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SeeND-G483-PS25

Repo for SeeND PS project

🤖 SeeND: Autonomous Indoor Delivery Rover

Team ID: G483
Institution: KMIT
Mentor: Mr. Sai Krishna
Team Members: Grandhi Navneet Sai, Kumar Datta, Naveen, Prranith, Rohith

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🧠 Project Overview

SeeND is an autonomous navigation rover that detects ArUco markers using a camera, estimates distance, and executes movement actions (move forward, turn, or stop) without GPS.

The rover uses:

• OpenCV (ArUco) for marker pose estimation
• Pixhawk IMU for yaw feedback
• Jetson Nano for onboard processing
• Flask Web Interface for live monitoring

Testing : It performs indoor navigation in a marker-guided sequence:

• ID 0 → Move forward → Turn right (80°)
• ID 1 → Move forward → Turn right (80°)
• ID 2 → Move forward → Stop completely

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🏗️ System Architecture

Layers:

1. Perception Layer: Camera + Pixhawk IMU
2. Decision Layer: Jetson Nano logic (Python threads)
3. Actuation Layer: DDSM Motor Controller
4. UI Layer: Flask web server for live dashboard

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🧩 Core Algorithm

Pose Estimation:

[rvec, tvec] = cv2.aruco.estimatePoseSingleMarkers(corners, marker_size, camera_matrix, dist_coeffs)
distance = float(tvec[0][0][2])



Installation
1 . Clone the repo
git clone https://github.com/kmitofficial/SeeND-G483-PS25.git
cd SeeND-Rover/src

2 . Install dependencies
pip install -r ../requirements.txt

3 . Connect Hardware

Pixhawk → /dev/ttyACM0

DDSM → /dev/ttyACM1

CSI camera → Jetson Nano MIPI port

4 . Run the code
python3 main_rover.py

5 . Open web interface

In your browser:

http://<JETSON_IP>:8080


**You’ll see:**

Live camera stream

Marker ID, distance, and status
>>>>>>> 690e822 (Initial commit - SeeND Rover Milestone 2 Submission)