment.

Remote Operation

Low-latency WebRTC streaming for remote control with keyboard, gamepad, or mobile input support.

Robot Assembly
Robot Assembly - Final Build
Vision Module
Vision Module
Field Testing
Field Testing

Development Process

01
Mechanical Design

3D-printed chassis and component mounting designed in Fusion 360 with stress analysis simulation.

02
Vision Pipeline

Camera calibration, image preprocessing, and YOLOv5 model training on custom dataset of 5,000 images.

03
Motion System

Servo motor integration with inverse kinematics for smooth 6-DOF arm movement and locomotion.

04
Software Integration

ROS2 framework connecting vision, planning, and control nodes with real-time message passing.

05
Field Testing

Iterative testing in controlled environments with progressively complex navigation and detection scenarios.

Project Results

Detection Accuracy98%
Processing Speed30 FPS
Navigation Success94%
Battery Life4.5 hrs
Object Manipulation91%
Tech Stack
NVIDIA Jetson Nano YOLOv5 OpenCV ROS2 Python TensorRT Fusion 360 Arduino LiDAR WebRTC Docker PyTorch

Ready to Build Your Intelligent Robot?

From autonomous navigation to computer vision, our team creates custom robotic solutions that push the boundaries of intelligent automation.

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Delaware, USA

+1 786 630 29 64

administrator@framtidr.com

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