Mechatronics @ UPenn

"Waldo" - Robotic 3D printed sensor actuated project

"Final Project - Throckinator" Autonomous Self driving ground robot

The Rise and Fall of Throckinator
Team 11 Final Project Report – MEAM 5100, University of Pennsylvania

For our MEAM5100 final project, our team designed and developed a competitive robotic system named Throckinator, inspired by the mechanics of autonomous systems and optimized for the course’s challenge-based competition.

Key Features:

1. Mechanical Design:

• Platform: Adapted a two-wheel-drive system with a caster wheel for enhanced maneuverability and stability.

• Motors: Upgraded to 12V, 100 RPM DC motors with magnetic encoders for precision and torque, overcoming the limitations of high-RPM motors from earlier iterations.

• Custom Bumper: Designed a robust front bumper for pushing, defending, and button-pressing tasks, with a personalized “The Rock” design.

• Sensor Mounts: Strategically positioned Time-of-Flight (ToF) and sonar sensors for wall following and autonomous navigation.

2. Electrical Design:

• Control Board: Developed a modular proto-board with Molex connectors to simplify wiring and ensure reliable connectivity.

• Power Systems: Utilized separate power sources: a 5V LiPO power bank for the ESP32 microcontroller and sensors, and an 11.1V LiPO battery for motors and peripherals.

• Noise Reduction: Mitigated ground noise from motors, ensuring cleaner sensor readings and improved system reliability.

3. Software Integration:

• Microcontroller: Chose the ESP32-S2 Saola microcontroller for its GPIO capabilities, PWM support, and Wi-Fi functionality.

• Control Interface: Designed a web-based control panel to manage robot modes (manual, idle, wall-following, and autonomous attack).

• Functionality: Integrated PID control for precise motor actuation and developed algorithms for wall-following and autonomous attack strategies.

• Collaboration Tools: Implemented a scheduler, structured file system, and a shared Google Drive for effective teamwork.

4. Competition Strategy:

• Focused on reliable wall-following to navigate the arena and perform attack modes effectively.

• Combined ToF sensor data and Vive position tracking for precise autonomous actions, successfully achieving target engagement.

Challenges and Solutions:

• I2C Communication: Addressed conflicts with multiple devices by assigning unique addresses and re-wiring connections.

• Integration: Overcame issues with noise, power management, and code stability through iterative testing and troubleshooting.

Retrospective:

This project was a collaborative effort with team members Ethan Sanchez and Mateusz Jaszczuk. Together, we applied mechatronics principles to create a competitive robot, enhancing our skills in system design, integration, and problem-solving. Special thanks to my teammates for their contributions to mechanical, electrical, and software subsystems, making this challenging project a rewarding experience.

Video: Autonomous ground robot navigating through the course using Sonar & Time of flight sensors

Left: Control, state and code flow diagram Right: Wiring diagram of robot

Robotic Pick-and-Place System – MEAM 5200 Final Project 

Course: MEAM 5200: Introduction to Robotics – University of Pennsylvania
Team: Andrik Puentes, Benjamin Aziel, Solomon Gonzalez, Mateusz Jaszczuk
Instructor: Prof. Cynthia Sung
Fall 2024

As part of UPenn’s MEAM 5200 course, our team designed and implemented a robotic system capable of autonomously picking and stacking blocks in a competitive setting using the Franka Emika Panda robot and a vision-based perception pipeline. The project required handling both static and dynamically moving objects with precision, speed, and robustness in both simulation (Gazebo) and hardware environments.

Technical Highlights & Responsibilities

• Inverse & Forward Kinematics:
  Developed a full-stack motion planning pipeline using custom FK and IK solvers with null space optimization to center joints while achieving target end-effector poses. Implemented real-time joint velocity control via the pseudoinverse of the Jacobian matrix for smooth, safe motion.

• Vision-Based Perception with AprilTags:
  Designed an end-effector mounted vision system to detect AprilTags on blocks. Transformed detected poses from the camera frame to the robot’s global coordinate system, enabling accurate grasping and manipulation.

• Static Block Manipulation:
  Created a modular routine to detect, approach, and stack static blocks using preconfigured joint positions. Accounted for sensor offsets, gripper dynamics, and detection inconsistencies between simulation and hardware.

• Dynamic Block Manipulation & Prediction:
  Implemented real-time trajectory prediction for moving blocks on a rotating turntable. Accounted for angular displacement and environmental delays to predict future poses and time the robot’s grasping motion.

• Stacking Algorithm:
  Designed a logic-based system to calculate stack heights and place blocks with consistent alignment. Dynamically adjusted stacking heights based on the number of blocks placed and ensured collision-free placements.

• Hardware Integration & Tuning:
  Tuned joint speeds, camera calibration, gripper force, and sensor tolerances. Addressed real-world challenges such as camera view angle, transformation matrix discrepancies, and turntable misalignments.
  
  

Results & Outcome

• Advanced to Quarterfinals in the final competition against other teams.

• Achieved 100% success rate in static block stacking during simulation and most hardware runs.

• Reached 57% success rate in dynamic block stacking on hardware, with promising results that improved with additional calibration.

• Developed a modular, team-agnostic codebase adaptable to different robot stations (blue/red) and capable of real-time adjustments.
  
  

Key Lessons Learned

• Bridging the gap between simulation and hardware requires significant calibration, especially for camera transforms and object detection.

• Real-time robotics systems demand robust timing control, hardware awareness, and fail-safe logic for edge cases like failed grasps or block misalignment.

• Modular design and separation of perception and control pipelines enable quick iteration and debugging.