🎉 Autonomus harvesting using Object Detecion

🎉 Autonomus harvesting using Object Detecion#

Detecting cucumbers and we generate specific actions for two types of robots: the ViperX 300s arm robot and an Automated Guided Vehicle (AGV) robot.

task

Cucumber harvesting using Object Detecion#

The development of a cucumber harvesting system that utilizes custom object detection with YOLOv11. After detecting cucumbers, we generate specific actions for two types of robots: the ViperX 300s arm robot and an Automated Guided Vehicle (AGV) robot.

The primary aim of this project is to create a comprehensive dataset that captures both the actions performed by the robots and the images taken during the harvesting process. This dataset is a crucial resource for developing and refining algorithms that will enhance future robotic harvesting techniques.

By systematically recording a wide array of interactions and scenarios, we not only improve the efficiency of current systems but also lay a robust foundation for future advancements in agricultural robotics. This initiative represents a significant step forward in automating and optimizing the harvesting process through the integration of advanced machine learning models and robotic technology.

🛠️ Prerequisites#

  • Ubuntu 20.04 🐧

  • Interbotix Packages 🤖

  • Python 🐍

  • ROS 🤖

  • interbotix_ws : -

🚀 Installation#

To get started with this frame work, follow these steps:

git clone https://github.com/sainavaneet/Harvesting.git

cd Harvesting/

pip install -e .

🗂 Project Structure#

├── base_control
│   ├── agv_control.py
│   ├── examples
│   │   ├── move_6s_back.py
│   │   ├── move_6s_forward.py
│   │   ├── move_base.py
│   │   ├── odom_cal.py
│   │   └── original.py
│   ├── gui_control.py
│   └── __pycache__
│       └── agv_control.cpython-38.pyc
├── config
│   └── vx300s.yaml
├── harvest.py
├── images
│   └── obj_detection.png
├── index.md
├── __init__.py
├── launch
│   └── robot.launch
├── object_detection
│   ├── dataset
│   │   └── Cucumber.v2i.yolov11.zip
│   ├── detection_realsenes.py
│   └── weights
│       ├── best.pt
│       └── last.pt
├── __pycache__
│   └── var.cpython-38.pyc
├── README.md
├── requirements.txt
├── robot_utils.py
├── rviz
│   ├── puppet_left.rviz
│   └── rviz.rviz
├── setup.py
├── sleep.py
├── transform_co.py
├── utilities.py
├── var.py
└── videos
    ├── 1.mp4
    ├── 2.mp4
    ├── 3.mp4
    └── 4.mp4

Launch#

source interbotix_ws/devel.setup.bash

cd ~/Harvesting/launch/

roslaunch robot.launch use_rviz:=false use_sim:=False # if you need in simulation use True

Object Detection#

The object detection files are located in the /object_detection directory.

By executing the detection_realsense.py script, cucumbers can be detected. We have designed the algorithm in such a way that it determines a stable pose of the cucumber after detecting it, based on a predefined threshold.

objdetection

🦾TASK#

  • Robot Movement: The robot starts at position 1, moves along the path, detects cucumbers, and harvests them.

  • Detection and Harvesting: After harvesting cucumbers at position 1, the robot moves to position 2, detects the next cucumber, and proceeds with harvesting. This pattern continues as the robot moves along the track.

  • Sequential Harvesting: The robot moves sequentially from positions 1 to 4, harvesting cucumbers at each point along the way.

  • Reversal of Process: Once the robot reaches the end of the track, at position 5, it reverses the process and moves back along the same path, harvesting cucumbers from position 6 to 3.

  • Return to Start: After completing the harvesting task, the robot returns to its starting position at 9.

The entire task can be executed using the Python script.

python harvest.py

🎁 Dataset#

Dataset can be found in the releases.

🏋️ Results#

Contents