Robot Pick-and-Place Simulation

Overview

This project simulates a robotic pick-and-place operation using two different control methods: Joint Space Control and Resolved Rate Motion Control (RRMC). The simulation involves planning and executing trajectories for a robotic arm to pick up objects and place them in designated locations, with dynamic tracking and visualization.

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Features

• Control Methods:
  • Joint Space Control: Smooth trajectory planning in joint space using cubic splines.
  • RRMC: Cartesian velocity control with resolved rate motion control.
• Dynamic Object Tracking: Real-time adjustments to track moving objects.
• Trajectory Planning: Automated planning of pick-and-place trajectories.
• Visualization: End-effector paths, joint angles, and workspace overview.
• Simulation Environment: Includes object creation, motion, and gravity simulation.

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Project Structure

robot/
├── main.py                  # Entry point for the simulation
├── README.md                # Project documentation
├── src/                     # Source code directory
│   ├── joint_space_controller.py  # Joint space trajectory control
│   ├── joint_space_executor.py    # Joint space execution logic
│   ├── rrmc_controller.py         # RRMC control logic
│   ├── object_manager.py          # Object creation and management
│   ├── pick_and_place_executor.py # RRMC pick-and-place execution
│   ├── recorder.py                # Data recording for visualization
│   ├── robot_environment.py       # Robot simulation environment
│   ├── trajectory_planner.py      # Trajectory planning logic
│   ├── visualizer.py              # Visualization utilities
│   └── __init__.py                # Package initialization
└── cubes_plot/             # Visualization outputs

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Installation

1. Clone the repository:

   git clone https://github.com/ManuCa93/robots.git
   cd robots

2. Install dependencies:

   pip install -r requirements.txt

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Usage

1. Run the simulation:

   python main.py

2. Choose the control method by modifying the CONTROL_METHOD variable in main.py:
   • "joint_space" for Joint Space Control
   • "rrmc" for Resolved Rate Motion Control

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Key Components

1. Joint Space Control

• Smooth trajectory planning using cubic splines.
• Direct control of joint angles.
• Suitable for tasks requiring smooth and predictable motion.

2. Resolved Rate Motion Control (RRMC)

• Cartesian velocity control with damping to handle singularities.
• Real-time adjustments for dynamic environments.
• Suitable for precise Cartesian control.

3. Visualization

• End-effector paths and joint angles over time.
• Workspace overview showing object positions and trajectories.

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Examples

Joint Space Control

RRMC Control

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Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Commit your changes:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

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Acknowledgments

• Libraries Used:
  • numpy: Numerical computations
  • scipy: Interpolation and optimization
  • spatialmath: Robotics transformations
  • roboticstoolbox: Environment
• Inspiration: Robotics control and simulation techniques.