Project

# Title Team Members TA Documents Sponsor
34 A Vision-Integrated Robot for Autonomous Book Classification in Library Environments
 Xinrui Xiong
Zehao Bao
Zhecheng Lou
Zhenxiong Tang
 design_document1.pdf
final_paper1.docx
final_paper2.pdf
other1.pdf
other2.pdf
other3.pdf
other4.pdf
video1.mp4
 Timothy Lee
Problem
In library operations, staff must identify returned books, look up shelf locations, and reshelve by hand—a labour-intensive process that does not scale and is harder when collections include multiple languages. We focus on a fixed workstation (no mobile base): a robotic arm picks books from a collection bin and places them on the shelf. The real challenge is not “place at (x,y,z)”, but how the robot visually understands the current bookshelf state and decides where and how to place each book—perceiving gaps, widths, and fit, then choosing stable, tidy placements. This “shelf perception and placement decision” remains active in warehouse robotics. We concentrate innovation on this software side and simplify hardware to a fixed arm, aiming for clear, publishable contributions aligned with “software tuning”.

Solution Overview
Fixed workstation: collection bin, arm with gripper, and shelves within reach. Readers place books in a vertical bin with fixed spine (barcode) orientation. The system runs in batches (scan barcodes, look up positions, plan return order by greedy or similar). Innovation in three areas: (1) Real-time shelf occupancy perception—camera scans each layer, image analysis finds gaps and widths and whether the current book fits. (2) Intelligent slot selection—policy-based choice (prefer larger gaps, avoid squeezing and isolated positions). (3) Visual-servo placement—camera gives real-time feedback during insertion instead of open-loop trajectories. One-button operation; runs on a single-board computer such as Raspberry Pi. Demonstrator uses a scaled-down environment and lightweight mock books.

Solution Components
Perception Subsystem (Subsystem I)
-Barcode/QR scanner or camera for book identity; optional depth camera for pose. Barcode decoding library and catalogue interface for shelf (level, slot).
-Shelf occupancy (innovation): Camera scans a layer; image analysis returns gap locations, widths, and fit for the current book. Output to slot selection.
-Output: book identity and target range; per-layer gap/occupancy for placement.

Task Planning and Scheduling Subsystem (Subsystem II)
-Batch order by simple greedy (stack order or shelf proximity). Intelligent slot selection (innovation): Within correct range, select slot by policy—larger gaps, next to similar books, avoid isolated positions. Designed for algorithm design and quantitative evaluation.
-Output: ordered task list (book → shelf, level, slot) from shelf perception and policy.

Manipulation Subsystem (Subsystem III)
-Arm (open-source 6-DOF) with smart servos (serial bus), gripper, and on-board camera. Visual-servo placement (innovation): Real-time camera feedback adjusts pose/trajectory during insertion (closed-loop “software tuning”). Kinematics and trajectory planning; camera confirms stability after placement.
-Output: book in chosen slot, stable (upright or leaning), vision-verified.

Criterion for Success
1. One-button operation: load bin, press start; system runs scan, planning, placement, then ready for next batch.
2. Barcode accuracy ≥ 95%; full flow from scan to reshelve without human intervention.
3. Success = book in correct range, upright or leaning; placement success rate ≥ 90%.
4. The three innovations in use: visual shelf occupancy for placement; intelligent slot policy (observable or evaluable); visual feedback during placement (not only open-loop).
5. No serious hardware collisions; shelf left tidier where feasible.
Demo success: Load a batch (five books), press once; system completes identification, planning, and placement using shelf perception, intelligent slot choice, and visual-servo placement; every book in correct range and stable, no serious collisions.

Low Cost Myoelectric Prosthetic Hand

Featured Project

According to the WHO, 80% of amputees are in developing nations, and less than 3% of that 80% have access to rehabilitative care. In a study by Heidi Witteveen, “the lack of sensory feedback was indicated as one of the major factors of prosthesis abandonment.” A low cost myoelectric prosthetic hand interfaced with a sensory substitution system returns functionality, increases the availability to amputees, and provides users with sensory feedback.

We will work with Aadeel Akhtar to develop a new iteration of his open source, low cost, myoelectric prosthetic hand. The current revision uses eight EMG channels, with sensors placed on the residual limb. A microcontroller communicates with an ADC, runs a classifier to determine the user’s type of grip, and controls motors in the hand achieving desired grips at predetermined velocities.

As requested by Aadeel, the socket and hand will operate independently using separate microcontrollers and interface with each other, providing modularity and customizability. The microcontroller in the socket will interface with the ADC and run the grip classifier, which will be expanded so finger velocities correspond to the amplitude of the user’s muscle activity. The hand microcontroller controls the motors and receives grip and velocity commands. Contact reflexes will be added via pressure sensors in fingertips, adjusting grip strength and velocity. The hand microcontroller will interface with existing sensory substitution systems using the pressure sensors. A PCB with a custom motor controller will fit inside the palm of the hand, and interface with the hand microcontroller.