Problem

Cosmo Logistic (CL) theme of eYRC 2023-24 competition:

The challenge in inter-planet logistics demands a sophisticated sorting system for packages within a space station warehouse. The task involves efficient navigation of a mobile robot, precise identification of packages on racks, and the coordinated manipulation of a robotic arm for accurate picking. The complexity arises from the need to autonomously manage these tasks in a simulated environment (Gazebo2) and later transition to a real-world setting in a warehouse at remote location.

Learn more about the theme over here.

Solution

To solve this problem, we designed a robotic arm and mobile robot with advanced capabilities. We integrated SLAM method into the system so that it could detect and localize packages placed on racks in a warehouse. We also developed software that allowed us to manipulate the robotic arm to pick up packages with ease.

We utilized various tools such as ROS 2, Gazebo, MoveIt 2, Computer Vision, Git, RViz 2, Nav 2. We ensured that all these tools worked together seamlessly by conducting extensive testing throughout the development process.

Impact

The successful implementation of the "Cosmo Logistic" theme has far-reaching implications for space logistics. The ability to autonomously sort and prepare packages for inter-planetary transportation streamlines the workflow, reduces the risk of human error, and enhances the overall efficiency of logistics operations in a space station. This project not only addresses the specific challenges of inter-planet logistics but also contributes to the broader field of robotics and automation.

Scored a perfect 100/100 score in the stage one the coding part of the eYRC 2023-24 competition.
Ranked 22 in the task2 and 15 in the task1.

Git Hub link for the project.

Task Learned

ROS 2: Teams will gain expertise in working with Robot Operating System 2, a crucial framework for developing robotic software.
Gazebo: Simulation in Gazebo allows for the testing and refinement of algorithms in a controlled virtual environment before implementation in the real-world setting.
MoveIt 2: Learning to use MoveIt 2 facilitates motion planning for the robotic arm, ensuring accurate and efficient manipulation.
Computer Vision: Utilizing computer vision techniques enables the detection and localization of packages on racks, a fundamental aspect of the sorting process.
Git: Version control with Git ensures collaborative and organized development, enabling teams to track changes and manage code effectively.
RViz 2: RViz 2 aids in visualizing the robot's sensor data and state, providing essential insights for debugging and optimization.
Nav 2: Navigating the mobile robot using Nav 2 involves advanced algorithms for path planning and obstacle avoidance, crucial for efficient warehouse logistics.

Robotic Arm and Mobile Robot Improve Package Sorting