Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 50 | Automatic Sorting Robotic Arm for Table Tennis Balls |
Siqi Pan |
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| # Team Members - Junway Lin (3220300390) - Siqi Pan (3220111242) - Xucheng Wu (3220111011) - Zhonghao Wang (3220111411) # Problem Table tennis balls exhibit varying sizes, weight, color, material composition, and surface markings due to evolving manufacturing standards and regulations. The current ITTF ruling specifies that a standard ball legal for play must meet the following requirements: - 40 mm in diameter - 2.7 g in weight - ABS plastic construction - White or orange matte appearance Prior to the regulation changes in October 2000, table tennis balls legal for play were: - 38 mm in diameter - 2.5 g in weight - Made of celluloid, a highly flammable and lightweight synthetic plastic These changes were implemented to slow down the game and improve visibility for spectators and television audiences. Notably, some differences among table tennis balls are visually difficult to distinguish, which complicates manual sorting. In training, storage, and equipment management contexts, these variations can lead to inefficiencies and classification errors when sorting is performed manually. # Solution Overview This project addresses the problem by designing and developing an automated robotic system capable of accurately identifying and sorting table tennis balls based on their physical and visual characteristics. The system will measure key attributes of each ball, including: - Diameter - Weight - Color - Logo presence These measurements are processed by an embedded controller to classify each ball and actuate a robotic arm to place it into the appropriate category. The system architecture combines: - Mechanical design - Multimodal sensing - Embedded processing - Controlled actuation Compared to manual sorting, the proposed solution improves accuracy, consistency, and efficiency while reducing human effort. In contrast to vision-only systems, the use of multiple sensing modalities enables more robust and reliable classification, particularly in cases where visual ambiguity or environmental variability may degrade performance. # Solution Components ## Input Box (Passive Storage) - **Geometry:** Sloped walls or funnel shape to keep balls clustered and accessible - **Surface:** Low-friction lining to prevent jamming - **Availability Detection (for robustness):** - Simple IR break-beam or ToF sensor aimed at the pile to corroborate “empty” detection from vision ## Sensing Platform (Measurement + Fixturing) - **Mechanical stabilization:** - Concave cup or V-groove with known geometry that allows for ease of ball placement and pickup and ensures the ball is fixed in place - **Diameter measurement:** - ToF sensor (e.g., VL53L0X) with fixed reference geometry - **Presence detection:** - IR break-beam or reflective IR sensor to trigger measurement - **Design note:** - Platform height and geometry should be consistent with the arm’s pickup kinematics ## Robotic Arm with Integrated Vision (Feeding + Inspection + Sorting) - **Functions combined:** 1. Pick ball from input box 2. Place ball on sensing platform 3. Capture image for color/logo 4. Pick ball from platform 5. Place into correct output bin - **Actuation:** - 3-4 DOF servo-based arm (PWM-controlled servos) - Actual model of arm undetermined - **End effector (critical choice):** - **Suction gripper:** - Small vacuum pump + nozzle - High reliability for smooth, lightweight balls - Might be unreliable if the balls are not clean - **Alternative:** - Compliant finger gripper with rubber padding - **Vision module:** - Camera (e.g., Raspberry Pi Camera) mounted near the end effector - **Vision tasks:** - Color classification (RGB thresholding) - Logo detection (contrast/contour-based) - **Key implications of using arm for feeding:** - Requires pose consistency of balls in input box - May need simple bin shaping - Increases cycle time, since feeding is no longer parallelized - Eliminates the need for feeder actuators, reducing mechanical complexity ## Output Boxes (Sorting Bins) - Fixed bin positions mapped to arm coordinates - Sized to tolerate placement error ## Custom PCB Controller (Centralized Control + Interfaces) ### Core Architecture - **Microcontroller (primary):** - STM32 (e.g., STM32F4 series) for real-time control and integration - **Vision processing (recommended split architecture):** - Raspberry Pi handles image processing - Communicates with PCB via UART/SPI ### PCB-Integrated Subsystems - **Sensor interfaces:** - HX711 circuit for load cell (can be integrated or provided as module footprint) - I2C buses for ToF sensors - GPIO for IR sensors - **Motor control:** - PWM outputs for servo control (arm + optional actuators) - Power drivers and proper current routing for motors - **Power management:** - Separate regulated rails: - 5-6 V high-current rail for servos - 3.3 V for logic and sensors - Decoupling and filtering - **Communication:** - UART/SPI link to vision processor - **Protection and layout considerations:** - Ground plane separation (analog vs. digital) - Noise isolation for weight measurement accuracy # Criterion for Success ## Ball Handling and Manipulation - The robotic arm shall successfully pick up a table tennis ball from the input box and place it on the sensing platform with a success rate of **≥ 90%** over **20 consecutive trials**. - The arm shall transfer a classified ball from the sensing platform to the correct output bin with a placement accuracy of **±2 cm** from the target bin center in at least **90% of trials**. ## Sensing Accuracy - The sensing platform shall measure ball weight with an error of **≤ ±0.02 g** compared to a calibrated reference scale. - The diameter estimation system shall classify balls as **38 mm vs 40 mm** with **≥ 95% accuracy** under controlled positioning conditions. - The vision system shall correctly classify ball color (**white vs orange**) with **≥ 95% accuracy** under consistent lighting conditions. - Logo presence detection shall achieve **≥ 85% accuracy**, acknowledging higher variability. ## System Reliability - The full system shall successfully complete at least **30 consecutive sorting cycles** with no more than **2 total classification or actuation failures**. - The system shall recover from a failed grasp or missed detection without manual reset in **≥ 80% of failure cases**. |
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