Project · Robotics
Robotic Automation for Laboratory Test Tube Handling
Anand Diagnostic Center processes over 2,000 test tube samples daily, but their existing conveyor-based segregation system required significant manual intervention for barcode verification and racking. Our team designed a 4-DOF robotic arm solution to automate the pick-scan-place workflow, eliminating manual labor and reducing errors.
As Design Domain Lead, I owned the complete mechanical design—from the initial site visit to capture floor space constraints (1000×500mm) and payload requirements, through CAD modeling of custom worm and cycloidal gearboxes, to 3D printing the final components in heat-resistant PETG. I also managed component sourcing and kept the project under the ₹1,25,000 budget.
The resulting robot weighs just 7.25 kg (well under the 13 kg portability target), handles 1 kg payloads, and features a modular two-claw gripper with an integrated Raspberry Pi camera for test tube detection. I proposed an overhead camera safety system using contour detection to halt the robot if operators enter the workspace. This was my first industry internship and a formative experience in cross-functional product development.
Problem Statement
- Need: Anand Diagnostic Center processes 2,000+ test tube samples daily requiring automated segregation and racking
- Prior art: Existing conveyor-based system required manual barcode verification, no automated racking, and error-prone batch handling
- Constraints: 1000×500mm floor space, <13kg total weight for portability, <₹1,25,000 budget, and 250g payload (with 1.67× safety factor)
- Open question: Could a compact, affordable robotic arm automate the pick-scan-place workflow while maintaining safety for nearby workers?
Methodology
The project followed a comprehensive requirements-design-fabricate-test workflow for a 4-DOF robotic arm system. Click a stage to jump there.
Design & Simulation
Requirements & Site Analysis: Conducted on-site visit to Anand Diagnostic Center to understand the complete sample workflow—from registration through department segregation to archiving. Captured critical constraints: 1000×500mm floor space, <13kg total weight for portability, <₹1,25,000 budget, and 250g payload (with 1.67× safety factor). Identified key pain points in the existing conveyor system: manual barcode verification, no automated racking, and error-prone batch handling.
Actuator Design & Selection: Selected NEMA 17/23 stepper motors over servos for cost-effectiveness and ease of adding magnetic encoder feedback. Designed custom worm gearboxes (3:1 and 30:1 ratios) for their non-backdrivable characteristic—critical for holding payloads when powered off. Implemented cycloidal drive (8:1) for wrist rotation. All actuator housings were designed for 3D printing in PETG to handle motor temperatures up to 70°C.
Joint Torque Calculations: Performed torque calculations for all four joints considering link masses, payload, and fully extended arm configuration. Base joint required highest torque (233.25 kgcm), achieved using NEMA 23 with 30:1 worm reduction. Validated that selected motor-gearbox combinations provided adequate torque margins across the workspace.
End Effector Design: Iterated through two gripper designs. Initial encompassing gripper was modular but required separate camera mount. Final two-claw gripper used spur gears and four-bar linkage to achieve >3.5N gripping force (~0.5Nm torque). Integrated slots for force sensing strips as alternative to unavailable force-torque sensors.
Vision System Integration: Selected Raspberry Pi HQ Camera (Sony IMX477R, 12.3MP) for wrist-mounted test tube detection and cap orientation identification. Designed modular camera adapter with dowel pins for precise, repeatable mounting. Positioned camera above end effector to maximize field of view without gripper obstruction.
Safety System Architecture: Proposed overhead camera-based safety system using contour and line detection. When a human hand or body enters the robot's workspace, the system triggers an automatic stop. This architecture allows the robot to operate near human workers while maintaining safety compliance.
Sensing Force sensing integration
Integrated slots for force sensing strips in the gripper design as a cost-effective alternative to unavailable force-torque sensors, enabling adaptive gripping force control.
Fabrication
3D Printing & Assembly: 3D printed all gearbox housings, gripper components, and camera mounts in PETG. Selected PETG over PLA for thermal stability and mechanical strength. Assembled complete robot including motor wiring, sensor integration, and Raspberry Pi mounting. Final weight: 7.25kg (excluding payload).
Test Setup
Prototype was 3D printed and assembled. Actuators were tested for performance under load. Static and dynamic stability validated through ANSYS FEA. Contour detection algorithms demonstrated successful hand and test tube identification.
Tools Software and equipment used
- SolidWorks — Complete CAD modeling
- ANSYS — Static and dynamic FEA validation
- Raspberry Pi — Embedded control and camera interface
- Python — Vision algorithms and system integration
- 3D Printer — PETG component fabrication
Results
Key Finding Project achievements
- Achieved 7.25 kg total robot weight – 44% under the 13 kg portability constraint
- Designed for 1 kg payload capacity with appropriate torque margins at all joints
- Completed design and prototype within ₹1,25,000 budget
- Successfully integrated contour detection for test tube identification and workspace safety
- Non-backdrivable worm gear design ensures payload security during power-off
- Modular design allows replication for archiving stations in other departments
Data Motor-Gearbox Configuration by Joint
Worm gearboxes selected for non-backdrivability – arm holds position when powered off
| Joint | Motor | Gearbox | Reduction | Required Torque (kgcm) |
|---|---|---|---|---|
| Wrist (T1) | NEMA 17 (4.2 kgcm) | Cycloidal | 8:1 | 12.5 |
| Link 2 (T2) | NEMA 17 (4.2 kgcm) | Worm | 3:1 | 74 |
| Link 1 (T3) | NEMA 23 (10.1 kgcm) | Worm | 30:1 | 233.25 |
| Base (T4) | NEMA 23 (18.9 kgcm) | Worm | 30:1 | — |
Future Future work
- Integrate magnetic encoders on stepper motors for closed-loop position feedback
- Add force-torque sensor at wrist for adaptive gripping and collision detection
- Develop path planning algorithms for optimal pick-scan-place trajectories
- Implement ROS-based control architecture for easier scaling and maintenance
- Deploy and validate with live test tube samples at Anand Diagnostic Center
Additional Information
Complete internship report with methodology, calculations, CAD figures, and results.
Skills Technical skills demonstrated
- CAD: SolidWorks — complete mechanical design and assembly
- Simulation: ANSYS FEA — static and dynamic structural validation
- Fabrication: 3D Printing (PETG) — actuator housings and gripper components
- Design: Gear design, torque calculations, robotic arm kinematics
- Embedded: Raspberry Pi, Python, computer vision integration
- Management: Requirements engineering, budget management, cross-domain coordination
Team Contributions & credits
Led the Design Domain for a 4-person team, owning the complete mechanical design lifecycle from requirements gathering through fabrication. Responsible for CAD modeling, FEA validation, motor/gearbox selection, 3D printing, component sourcing, budget management, and cross-domain coordination. Also proposed the safety system architecture and contributed to embedded systems integration.
Guided by Dr. D. Sethuram (Primary Guide, PES University), Prof. Venkatrangan (Technical Guidance), and Dr. Sujay Prasad (Industry Mentor, Anand Diagnostic Center).
Project sponsored by Anand Diagnostic Center; fabricated at PES University Department of Mechanical Engineering.