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 |
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. |
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