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of the handheld scanner shell, housing the depth camera aperture, spectral array bezel, and UI display window.", "dimensions": "110x85x45mm", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, supports required", "quantity": 1, "estimatedCost": 4.5, "purchaseUrl": "https://www.amazon.com/s?k=PETG+filament+1.75mm" }, { "id": "handheld_enclosure_back", "name": "Handheld Enclosure Back Cover", "category": "mechanical", "type": "3d_printed", "description": "Rear half of the handheld scanner shell with an ergonomic handle grip, ventilation slots, and USB-PD charging port cutout.", "dimensions": "110x85x65mm", "material": "PETG", "printSettings": "35% infill, 0.2mm layer, supports required", "quantity": 1, "estimatedCost": 5.5, "purchaseUrl": "https://www.amazon.com/s?k=PETG+filament+1.75mm" }, { "id": "carrier_board_mount", "name": "Carrier Board Mounting Frame", "category": "mechanical", "type": "3d_printed", "description": "Internal chassis bracket that secures the custom carrier board and provides strain relief for internal cabling.", "dimensions": "95x75x8mm", "material": "PLA", "printSettings": "20% infill, 0.2mm layer, no supports", "mountsFor": "baseboard_hub", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "https://www.amazon.com/s?k=PLA+filament+1.75mm" }, { "id": "depth_camera_mount", "name": "Depth Camera Secure Bracket", "category": "mechanical", "type": "3d_printed", "description": "Custom bracket designed to securely lock the Intel RealSense depth camera module behind the front bezel optical window.", "dimensions": "90x25x25mm", "material": "PLA", "printSettings": "20% infill, 0.2mm layer", "mountsFor": "depth_sensor_cam", "quantity": 1, "estimatedCost": 1.5, "purchaseUrl": "https://www.amazon.com/s?k=PLA+filament+1.75mm" }, { "id": "battery_holder_bracket", "name": "18650 Battery Pack Clip", "category": "mechanical", "type": "3d_printed", "description": "Custom modular clamp that isolates and secures the internal single-cell Li-ion battery pack inside the handle grip.", "dimensions": "75x25x22mm", "material": "PETG", "printSettings": "40% infill, 0.2mm layer", "mountsFor": "li_ion_cell", "quantity": 1, "estimatedCost": 1.2, "purchaseUrl": "https://www.amazon.com/s?k=PETG+filament+1.75mm" }, { "id": "oled_display_bezel", "name": "OLED Display Mount Bezel", "category": "mechanical", "type": "3d_printed", "description": "Bezel frame to mount the user interface OLED display flush against the upper back slope of the scanner shell.", "dimensions": "45x35x6mm", "material": "PLA", "printSettings": "20% infill, 0.15mm layer", "mountsFor": "status_display", "quantity": 1, "estimatedCost": 0.8, "purchaseUrl": "https://www.amazon.com/s?k=PLA+filament+1.75mm" }, { "id": "brass_threaded_inserts", "name": "M3 Threaded Heat-Set Inserts", "productName": "RUIZHI M3 Brass Threaded Heat-Set Inserts", "category": "mechanical", "type": "misc", "description": "M3 brass threaded inserts melted into the 3D-printed enclosure halves to allow repeated disassembly without stripping threads.", "dimensions": "M3x4.0x5.0mm", "material": "Brass", "quantity": 12, "estimatedCost": 0.08, "purchaseUrl": "https://www.amazon.com/s?k=M3+brass+threaded+inserts+4mm", "partId": "1878ca3e-770a-44a3-9e50-d876d0bc24aa", "ebayUrl": "https://www.ebay.com/sch/i.html?_nkw=RUIZHI+M3+Brass+Threaded+Heat-Set+Inserts&_sacat=0&_from=R40&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339153059&customid=blueprint&toolid=10001&mkevt=1" }, { "id": "enclosure_m3_screws", "name": "Enclosure Assembly Screws", "productName": "Sutemribor M3 Hex Button Head Cap Screws", "category": "mechanical", "type": "misc", "description": "Black oxide alloy steel hex button head screws for fastening the front and back enclosure halves together.", "dimensions": "M3x12mm", "material": "Alloy Steel", "quantity": 6, "estimatedCost": 0.05, "purchaseUrl": "https://www.amazon.com/s?k=M3x12mm+button+head+screws", "partId": "653dbdbe-c9bc-499d-9d3e-20b1c9195a2b", "ebayUrl": "https://www.ebay.com/sch/i.html?_nkw=Sutemribor+M3+Hex+Button+Head+Cap+Screws&_sacat=0&_from=R40&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339153059&customid=blueprint&toolid=10001&mkevt=1" }, { "id": "pcb_m3_screws", "name": "PCB Mounting Screws", "productName": "Sutemribor M3 Hex Button Head Cap Screws", "category": "mechanical", "type": "misc", "description": "Button head hex screws used to secure the carrier board to the internal printed bracket frames.", "dimensions": "M3x6mm", "material": "Alloy Steel", "quantity": 8, "estimatedCost": 0.04, "purchaseUrl": "https://www.amazon.com/s?k=M3x6mm+button+head+screws", "partId": "653dbdbe-c9bc-499d-9d3e-20b1c9195a2b", "ebayUrl": "https://www.ebay.com/sch/i.html?_nkw=Sutemribor+M3+Hex+Button+Head+Cap+Screws&_sacat=0&_from=R40&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339153059&customid=blueprint&toolid=10001&mkevt=1" }, { "id": "teflon_diffuse_target", "name": "Spectral Calibration Reference Target", "productName": "Labsphere Spectralon Equivalent PTFE Disc", "category": "mechanical", "type": "structural", "description": "High-purity diffuse white PTFE calibration target used for field normalization of the 18-channel spectral array.", "dimensions": "30x30x6mm", "material": "PTFE (Teflon)", "quantity": 1, "estimatedCost": 12.5, "purchaseUrl": "https://www.amazon.com/s?k=diffuse+PTFE+calibration+target+disc", "partId": "99c99822-cada-4b6d-98ac-c33655bb474b", "ebayUrl": "https://www.ebay.com/sch/i.html?_nkw=Labsphere+Spectralon+Equivalent+PTFE+Disc&_sacat=0&_from=R40&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339153059&customid=blueprint&toolid=10001&mkevt=1" }, { "id": "main_mcu_mount", "name": "Main System Controller Mount", "category": "mechanical", "type": "3d_printed", "mountsFor": "main_mcu", "description": "Standoff bracket sized to the Main System Controller's mounting hole pattern. M2.5 holes for board fasteners; vented underside for airflow.", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, 4 perimeters", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "", "dimensions": "60x40x12mm" }, { "id": "spectral_sensor_triad_mount", "name": "18-Channel Spectral Triad Mount", "category": "mechanical", "type": "3d_printed", "mountsFor": "spectral_sensor_triad", "description": "Form-fitting cradle for the 18-Channel Spectral Triad that holds the sensing surface in its rated orientation. Snap-fit retention so it can be removed for calibration without tools.", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, 4 perimeters", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "", "dimensions": "30x30x15mm" }, { "id": "dac_led_driver_mount", "name": "I2C LED Driver DAC Mount", "category": "mechanical", "type": "3d_printed", "mountsFor": "dac_led_driver", "description": "Per-position bracket for the I2C LED Driver DAC with M3 mounting holes matched to its bolt pattern. Rated for the actuator's continuous torque/load class.", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, 4 perimeters", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "", "dimensions": "50x50x20mm" }, { "id": "vnir_led_array_mount", "name": "VNIR Multi-Wavelength LED Array Mount", "category": "mechanical", "type": "3d_printed", "mountsFor": "vnir_led_array", "description": "Per-position bracket for the VNIR Multi-Wavelength LED Array with M3 mounting holes matched to its bolt pattern. Rated for the actuator's continuous torque/load class.", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, 4 perimeters", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "", "dimensions": "50x50x20mm" }, { "id": "environmental_sensor_mount", "name": "Ambient Air & VOC Sensor Mount", "category": "mechanical", "type": "3d_printed", "mountsFor": "environmental_sensor", "description": "Form-fitting cradle for the Ambient Air & VOC Sensor that holds the sensing surface in its rated orientation. Snap-fit retention so it can be removed for calibration without tools.", "material": "PETG", "printSettings": "30% infill, 0.2mm layer, 4 perimeters", "quantity": 1, "estimatedCost": 2, "purchaseUrl": "", "dimensions": "30x30x15mm" } ], "electricalConnections": [ { "source": "main_mcu", "target": "baseboard_hub", "type": "data", "protocol": "i2c", "sourcePin": "GPIO2_SDA", "targetPin": "I2C1_SDA", "label": "Main I2C Data" }, { "source": "main_mcu", "target": "baseboard_hub", "type": "data", "protocol": "i2c", "sourcePin": "GPIO3_SCL", "targetPin": "I2C1_SCL", "label": "Main I2C Clock" }, { "source": "baseboard_hub", "target": "battery_charger_bms", "type": "data", "protocol": "i2c", "sourcePin": "I2C1_SDA", "targetPin": "SDA" }, { "source": "baseboard_hub", "target": "battery_charger_bms", "type": "data", "protocol": "i2c", "sourcePin": "I2C1_SCL", "targetPin": "SCL" }, { "source": "baseboard_hub", "target": "power_delivery_sink", "type": "data", "protocol": "i2c", "sourcePin": "I2C1_SDA", "targetPin": "SDA" }, { "source": "baseboard_hub", "target": "power_delivery_sink", "type": "data", "protocol": "i2c", "sourcePin": "I2C1_SCL", "targetPin": "SCL" }, { "source": "main_mcu", "target": "depth_sensor_cam", "type": "data", "protocol": "usb", "sourcePin": "USB_OTG_DP", "targetPin": "USB3_D_P", "label": "Depth Cam Data P" }, { "source": "main_mcu", "target": "depth_sensor_cam", "type": "data", "protocol": "usb", "sourcePin": "USB_OTG_DM", "targetPin": "USB3_D_N", "label": "Depth Cam Data N" }, { "source": "baseboard_hub", "target": "power_delivery_sink", "type": "power", "voltage": "5V", "sourcePin": "+5V", "targetPin": "VBUS" }, { "source": "power_delivery_sink", "target": "battery_charger_bms", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "battery_charger_bms", "target": "baseboard_hub", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "main_mcu", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "depth_sensor_cam", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "spectral_sensor_triad", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "environmental_sensor", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "dac_led_driver", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "baseboard_hub", "target": "status_display", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "dac_led_driver", "target": "vnir_led_array", "type": "power", "sourcePin": "GND", "targetPin": "GND_THERMAL" }, { "source": "baseboard_hub", "target": "power_delivery_sink", "type": "power", "sourcePin": "GND", "targetPin": "GND" }, { "source": "power_delivery_sink", "target": "battery_charger_bms", "type": "power", "sourcePin": "VBUS", "targetPin": "VBUS", "voltage": "5V" }, { "source": "battery_charger_bms", "target": "li_ion_cell", "type": "power", "sourcePin": "BAT", "targetPin": "+", "voltage": "4.2V" }, { "source": "li_ion_cell", "target": "battery_charger_bms", "type": "power", "sourcePin": "+", "targetPin": "BAT", "voltage": "3.7V" }, { "source": "battery_charger_bms", "target": "baseboard_hub", "type": "power", "sourcePin": "SYS", "targetPin": "+5V", "voltage": "5V" }, { "source": "baseboard_hub", "target": "main_mcu", "type": "power", "sourcePin": "+5V", "targetPin": "+5V", "voltage": "5V" }, { "source": "baseboard_hub", "target": "depth_sensor_cam", "type": "power", "sourcePin": "+5V", "targetPin": "VBUS", "voltage": "5V" }, { "source": "baseboard_hub", "target": "spectral_sensor_triad", "type": "power", "sourcePin": "+5V", "targetPin": "VDD", "voltage": "3.3V" }, { "source": "baseboard_hub", "target": "environmental_sensor", "type": "power", "sourcePin": "+5V", "targetPin": "VDDIO", "voltage": "3.3V" }, { "source": "baseboard_hub", "target": "dac_led_driver", "type": "power", "sourcePin": "+5V", "targetPin": "VIN", "voltage": "5V" }, { "source": "baseboard_hub", "target": "status_display", "type": "power", "sourcePin": "+5V", "targetPin": "VIN", "voltage": "3.3V" }, { "source": "dac_led_driver", "target": "vnir_led_array", "type": "power", "sourcePin": "OUT1", "targetPin": "LED_A1", "voltage": "3.6V" }, { "source": "dac_led_driver", "target": "vnir_led_array", "type": "power", "sourcePin": "OUT2", "targetPin": "LED_A2", "voltage": "3.6V" } ], "mechanicalConnections": [ { "source": "handheld_enclosure_front", "target": "brass_threaded_inserts", "label": "heat-set press fit", "delta": { "x": 0, "y": 0, "z": 0 } }, { "source": "handheld_enclosure_back", "target": "handheld_enclosure_front", "label": "enclosure_m3_screws into brass_threaded_inserts", "delta": { "x": 0, "y": 0, "z": -55 } }, { "source": "carrier_board_mount", "target": "handheld_enclosure_front", "label": "pcb_m3_screws", "delta": { "x": 0, "y": 0, "z": 0 } }, { "source": "depth_camera_mount", "target": "handheld_enclosure_front", "label": "pcb_m3_screws", "delta": { "x": 0, "y": -35, "z": 0 } }, { "source": "battery_holder_bracket", "target": "handheld_enclosure_back", "label": "pcb_m3_screws", "delta": { "x": 0, "y": 0, "z": 0 } }, { "source": "oled_display_bezel", "target": "handheld_enclosure_front", "label": "snap-fit and glue", "delta": { "x": 0, "y": -25.5, "z": 25.5 } }, { "source": "main_mcu_mount", "target": "carrier_board_mount", "label": "pcb_m3_screws", "delta": { "x": 0, "y": -10, "z": 0 } }, { "source": "spectral_sensor_triad_mount", "target": "handheld_enclosure_front", "label": "pcb_m3_screws", "delta": { "x": 0, "y": 15, "z": -30 } }, { "source": "dac_led_driver_mount", "target": "carrier_board_mount", "label": "pcb_m3_screws", "delta": { "x": 0, "y": 14, "z": 0 } }, { "source": "vnir_led_array_mount", "target": "spectral_sensor_triad_mount", "label": "integrated snap-lock", "delta": { "x": 0, "y": 17.5, "z": 0 } }, { "source": "environmental_sensor_mount", "target": "handheld_enclosure_back", "label": "pcb_m3_screws", "delta": { "x": 0, "y": 25, "z": 0 } }, { "source": "teflon_diffuse_target", "target": "handheld_enclosure_front", "label": "slid-in protective slot", "delta": { "x": 0, "y": 19.5, "z": 0 } }, { "source": "carrier_board_mount", "target": "baseboard_hub", "label": "mount", "delta": { "x": 0, "y": 2, "z": 0 } }, { "source": "depth_camera_mount", "target": "depth_sensor_cam", "label": "mount", "delta": { "x": 0, "y": 0, "z": 0 } }, { "source": "battery_holder_bracket", "target": "li_ion_cell", "label": "mount", "delta": { "x": 0, "y": 26.5, "z": 0 } }, { "source": "oled_display_bezel", "target": "status_display", "label": "mount", "delta": { "x": 0, "y": 0.5, "z": 0 } }, { "source": "main_mcu_mount", "target": "main_mcu", "label": "mount", "delta": { "x": 0, "y": 3.7, "z": 0 } }, { "source": "spectral_sensor_triad_mount", "target": "spectral_sensor_triad", "label": "mount", "delta": { "x": 0, "y": 5.5, "z": 0 } }, { "source": "dac_led_driver_mount", "target": "dac_led_driver", "label": "mount", "delta": { "x": 0, "y": 9.5, "z": 0 } }, { "source": "vnir_led_array_mount", "target": "vnir_led_array", "label": "mount", "delta": { "x": 0, "y": 8.8, "z": 0 } }, { "source": "environmental_sensor_mount", "target": "environmental_sensor", "label": "mount", "delta": { "x": 0, "y": 0, "z": 0 } }, { "source": "teflon_diffuse_target", "target": "battery_charger_bms", "label": "attached", "delta": { "x": -10, "y": 3.4, "z": 0 } }, { "source": "teflon_diffuse_target", "target": "power_delivery_sink", "label": "attached", "delta": { "x": 10, "y": 3.5, "z": 0 } } ], "projectDescription": "An 18-channel handheld scanner that generates structured reality tokens by fusing 3D point-clouds and environmental data with spectral material classifications. The system integrates an RGB-D ToF camera, an AS7265x VNIR spectral triad, custom LED calibration arrays, and USB-PD battery management on a custom carrier board.", "imagePromptSnapshot": { "description": "Project idea: # Global Tokenizer Prototype Architecture\n\n## Overview\n\nThis document describes a tangible prototype path for a “reality scanner” that converts physical objects and environments into structured machine-readable representations rather than only images, meshes, or point clouds. The target output is a **reality token**: a fused record containing geometry, material hypotheses, environmental context, provenance, confidence, and semantic relationships, suitable for downstream simulation, digital-twin generation, and reasoning workflows.[1][2]\n\nThe architecture is intentionally staged. A practical first version should not attempt universal material science, full chemistry, and perfect physics estimation in one device. Instead, it should begin with geometry capture plus coarse material classification, then add richer sensing and inference layers over time.[3][4]\n\n## System objective\n\nThe scanner’s purpose is not just to answer “what does this object look like?” but also “what is it made of, what condition is it in, what environment surrounds it, and what machine-usable representation best predicts its behavior?” This differs from conventional 3D scanning, which generally captures geometry and texture but not deeper physical or semantic attributes.[1][2]\n\n## Core output: the reality token\n\nA first-generation reality token should be a structured record, not a file format replacement. Geometry files, spectral cubes, and sensor traces remain important artifacts, but the primary system output is a fused semantic object record.\n\nExample logical schema:\n\n```json\n{\n \"object_id\": \"scan-000184\",\n \"timestamp\": 1784775300,\n \"geometry\": {\n \"mesh_uri\": \"mesh/scan-000184.glb\",\n \"point_cloud_uri\": \"pc/scan-000184.pcd\",\n \"bounding_box_mm\": [26.1, 26.1, 58.4]\n },\n \"material\": {\n \"class\": \"316_stainless_steel\",\n \"confidence\": 0.82,\n \"spectral_signature_uri\": \"spectra/scan-000184-vnir\",\n \"secondary_candidates\": [\"304_stainless\", \"nickel-plated_steel\"]\n },\n \"surface\": {\n \"finish\": \"machined\",\n \"corrosion_state\": \"minor_oxidation\",\n \"confidence\": 0.68\n },\n \"environment\": {\n \"temperature_c\": 24.8,\n \"humidity_pct\": 46.2,\n \"voc_index\": 3\n },\n \"relationships\": [\n {\"type\": \"fastens\", \"target\": \"plate-00077\", \"confidence\": 0.55}\n ],\n \"provenance\": {\n \"rgb_sensor\": \"stereo-rgb-v1\",\n \"depth_sensor\": \"tof-v1\",\n \"spectral_sensor\": \"vnir-v1\"\n }\n}\n```\n\nThis token should remain stable even if the underlying capture stack evolves, so that future hardware can improve fidelity without breaking the computational layer built above it.\n\n## Functional layers\n\n### 1. Geometry layer\n\nThe geometry layer captures shape, dimensions, and surface structure. A practical first implementation can use stereo vision, time-of-flight (ToF), or structured-light depth sensing, all of which are commonly used to produce point clouds, meshes, and volumetric estimates in portable or bench-top scanning systems.\n\nRecommended outputs:\n\n- Point cloud.\n- Reconstructed mesh.\n- Surface normals and roughness estimates.\n- Bounding box, surface area, and volume proxies.\n\nFor v1, geometry accuracy matters more than visual polish because geometry anchors every other sensor modality during registration and fusion.\n\n### 2. Material identification layer\n\nMaterial identification should begin with spectral sensing, not with RGB image classification alone. Handheld hyperspectral systems already exist that provide automated material identification using VNIR or SWIR signatures, including portable systems with onboard or near-real-time workflows.[3][4]\n\nPractical sensor options:\n\n- **VNIR hyperspectral (400–1000 nm):** useful for many visible/near-infrared surface signatures and broad material classes.[4][5]\n- **SWIR hyperspectral:** stronger for many mineral, moisture, polymer, and industrial material distinctions; often more expensive than VNIR.[3][5]\n- **Portable NIR spectroscopy:** lower-dimensional but potentially cheaper than full hyperspectral imaging.\n- **Handheld Raman spectroscopy:** strong for point identification of many chemicals, minerals, polymers, and raw materials; widely used in industrial verification workflows.[6][7][8]\n\nThe right design principle is to use **imaging spectroscopy** when spatial maps of material are needed, and **spot spectroscopy** when high-confidence confirmation is needed on a selected location.\n\n### 3. Environmental sensing layer\n\nEnvironmental context can substantially improve interpretation. Humidity, temperature, ambient light, and gas indicators can affect spectral readings and may themselves be part of the object’s operational meaning.\n\nSuggested environmental sensors:\n\n- Temperature.\n- Relative humidity.\n- Pressure.\n- Ambient light / illumination reference.\n- Optional VOC, CO2, methane, or other gas sensors depending on the domain.\n\nThis layer should feed both the token and the calibration pipeline. For example, reflectance and certain material signatures may shift with moisture, contamination, or thermal state.\n\n### 4. Semantic inference layer\n\nThe semantic layer maps fused sensor evidence into interpretable hypotheses: object class, likely material family, surface condition, likely manufacturing process, possible function, and relationships to nearby objects. This layer should use models, rules, and external libraries together rather than relying on one classifier.\n\nSuggested outputs:\n\n- Object type hypothesis.\n- Material family and secondary candidates.\n- Surface condition class (painted, oxidized, polished, rough-cut, damaged).\n- Manufacturing hints (cast, machined, extruded, injection molded, laminated) when evidence is strong enough.\n- Relationship graph edges (attached_to, contains, rests_on, adjacent_to, likely_part_of).\n\n### 5. Fusion and tokenization layer\n\nThis is the key novelty layer. It must align RGB, depth, spectral, and environmental measurements into a common coordinate frame and emit a single structured token or token set.\n\nResponsibilities:\n\n- Sensor time synchronization.\n- Cross-modal spatial registration.\n- Confidence-weighted sensor fusion.\n- Provenance tracking per field.\n- Emission of reality tokens and related graph edges.\n\nWithout this layer, the system is merely a bundle of sensors. With it, the system becomes a machine-readable reality encoder.\n\n## Physical form factors\n\nThree viable physical forms exist.\n\n### Handheld scanner\n\nBest for field inspection, warehouse use, repair, recycling, and on-site capture. Handheld hyperspectral systems such as portable VNIR or SWIR devices show that this form factor is achievable, though cost and calibration complexity remain significant.[3][4]\n\nPros:\n\n- Portable.\n- Intuitive user experience.\n- Good fit for inspection and identification workflows.\n\nCons:\n\n- Harder illumination control.\n- Motion blur and registration challenges.\n- More difficult repeatability.\n\n### Bench-top scanner\n\nBest for first prototypes, lab workflows, and repeatable material studies.\n\nPros:\n\n- Stable geometry.\n- Controlled lighting.\n- Easier calibration.\n- Better for building ground-truth datasets.\n\nCons:\n\n- Less portable.\n- Less suited for field use.\n\n### Embedded line scanner\n\nBest for factory, sorting, and conveyor workflows, where repeatability and throughput matter more than user mobility.\n\nPros:\n\n- Highest consistency.\n- Easier lighting and pose assumptions.\n- Strong industrial ROI.\n\nCons:\n\n- Narrower use cases.\n- Less flexible for arbitrary objects.\n\n## Recommended prototype sequence\n\n### Phase 1: Geometry-first bench prototype\n\nGoal: prove registration, object segmentation, and the token structure before solving hard portable spectroscopy problems.\n\nHardware:\n\n- RGB stereo or RGB + depth camera.\n- ToF or structured-light sensor.\n- Controlled lighting enclosure.\n- Embedded compute or small workstation.\n\nSoftware:\n\n- Camera calibration.\n- Point-cloud generation.\n- Mesh reconstruction.\n- Object segmentation.\n- Initial token writer with geometry-only plus placeholder material fields.\n\nWhy this phase first: it creates the spatial backbone and data model needed for all later sensors.\n\n### 2: Add coarse material classification\n\nGoal: move from “object geometry” to “object + material-family token.”\n\nHardware options:\n\n- Portable VNIR hyperspectral camera or module for surface classification.[4][9]\n- Alternatively, a spot Raman device for confirmation-style workflows.[6][7]\n\nSoftware:\n\n- Spectral preprocessing and reflectance normalization.\n- Material library matching.\n- Pixel-to-object spectral aggregation.\n- Confidence scoring and candidate ranking.\n\nRecommended target classes for v1:\n\n- Steel / stainless steel.\n- Aluminum.\n- Copper / brass.\n- ABS / PVC / PE-like plastics.\n- Wood / paper / composite.\n- Painted vs. bare metal.\n\nThis class set is intentionally coarse because broad material families are easier to classify reliably than exact alloy or polymer grade.\n\n### Phase 3: Add semantic and condition inference\n\nGoal: infer surface state, likely process, and object role.\n\nCapabilities:\n\n- Surface finish classification.\n- Corrosion / oxidation / contamination detection.\n- Basic object taxonomy.\n- Relationship extraction among scanned objects.\n\nThis phase benefits from domain-specific training data rather than generic internet-scale image labeling.\n\n### Phase 4: Add active confirmation sensors\n\nGoal: improve confidence on edge cases and move toward “behavior-aware” tokens.\n\nPossible additions:\n\n- Raman for molecular / compound confirmation.[6][7]\n- Thermal imaging for heat-retention or conductivity clues.\n- Contact probes for conductivity or hardness in controlled workflows.\n- Optional XRF in professional/regulated settings for elemental analysis.\n\nAt this stage the scanner begins to answer not only “what is this likely made of?” but “what evidence supports that answer strongly enough to use in simulation or QA?”\n\n## Sensor trade-off matrix\n\n| Sensor | Strength | Weakness | Best use |\n|---|---|---|---|\n| RGB camera | Cheap, high resolution, familiar | Weak on material truth | Visual context, segmentation |\n| Stereo / ToF / structured light | Geometry and dimension capture | Sensitive to shiny/dark surfaces depending on method | Shape backbone |\n| VNIR hyperspectral | Spatially resolved spectral maps; portable options exist.[4][9] | Cost, data volume, lighting sensitivity | Broad material classification |\n| SWIR hyperspectral | Stronger material separability in many industrial domains.[3][5] | Higher cost, more complex optics | Minerals, polymers, moisture-sensitive materials |\n| Handheld Raman | Strong chemical/material fingerprinting; commercial handheld devices are mature.[6][8] | Point measurement, fluorescence issues on some samples | Confirmatory identification |\n| Environmental sensors | Cheap context and calibration support | Indirect for material ID | Context, drift reduction |\n\n## Compute architecture\n\nThe compute path should be modular.\n\n### Edge compute\n\nUse local compute for:\n\n- Sensor control.\n- Calibration.\n- Registration.\n- Real-time preview.\n- Lightweight material matching.\n- Token assembly.\n\n### Cloud or workstation compute\n\nUse heavier compute for:\n\n- Large spectral library search.\n- Model retraining.\n- Digital-twin generation.\n- Cross-object graph reasoning.\n- Dataset curation.\n\nA good prototype should function locally for basic scans, then optionally sync to a higher-capability backend for deeper analysis.\n\n## Data and software architecture\n\n### Core software modules\n\n- Sensor drivers.\n- Calibration manager.\n- Capture session manager.\n- Registration engine.\n- Spectral preprocessing.\n- Material classifier.\n- Semantic inference engine.\n- Reality token emitter.\n- Asset store for raw artifacts.\n- Graph / database interface.\n\n### Storage outputs\n\nThe system should store:\n\n- Raw RGB frames.\n- Raw depth frames / point clouds.\n- Spectral cubes or spectra.\n- Calibration metadata.\n- Derived meshes.\n- Reality tokens.\n- Confidence and provenance logs.\n\nThe raw artifacts matter because future reprocessing may improve the token without requiring a re-scan.\n\n## Suggested BOM tiers\n\n### Tier A: Low-risk bench prototype\n\n- Depth camera or stereo camera.\n- Fixed illumination enclosure.\n- Small environmental sensor board.\n- Embedded PC or mini workstation.\n- Optional off-device Raman or spectral confirmation instrument.\n\nPurpose: validate token schema, registration, and the geometry-to-semantics pipeline at low complexity.\n\n### Tier B: Portable field prototype\n\n- Compact RGB + depth package.\n- Portable VNIR hyperspectral unit or compact imaging spectrometer.[4][9]\n- Small compute module with battery pack.\n- Environmental board.\n- Tablet or phone UI.\n\nPurpose: prove handheld material-aware scanning in real environments.\n\n### Tier C: High-confidence industrial prototype\n\n- Bench or cart-based platform.\n- RGB + high-quality geometry sensor.\n- VNIR and/or SWIR hyperspectral imaging.[5]\n- Raman confirmation unit.[6][8]\n- Controlled lighting and reference targets.\n\nPurpose: high-confidence digital-twin intake, QA, and industrial material identification.\n\n## Best first use cases\n\nThe strongest first markets are those where broad material identity and geometry together are already highly valuable.\n\nRecommended early domains:\n\n- Manufacturing incoming inspection.\n- Raw material verification and sorting.\n- Repair / maintenance part identification.\n- Recycling and waste-stream classification.\n- Mineral or geology field classification.\n- Digital-twin intake for known industrial parts.\n\nThese domains align with existing strengths of handheld Raman and hyperspectral systems, which are already used for material verification, raw material identification, and on-site analysis.[6][7][3]\n\n## Risks and limitations\n\nA reality scanner should not overclaim.\n\nKey limits:\n\n- Appearance does not equal composition.\n- Surface spectroscopy may fail to reveal bulk material under coatings or contaminants.\n- Exact mechanical properties usually cannot be determined passively without strong priors or active testing.\n- Lighting, pose, and calibration drift can degrade classification.\n- Universal object understanding is much harder than domain-specific scanning.\n\nBecause of this, every material or property field should carry provenance and confidence rather than pretending certainty.\n\n## Recommended v1 product definition\n\nThe most credible v1 product is:\n\n**A bench-top or portable material-aware geometry scanner that emits structured reality tokens for industrial parts and known material classes.**\n\nThat product is tangible, useful, and close enough to existing sensing technology to build now, while still pointing toward the longer-term vision of automatic digital-twin generation from observation.[1][2]\n\n## Next steps\n\n1. Define the stable reality-token schema before final hardware selection.\n2. Choose one narrow domain, such as fastener identification, plastics sorting, or industrial part intake.\n3. Build the bench prototype first to solve calibration and fusion under controlled conditions.\n4. Add one spectral modality, preferably VNIR imaging or Raman confirmation, before attempting multi-sensor universality.\n5. Build a labeled dataset with geometry, spectra, and known ground truth.\n6. Only after that, move to handheld packaging and broader object categories.\n\nThis sequence keeps the system grounded in measurable progress rather than turning into an open-ended research project too early.\n\nDesign notes: bench-top scanner, structured reality token, coarse material classification, VNIR hyperspectral, controlled lighting enclosure, modular compute modular, modular compute, industrial part intake, modular compute path for reality scanner\n\nElectrical components (current): Main System Controller (mcu, 55x40x4.7mm); Custom Handheld Carrier Board (module, 85x56x12mm); 3x sensor: 18-Channel Spectral Triad, RGB-D ToF Camera Module, Ambient Air & VOC Sensor; 2x actuator: I2C LED Driver DAC, VNIR Multi-Wavelength LED Array; 3x power: USB-PD Sink Controller, 1S Li-Ion Charger & Powerpath, Internal Li-Ion Battery Pack; User Interface OLED (display, 35x35x5mm)\n\nMechanical/structural parts (current): 11x 3d_printed: Handheld Enclosure Front Cover, Handheld Enclosure Back Cover, Carrier Board Mounting Frame, Depth Camera Secure Bracket +7 more; 3x misc: M3 Threaded Heat-Set Inserts, Enclosure Assembly Screws, PCB Mounting Screws; Spectral Calibration Reference Target (structural, 30x30x6mm)", "tags": [ "bench-top scanner", "structured reality token", "coarse material classification", "VNIR hyperspectral", "controlled lighting enclosure", "modular compute modular", "modular compute", "industrial part intake", "modular compute path for reality scanner" ] }, "originalPrompt": "# Global Tokenizer Prototype Architecture\n\n## Overview\n\nThis document describes a tangible prototype path for a “reality scanner” that converts physical objects and environments into structured machine-readable representations rather than only images, meshes, or point clouds. The target output is a **reality token**: a fused record containing geometry, material hypotheses, environmental context, provenance, confidence, and semantic relationships, suitable for downstream simulation, digital-twin generation, and reasoning workflows.[1][2]\n\nThe architecture is intentionally staged. A practical first version should not attempt universal material science, full chemistry, and perfect physics estimation in one device. Instead, it should begin with geometry capture plus coarse material classification, then add richer sensing and inference layers over time.[3][4]\n\n## System objective\n\nThe scanner’s purpose is not just to answer “what does this object look like?” but also “what is it made of, what condition is it in, what environment surrounds it, and what machine-usable representation best predicts its behavior?” This differs from conventional 3D scanning, which generally captures geometry and texture but not deeper physical or semantic attributes.[1][2]\n\n## Core output: the reality token\n\nA first-generation reality token should be a structured record, not a file format replacement. Geometry files, spectral cubes, and sensor traces remain important artifacts, but the primary system output is a fused semantic object record.\n\nExample logical schema:\n\n```json\n{\n \"object_id\": \"scan-000184\",\n \"timestamp\": 1784775300,\n \"geometry\": {\n \"mesh_uri\": \"mesh/scan-000184.glb\",\n \"point_cloud_uri\": \"pc/scan-000184.pcd\",\n \"bounding_box_mm\": [26.1, 26.1, 58.4]\n },\n \"material\": {\n \"class\": \"316_stainless_steel\",\n \"confidence\": 0.82,\n \"spectral_signature_uri\": \"spectra/scan-000184-vnir\",\n \"secondary_candidates\": [\"304_stainless\", \"nickel-plated_steel\"]\n },\n \"surface\": {\n \"finish\": \"machined\",\n \"corrosion_state\": \"minor_oxidation\",\n \"confidence\": 0.68\n },\n \"environment\": {\n \"temperature_c\": 24.8,\n \"humidity_pct\": 46.2,\n \"voc_index\": 3\n },\n \"relationships\": [\n {\"type\": \"fastens\", \"target\": \"plate-00077\", \"confidence\": 0.55}\n ],\n \"provenance\": {\n \"rgb_sensor\": \"stereo-rgb-v1\",\n \"depth_sensor\": \"tof-v1\",\n \"spectral_sensor\": \"vnir-v1\"\n }\n}\n```\n\nThis token should remain stable even if the underlying capture stack evolves, so that future hardware can improve fidelity without breaking the computational layer built above it.\n\n## Functional layers\n\n### 1. Geometry layer\n\nThe geometry layer captures shape, dimensions, and surface structure. A practical first implementation can use stereo vision, time-of-flight (ToF), or structured-light depth sensing, all of which are commonly used to produce point clouds, meshes, and volumetric estimates in portable or bench-top scanning systems.\n\nRecommended outputs:\n\n- Point cloud.\n- Reconstructed mesh.\n- Surface normals and roughness estimates.\n- Bounding box, surface area, and volume proxies.\n\nFor v1, geometry accuracy matters more than visual polish because geometry anchors every other sensor modality during registration and fusion.\n\n### 2. Material identification layer\n\nMaterial identification should begin with spectral sensing, not with RGB image classification alone. Handheld hyperspectral systems already exist that provide automated material identification using VNIR or SWIR signatures, including portable systems with onboard or near-real-time workflows.[3][4]\n\nPractical sensor options:\n\n- **VNIR hyperspectral (400–1000 nm):** useful for many visible/near-infrared surface signatures and broad material classes.[4][5]\n- **SWIR hyperspectral:** stronger for many mineral, moisture, polymer, and industrial material distinctions; often more expensive than VNIR.[3][5]\n- **Portable NIR spectroscopy:** lower-dimensional but potentially cheaper than full hyperspectral imaging.\n- **Handheld Raman spectroscopy:** strong for point identification of many chemicals, minerals, polymers, and raw materials; widely used in industrial verification workflows.[6][7][8]\n\nThe right design principle is to use **imaging spectroscopy** when spatial maps of material are needed, and **spot spectroscopy** when high-confidence confirmation is needed on a selected location.\n\n### 3. Environmental sensing layer\n\nEnvironmental context can substantially improve interpretation. Humidity, temperature, ambient light, and gas indicators can affect spectral readings and may themselves be part of the object’s operational meaning.\n\nSuggested environmental sensors:\n\n- Temperature.\n- Relative humidity.\n- Pressure.\n- Ambient light / illumination reference.\n- Optional VOC, CO2, methane, or other gas sensors depending on the domain.\n\nThis layer should feed both the token and the calibration pipeline. For example, reflectance and certain material signatures may shift with moisture, contamination, or thermal state.\n\n### 4. Semantic inference layer\n\nThe semantic layer maps fused sensor evidence into interpretable hypotheses: object class, likely material family, surface condition, likely manufacturing process, possible function, and relationships to nearby objects. This layer should use models, rules, and external libraries together rather than relying on one classifier.\n\nSuggested outputs:\n\n- Object type hypothesis.\n- Material family and secondary candidates.\n- Surface condition class (painted, oxidized, polished, rough-cut, damaged).\n- Manufacturing hints (cast, machined, extruded, injection molded, laminated) when evidence is strong enough.\n- Relationship graph edges (attached_to, contains, rests_on, adjacent_to, likely_part_of).\n\n### 5. Fusion and tokenization layer\n\nThis is the key novelty layer. It must align RGB, depth, spectral, and environmental measurements into a common coordinate frame and emit a single structured token or token set.\n\nResponsibilities:\n\n- Sensor time synchronization.\n- Cross-modal spatial registration.\n- Confidence-weighted sensor fusion.\n- Provenance tracking per field.\n- Emission of reality tokens and related graph edges.\n\nWithout this layer, the system is merely a bundle of sensors. With it, the system becomes a machine-readable reality encoder.\n\n## Physical form factors\n\nThree viable physical forms exist.\n\n### Handheld scanner\n\nBest for field inspection, warehouse use, repair, recycling, and on-site capture. Handheld hyperspectral systems such as portable VNIR or SWIR devices show that this form factor is achievable, though cost and calibration complexity remain significant.[3][4]\n\nPros:\n\n- Portable.\n- Intuitive user experience.\n- Good fit for inspection and identification workflows.\n\nCons:\n\n- Harder illumination control.\n- Motion blur and registration challenges.\n- More difficult repeatability.\n\n### Bench-top scanner\n\nBest for first prototypes, lab workflows, and repeatable material studies.\n\nPros:\n\n- Stable geometry.\n- Controlled lighting.\n- Easier calibration.\n- Better for building ground-truth datasets.\n\nCons:\n\n- Less portable.\n- Less suited for field use.\n\n### Embedded line scanner\n\nBest for factory, sorting, and conveyor workflows, where repeatability and throughput matter more than user mobility.\n\nPros:\n\n- Highest consistency.\n- Easier lighting and pose assumptions.\n- Strong industrial ROI.\n\nCons:\n\n- Narrower use cases.\n- Less flexible for arbitrary objects.\n\n## Recommended prototype sequence\n\n### Phase 1: Geometry-first bench prototype\n\nGoal: prove registration, object segmentation, and the token structure before solving hard portable spectroscopy problems.\n\nHardware:\n\n- RGB stereo or RGB + depth camera.\n- ToF or structured-light sensor.\n- Controlled lighting enclosure.\n- Embedded compute or small workstation.\n\nSoftware:\n\n- Camera calibration.\n- Point-cloud generation.\n- Mesh reconstruction.\n- Object segmentation.\n- Initial token writer with geometry-only plus placeholder material fields.\n\nWhy this phase first: it creates the spatial backbone and data model needed for all later sensors.\n\n### 2: Add coarse material classification\n\nGoal: move from “object geometry” to “object + material-family token.”\n\nHardware options:\n\n- Portable VNIR hyperspectral camera or module for surface classification.[4][9]\n- Alternatively, a spot Raman device for confirmation-style workflows.[6][7]\n\nSoftware:\n\n- Spectral preprocessing and reflectance normalization.\n- Material library matching.\n- Pixel-to-object spectral aggregation.\n- Confidence scoring and candidate ranking.\n\nRecommended target classes for v1:\n\n- Steel / stainless steel.\n- Aluminum.\n- Copper / brass.\n- ABS / PVC / PE-like plastics.\n- Wood / paper / composite.\n- Painted vs. bare metal.\n\nThis class set is intentionally coarse because broad material families are easier to classify reliably than exact alloy or polymer grade.\n\n### Phase 3: Add semantic and condition inference\n\nGoal: infer surface state, likely process, and object role.\n\nCapabilities:\n\n- Surface finish classification.\n- Corrosion / oxidation / contamination detection.\n- Basic object taxonomy.\n- Relationship extraction among scanned objects.\n\nThis phase benefits from domain-specific training data rather than generic internet-scale image labeling.\n\n### Phase 4: Add active confirmation sensors\n\nGoal: improve confidence on edge cases and move toward “behavior-aware” tokens.\n\nPossible additions:\n\n- Raman for molecular / compound confirmation.[6][7]\n- Thermal imaging for heat-retention or conductivity clues.\n- Contact probes for conductivity or hardness in controlled workflows.\n- Optional XRF in professional/regulated settings for elemental analysis.\n\nAt this stage the scanner begins to answer not only “what is this likely made of?” but “what evidence supports that answer strongly enough to use in simulation or QA?”\n\n## Sensor trade-off matrix\n\n| Sensor | Strength | Weakness | Best use |\n|---|---|---|---|\n| RGB camera | Cheap, high resolution, familiar | Weak on material truth | Visual context, segmentation |\n| Stereo / ToF / structured light | Geometry and dimension capture | Sensitive to shiny/dark surfaces depending on method | Shape backbone |\n| VNIR hyperspectral | Spatially resolved spectral maps; portable options exist.[4][9] | Cost, data volume, lighting sensitivity | Broad material classification |\n| SWIR hyperspectral | Stronger material separability in many industrial domains.[3][5] | Higher cost, more complex optics | Minerals, polymers, moisture-sensitive materials |\n| Handheld Raman | Strong chemical/material fingerprinting; commercial handheld devices are mature.[6][8] | Point measurement, fluorescence issues on some samples | Confirmatory identification |\n| Environmental sensors | Cheap context and calibration support | Indirect for material ID | Context, drift reduction |\n\n## Compute architecture\n\nThe compute path should be modular.\n\n### Edge compute\n\nUse local compute for:\n\n- Sensor control.\n- Calibration.\n- Registration.\n- Real-time preview.\n- Lightweight material matching.\n- Token assembly.\n\n### Cloud or workstation compute\n\nUse heavier compute for:\n\n- Large spectral library search.\n- Model retraining.\n- Digital-twin generation.\n- Cross-object graph reasoning.\n- Dataset curation.\n\nA good prototype should function locally for basic scans, then optionally sync to a higher-capability backend for deeper analysis.\n\n## Data and software architecture\n\n### Core software modules\n\n- Sensor drivers.\n- Calibration manager.\n- Capture session manager.\n- Registration engine.\n- Spectral preprocessing.\n- Material classifier.\n- Semantic inference engine.\n- Reality token emitter.\n- Asset store for raw artifacts.\n- Graph / database interface.\n\n### Storage outputs\n\nThe system should store:\n\n- Raw RGB frames.\n- Raw depth frames / point clouds.\n- Spectral cubes or spectra.\n- Calibration metadata.\n- Derived meshes.\n- Reality tokens.\n- Confidence and provenance logs.\n\nThe raw artifacts matter because future reprocessing may improve the token without requiring a re-scan.\n\n## Suggested BOM tiers\n\n### Tier A: Low-risk bench prototype\n\n- Depth camera or stereo camera.\n- Fixed illumination enclosure.\n- Small environmental sensor board.\n- Embedded PC or mini workstation.\n- Optional off-device Raman or spectral confirmation instrument.\n\nPurpose: validate token schema, registration, and the geometry-to-semantics pipeline at low complexity.\n\n### Tier B: Portable field prototype\n\n- Compact RGB + depth package.\n- Portable VNIR hyperspectral unit or compact imaging spectrometer.[4][9]\n- Small compute module with battery pack.\n- Environmental board.\n- Tablet or phone UI.\n\nPurpose: prove handheld material-aware scanning in real environments.\n\n### Tier C: High-confidence industrial prototype\n\n- Bench or cart-based platform.\n- RGB + high-quality geometry sensor.\n- VNIR and/or SWIR hyperspectral imaging.[5]\n- Raman confirmation unit.[6][8]\n- Controlled lighting and reference targets.\n\nPurpose: high-confidence digital-twin intake, QA, and industrial material identification.\n\n## Best first use cases\n\nThe strongest first markets are those where broad material identity and geometry together are already highly valuable.\n\nRecommended early domains:\n\n- Manufacturing incoming inspection.\n- Raw material verification and sorting.\n- Repair / maintenance part identification.\n- Recycling and waste-stream classification.\n- Mineral or geology field classification.\n- Digital-twin intake for known industrial parts.\n\nThese domains align with existing strengths of handheld Raman and hyperspectral systems, which are already used for material verification, raw material identification, and on-site analysis.[6][7][3]\n\n## Risks and limitations\n\nA reality scanner should not overclaim.\n\nKey limits:\n\n- Appearance does not equal composition.\n- Surface spectroscopy may fail to reveal bulk material under coatings or contaminants.\n- Exact mechanical properties usually cannot be determined passively without strong priors or active testing.\n- Lighting, pose, and calibration drift can degrade classification.\n- Universal object understanding is much harder than domain-specific scanning.\n\nBecause of this, every material or property field should carry provenance and confidence rather than pretending certainty.\n\n## Recommended v1 product definition\n\nThe most credible v1 product is:\n\n**A bench-top or portable material-aware geometry scanner that emits structured reality tokens for industrial parts and known material classes.**\n\nThat product is tangible, useful, and close enough to existing sensing technology to build now, while still pointing toward the longer-term vision of automatic digital-twin generation from observation.[1][2]\n\n## Next steps\n\n1. Define the stable reality-token schema before final hardware selection.\n2. Choose one narrow domain, such as fastener identification, plastics sorting, or industrial part intake.\n3. Build the bench prototype first to solve calibration and fusion under controlled conditions.\n4. Add one spectral modality, preferably VNIR imaging or Raman confirmation, before attempting multi-sensor universality.\n5. Build a labeled dataset with geometry, spectra, and known ground truth.\n6. Only after that, move to handheld packaging and broader object categories.\n\nThis sequence keeps the system grounded in measurable progress rather than turning into an open-ended research project too early.", "plan": "- Embedded Computer / Controller: NVIDIA Jetson Orin Nano or Raspberry Pi 5 to handle sensor registration, point-cloud processing, and token assembly.\n- Depth & RGB Sensors: Intel RealSense D435i or Orbbec Astra stereo/ToF camera for point-cloud, mesh, and geometry capture.\n- Spectroscopy & Material Sensors: Compact VNIR spectrometer module (e.g., Hamamatsu micro-spectrometer) or a spot Raman spectroscopic sensor interface.\n- Environmental Sensor Board: BME280 sensor breakout for ambient temperature, humidity, and barometric pressure data.\n- Power & Interface: 12V DC power supply, internal 5V/3A buck converter, and custom USB 3.0/I2C hub wiring.\n- Frame & Mechanical Hardware: Aluminum extrusion profiles (2020), T-nuts, M3/M4 mounting screws, and acrylic baseplates for the stable bench-top setup.\n- Custom 3D Printed Parts: Monolithic scanner head enclosure, adjustable camera mounts, spectrometer alignment bracket, and a calibrated diffuse reflectance target holder.", "notes": [ "bench-top scanner", "structured reality token", "coarse material classification", "VNIR hyperspectral", "controlled lighting enclosure", "modular compute modular", "modular compute", "industrial part intake", "modular compute path for reality scanner" ], "projectId": "76cfda06-c006-46b6-9ce4-d53895232cd3", "instructionPreamble": { "tools": [ "3D printer (PETG and PLA capable)", "Soldering iron with fine tip", "Solder (rosin-core)", "Heat-set insert soldering tip", "M3 hex key", "Wire strippers", "Digital multimeter", "USB-C programming cable" ], "assumptions": [ "Familiarity with I2C and USB protocol wiring", "Experience with 3D printing and post-processing", "Basic soldering and electronics debugging skills", "Ability to program ARM/ESP32 microcontrollers" ] }, "instructionSteps": [ { "id": "fabricate", "subSteps": [ { "id": "fabricate_1", "title": "3D print the enclosure and mechanical brackets", "partIds": [ "handheld_enclosure_front", "handheld_enclosure_back", "carrier_board_mount", "depth_camera_mount", "battery_holder_bracket", "oled_display_bezel", "main_mcu_mount", "spectral_sensor_triad_mount", "dac_led_driver_mount", "vnir_led_array_mount", "environmental_sensor_mount" ] }, { "id": "fabricate_2", "title": "Install brass heat-set inserts into enclosure front", "partIds": [ "handheld_enclosure_front", "brass_threaded_inserts" ] }, { "id": "fabricate_3", "title": "Slide Teflon calibration target into front slot", "partIds": [ "handheld_enclosure_front", "teflon_diffuse_target" ] } ] }, { "id": "wire", "subSteps": [ { "id": "wire_1", "title": "Wire power delivery sink to BMS charger module", "partIds": [ "power_delivery_sink", "battery_charger_bms" ] }, { "id": "wire_2", "title": "Connect Li-Ion battery pack to charger circuit", "partIds": [ "li_ion_cell", "battery_charger_bms" ] }, { "id": "wire_3", "title": "Connect main MCU I2C bus to baseboard hub", "partIds": [ "main_mcu", "baseboard_hub" ] }, { "id": "wire_4", "title": "Solder shared I2C bus wiring to baseboard hub distribution point", "partIds": [ "baseboard_hub", "spectral_sensor_triad", "environmental_sensor", "dac_led_driver", "battery_charger_bms", "power_delivery_sink", "status_display" ] }, { "id": "wire_5", "title": "Solder LED driver DAC power lines to VNIR LED array", "partIds": [ "dac_led_driver", "vnir_led_array" ] }, { "id": "wire_6", "title": "Solder USB data lines from main MCU to depth sensor camera", "partIds": [ "main_mcu", "depth_sensor_cam" ] } ] }, { "id": "bringup", "subSteps": [ { "id": "bringup_1", "title": "Verify basic power rail voltages and battery charging", "partIds": [ "power_delivery_sink", "battery_charger_bms", "li_ion_cell" ] }, { "id": "bringup_2", "title": "Scan I2C address map to confirm peripheral responses", "partIds": [ "main_mcu", "baseboard_hub", "spectral_sensor_triad", "environmental_sensor", "dac_led_driver", "status_display" ] }, { "id": "bringup_3", "title": "Test VNIR LED driving and spectral triad detection", "partIds": [ "dac_led_driver", "vnir_led_array", "spectral_sensor_triad" ] }, { "id": "bringup_4", "title": "Test USB recognition of depth camera module", "partIds": [ "main_mcu", "depth_sensor_cam" ] } ] }, { "id": "assemble", "subSteps": [ { "id": "assemble_1", "title": "Secure electronics to their respective mounts and brackets", "partIds": [ "baseboard_hub", "carrier_board_mount", "depth_sensor_cam", "depth_camera_mount", "li_ion_cell", "battery_holder_bracket", "status_display", "oled_display_bezel", "main_mcu", "main_mcu_mount", "spectral_sensor_triad", "spectral_sensor_triad_mount", "dac_led_driver", "dac_led_driver_mount", "vnir_led_array", "vnir_led_array_mount", "environmental_sensor", "environmental_sensor_mount" ] }, { "id": "assemble_2", "title": "Snap-fit VNIR array to spectral sensor triad assembly", "partIds": [ "vnir_led_array_mount", "spectral_sensor_triad_mount" ] }, { "id": "assemble_3", "title": "Mount the sub-brackets inside front and back covers using screws", "partIds": [ "handheld_enclosure_front", "handheld_enclosure_back", "carrier_board_mount", "depth_camera_mount", "battery_holder_bracket", "spectral_sensor_triad_mount", "environmental_sensor_mount", "pcb_m3_screws" ] }, { "id": "assemble_4", "title": "Fasten front cover to back cover with assembly screws", "partIds": [ "handheld_enclosure_front", "handheld_enclosure_back", "enclosure_m3_screws" ] } ] } ], "wiringCleanedHash": "76cfda06-c006-46b6-9ce4-d53895232cd3::baseboard_hub:+5V|GND|I2C1_SDA|I2C1_SCL|USB_D_P|USB_D_N,battery_charger_bms:VBUS|PMID|SYS|BAT|SDA|SCL|INT|OTG|GND,dac_led_driver:VIN|SDA|SCL|OUT1|OUT2|GND,depth_sensor_cam:VBUS|USB3_D_P|USB3_D_N|USB3_TX_P|USB3_TX_N|USB3_RX_P|USB3_RX_N|GND,environmental_sensor:VDDIO|GND|SDA|SCL|SDO|CSB,li_ion_cell:+|-,main_mcu:GPIO2_SDA|GPIO3_SCL|GPIO14_TXD|GPIO15_RXD|USB_OTG_DP|USB_OTG_DM|PCIE_TXP|PCIE_TXN|+5V|GND,power_delivery_sink:VBUS|CC1|CC2|VREG|SDA|SCL|OUT_PDO1|OUT_PDO2|GND,spectral_sensor_triad:VDD|GND|I2C_SDA|I2C_SCL|INT|RST,status_display:VIN|GND|SCL|SDA|RST,vnir_led_array:LED_A1|LED_K1|LED_A2|LED_K2|GND_THERMAL::data|baseboard_hub|I2C1_SCL|battery_charger_bms|SCL|i2c|;data|baseboard_hub|I2C1_SCL|power_delivery_sink|SCL|i2c|;data|baseboard_hub|I2C1_SDA|battery_charger_bms|SDA|i2c|;data|baseboard_hub|I2C1_SDA|power_delivery_sink|SDA|i2c|;data|main_mcu|GPIO2_SDA|baseboard_hub|I2C1_SDA|i2c|;data|main_mcu|GPIO3_SCL|baseboard_hub|I2C1_SCL|i2c|;data|main_mcu|USB_OTG_DM|depth_sensor_cam|USB3_D_N|usb|;data|main_mcu|USB_OTG_DP|depth_sensor_cam|USB3_D_P|usb|;power|baseboard_hub|+5V|dac_led_driver|VIN||5V;power|baseboard_hub|+5V|depth_sensor_cam|VBUS||5V;power|baseboard_hub|+5V|environmental_sensor|VDDIO||3.3V;power|baseboard_hub|+5V|main_mcu|+5V||5V;power|baseboard_hub|+5V|power_delivery_sink|VBUS||5V;power|baseboard_hub|+5V|spectral_sensor_triad|VDD||3.3V;power|baseboard_hub|+5V|status_display|VIN||3.3V;power|baseboard_hub|GND|dac_led_driver|GND||;power|baseboard_hub|GND|depth_sensor_cam|GND||;power|baseboard_hub|GND|environmental_sensor|GND||;power|baseboard_hub|GND|main_mcu|GND||;power|baseboard_hub|GND|power_delivery_sink|GND||;power|baseboard_hub|GND|spectral_sensor_triad|GND||;power|baseboard_hub|GND|status_display|GND||;power|battery_charger_bms|BAT|li_ion_cell|+||4.2V;power|battery_charger_bms|GND|baseboard_hub|GND||;power|battery_charger_bms|SYS|baseboard_hub|+5V||5V;power|dac_led_driver|GND|vnir_led_array|GND_THERMAL||;power|dac_led_driver|OUT1|vnir_led_array|LED_A1||3.6V;power|dac_led_driver|OUT2|vnir_led_array|LED_A2||3.6V;power|li_ion_cell|+|battery_charger_bms|BAT||3.7V;power|power_delivery_sink|GND|battery_charger_bms|GND||;power|power_delivery_sink|VBUS|battery_charger_bms|VBUS||5V" }