Integrating Perception in Robotic Pick & Place Tasks

Introduction

Building on our previous work, this project enhances the basic Pick & Place operation by integrating perception capabilities. This development allows the robotic system to autonomously identify and locate objects to be manipulated, greatly improving task robustness and adaptability.

Objectives

• Implement a perception node that detects and locates objects using a depth sensor.
• Integrate perception data into the Pick & Place sequence, enabling dynamic object handling based on real-time visual input.

Tools and Technologies

• Programming Languages: C++
• Frameworks and Libraries: ROS 2, MoveIt2, Perception Pipeline
• Robotics Hardware: UR3e Robotic Arm, RGB-D Camera
• Simulation Tools: Gazebo, RViz
• Version Control: Git

Source Code

• GitHub Manipulation Project Repository

Process and Development

This project was divided into key development stages, focusing on the integration of perception technology with existing robotic control frameworks.

Perception Node Development

Setup: Configured and tested the perception node to ensure accurate detection and localization of objects using the robot’s mounted RGB-D camera.

Integration: Linked the perception node outputs directly to the MoveIt2 controlled Pick & Place task, allowing the robotic arm to adapt its operations based on real-time data.

Real and Simulated Environment Testing

Simulation Testing: Validated the integrated system in a simulated environment to ensure accurate object detection and manipulation without any physical risks.

Real Robot Testing: After successful simulation tests, the system was implemented on the actual UR3e robotic arm to handle real objects, fine-tuning the perception algorithms and robotic movements for optimal performance.

Results

The integration of perception technology allowed for a more dynamic and flexible robotic system. The UR3e arm successfully performed the Pick & Place tasks by dynamically locating and interacting with objects, demonstrating significant advancements over traditional pre-programmed robotic systems.

Key Insights

• The project highlighted the importance of perception in robotic systems for increasing operational versatility and reliability.
• Further developments could include enhancing the system’s ability to handle objects of varying shapes and sizes and improving its responsiveness to changes in the environment.