https://github.com/surdubey/steadysync-equilibria
SteadySync-Equilibria: Visual Servoing for Kinova Gen3 ROS 1 Noetic-based image-based visual servoing (IBVS) for the Kinova Gen3 arm. Uses ORB + RANSAC for rectangle detection, adjusts position dynamically, and integrates with MoveIt! & Gazebo. 🚀
https://github.com/surdubey/steadysync-equilibria
gazebo ibvs kinova opencv python ros rviz
Last synced: 3 months ago
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SteadySync-Equilibria: Visual Servoing for Kinova Gen3 ROS 1 Noetic-based image-based visual servoing (IBVS) for the Kinova Gen3 arm. Uses ORB + RANSAC for rectangle detection, adjusts position dynamically, and integrates with MoveIt! & Gazebo. 🚀
- Host: GitHub
- URL: https://github.com/surdubey/steadysync-equilibria
- Owner: SurDubey
- Created: 2025-03-22T13:45:13.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-22T14:27:00.000Z (over 1 year ago)
- Last Synced: 2025-03-22T15:27:34.292Z (over 1 year ago)
- Topics: gazebo, ibvs, kinova, opencv, python, ros, rviz
- Language: Python
- Homepage:
- Size: 43 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SteadySync-Equilibria
## Overview
This project implements **visual servoing** for a **Kinova Gen3 7-DOF robotic arm** in **ROS 1 Noetic**. The robot uses **image-based visual servoing (IBVS)** to detect and track a rectangular object using its **end-effector camera**. The system extracts **corner points** of the rectangle and adjusts the robot's position dynamically to align with a predefined reference.
## Features
- **Real-time Rectangle Detection**: Uses **ORB feature detection and RANSAC filtering** for robust corner point detection.
- **ROS Integration**: Subscribes to **camera image topics**, processes frames in **OpenCV**, and publishes **detected features**.
- **Trajectory Control**: Uses **FollowJointTrajectoryAction** to move the Kinova Gen3 arm.
- **Visual Servoing Loop**: Computes **position corrections** based on detected rectangle corner deviations.
- **Gazebo Simulation Support**: Can run in both **real hardware** and **Ignition Gazebo**.
## System Architecture
```
+------------------------------------------------------+
| ROS Framework |
+------------------------------------------------------+
| Image Capture | Feature Detection | Control |
| (Camera Topic) | (ORB + RANSAC) | (MoveIt!)|
+------------------------------------------------------+
| Kinova Gen3 Robot Arm |
+------------------------------------------------------+
```
## Dependencies
Ensure you have the following installed:
- **ROS 1 Noetic**
- **MoveIt!**
- **OpenCV (cv\_bridge, image\_transport)**
- **Kinova ROS packages** (`kortex_driver`, `kortex_examples`)
- **Gazebo Fortress/Garden** (for simulation)
### Installation
```bash
# Clone your workspace and install dependencies
cd ~/Desktop/robot_ws/src
git clone https://github.com/your-repo/your-project.git
cd ..
rosdep install --from-paths src --ignore-src -r -y
catkin_make
source devel/setup.bash
```
## Running the Project
### 1. **Launch Gazebo Simulation**
```bash
roslaunch kortex_examples gazebo3.launch
```
### 2. **Start the Visual Servoing Node**
```bash
rosrun kortex_examples visual_servoing.py
```
### 3. **View Processed Images**
```bash
rqt_image_view /edge_detected_image
```
### 4. **Move Robot to Initial Position**
```bash
rostopic pub /move_robot std_msgs/Empty {}
```
## How it Works
1. **Capturing Reference Image**: The arm moves to **position-1** and captures a reference image.
2. **Real-time Processing**: The camera continuously captures frames and detects the largest rectangle.
3. **Feature Extraction**: ORB detects corner points, filtered using **RANSAC** for robustness.
4. **Error Computation**: Compares current rectangle corners with the reference and computes an error vector.
5. **Robot Adjustment**: Adjusts the arm’s position iteratively to align with the reference.
6. **Convergence**: The process stops once the alignment error falls below a threshold.
## Expected Results
- The robot should **accurately align** with the rectangle even with slight perturbations.
- Robust corner detection should work even in **varying lighting conditions**.
## Future Improvements
- **Refine ORB & RANSAC parameters** for better robustness.
- Implement **deep learning-based corner detection** for improved accuracy.
- Extend to **6-DOF pose estimation** instead of just 2D alignment.
## Authors
- **Your Name** – *Surabhi Dwivedi, Deepraj Majumdar*
- **Institution/Organization** – *Indian Institute of Technology, Jodhpur*
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