This project demonstrates the potential of autonomous mobile manipulators by simulating a UR5e robotic arm mounted on an MiR250 mobile base. The system integrates manipulation and mobility to perform tasks requiring perception, alignment, and navigation in dynamic environments. A central theme of the project is color-based object detection and interaction, where the robot leverages enhanced vision to aid human decision-making in scenarios where color differentiation is critical. The simulation highlights progressive capabilities, including color identification, color tracking, and environment-based color search.

Goals

Demonstrate the collaborative potential of autonomous mobile manipulators in simulation.
Leverage Gazebo for physics-based simulation and ROS2 for inter-node communication.
Implement Python-based ROS2 nodes for mobility, perception, and manipulation tasks.
Develop coordinated behaviors combining arm control, navigation, and computer vision.
Explore path planning and mapping techniques for object search in unknown environments.

Features

Color Identification: Robot rotates until detecting a red object, aligns with its centroid, and approaches it.
Color Tracking: Robot orbits a red object at a fixed radius, coordinating its mobile base with the UR5e arm.
Color Search: Robot navigates through an unfamiliar map, scanning predefined coordinates until a red object is found.

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Specific Contributions

Contributed to the development of all three simulation tasks (identification, tracking, and search).
Wrote Python control scripts to implement perception, navigation, and manipulation behaviors.
Created a high-level systems diagram representing the architecture of the mobile manipulator.
Integrated computer vision algorithms with Gazebo-based simulation to detect and align with objects.
Supported the coordination of arm and base motion during task execution.

Cam 'n SLAM

Goals

Features

Specific Contributions

Final Project Video