🤖 TrackieLLM

A High-Complexity, Multimodal AI Platform for Accessibility, Safety, and Autonomy
Engineered with precision and innovation for the 2025 Industrial Innovation Olympiad

🌐 Overview

TrackieLLM is a production-grade multimodal AI framework designed to expand perception, mobility, and safety for people with visual impairment. It fuses low-latency computer vision, robust audio processing and an on-device LLM core to deliver near real-time reasoning and assistive actions across embedded and mobile platforms.

Supported deployment targets:

• Embedded Linux (Orange Pi with CUDA, Raspberry Pi variants)
• Mobile (Android, iOS — Metal optimized)
• Desktop/Edge servers (x86_64 with CUDA / ROCm)
• Hybrid: device + phone (SpotWay model described below)

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🧭 New Product Variants & Form Factors

Trackway (Hat) — full embedded compute

Trackway is the full-featured, computerized hat platform: camera, IMU, microphone array, edge compute (OrangePi/Jetson/embedded module), and battery. The on-hat system can run TrackieLLM locally (models + reasoning). It is designed for maximum autonomy and offline safety.

SpotWay — low-cost hybrid hat

SpotWay uses microcontrollers on the hat for sensor aggregation and low-latency pre-processing. Heavy models and reasoning run on the user’s smartphone (Android/iOS) over a secure local link (Bluetooth LE / Wi-Fi Direct). SpotWay is optimized for accessibility and cost-efficiency.

Trackway Glasses

Compact eyewear form factor (stereo cameras, bone-conduction audio). Same software stack but mechanical and thermal constraints require careful model selection and power budgeting.

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🛠️ Deploying Trackie — High-level Path (one-line summary)

1. Obtain hardware (Orange Pi / supported board) → 2. Prepare OS image and toolchain → 3. Acquire model assets (ONNX / GGUF) → 4. Build native binaries (C/C++/Rust) with CMake + Cargo → 5. Package into service/container → 6. Deploy as systemd service or Docker container → 7. Validate sensors and run safety tests.

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🔧 Detailed Deployment Guide — Embedded (Orange Pi / RPi / ARM)

1) Preconditions

• Host machine (build): x86_64 Linux or containerized cross-build environment.
• Cross-toolchains: gcc / clang for target arch, aarch64-linux-gnu-gcc etc. (or use Docker build images).
• NDK for Android builds, Xcode for iOS builds (macOS host).
• Required runtimes: ONNX Runtime (ARM build), llama.cpp (GGUF runtime), onnxruntime or custom lightweight runners, libsndfile/pulseaudio or ALSA.
• Model assets placed in /opt/trackie/models/ (permissions restricted).
• Hardware: camera (UVC or CSI), microphone(s), IMU, battery management, optional LIDAR.

2) Clone code + assets

git clone https://github.com/phkaiser13/trackway.git trackie-src
cd trackie-src
# Acquire model bundles (place into /opt/trackie/models or ./models for dev)
# Example local path:
mkdir -p assets/models
# copy ONNX / GGUF files into assets/models

3) Build (native or cross)

Prefer reproducible build containers. Example with native CMake + Cargo:

# create build dir
cmake -S . -B build -DTRACKIE_ENABLE_CUDA=ON -DTRACKIE_BUILD_TESTS=ON
cmake --build build -- -j$(nproc)
# Optionally build Rust crates (if Cargo not wired with CMake)
cargo build --workspace --release

Cross-compiling for aarch64 from x86_64 should use a Dockerfile that installs aarch64-linux-gnu-gcc, sets CMAKE_TOOLCHAIN_FILE, or uses dockcross images.

4) Packaging & Service

Recommended: package as a systemd user service + optional Docker image for field updates.

Systemd service example (place as /etc/systemd/system/trackie.service):

[Unit]
Description=TrackieLLM runtime
After=network.target

[Service]
User=trackie
Group=trackie
WorkingDirectory=/opt/trackie
ExecStart=/opt/trackie/bin/trackie-core --config /etc/trackie/config.yaml
Restart=on-failure
LimitNOFILE=65536

[Install]
WantedBy=multi-user.target

Alternatively build and publish a Docker image:

FROM ubuntu:24.04
# install libs, copy binaries, set entrypoint
COPY build/ /opt/trackie/
ENTRYPOINT ["/opt/trackie/bin/trackie-core"]

5) Model & Runtime Considerations

• Use ONNX Runtime for vision models (TensorOps optimized with CUDA/ROCm where available).
• For LLM (Mistral-7B GGUF), use llama.cpp or equivalent optimized runtimes compiled for ARM aarch64 + NEON / VNNI / FP16.
• Place large model files on a high-throughput NVMe or SD card with good I/O.
• For deterministic performance, pin CPU governors, tune swappiness, and disable unneeded services.

6) Sensors & Safety Calibration

• Calibrate camera intrinsics / extrinsics, IMU alignment, and microphone arrays.
• Run validation suite: obstacle detection, step detection, free-space detection, grasp-point detection.
• Safety modes: audible alarm, haptic/vibration fallback, emergency stop (if robot actuators present).

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🧱 Building your own Trackie Robot — practical blueprint

A) Mechanical & Power

• Chassis: compact, low center of gravity if wheeled; balanced for wearables (hat).
• Actuators: if moving platform, use differential drive with closed-loop encoders.
• Power: dedicated battery service, battery gauge, low-voltage cutoffs, UPS for critical features (emergency beeps).
• Connectors: standard JST / SMBus for sensors; design for hot-swap battery.

B) Electronics & Sensors

• Main compute: Orange Pi CM4-class module, or Jetson Orin Nano for higher perf.
• Microcontroller: ESP32 / STM32 for sensor preproc and real-time closed-loop tasks.
• Sensors: stereo/mono camera (UVC or CSI), IMU (6/9DOF), ultrasonic or LIDAR for redundancy, multiple microphones for beamforming.
• Communications: USB for cameras, I2C/SPI for IMU, UART for microcontroller, Wi-Fi + BLE for host-phone comms.

C) Software Architecture (on-robot)

• tk_core (C) — low-level drivers, frame acquisition, actuator safe controllers.
• cortex (Rust/C) — reasoning, memory manager, safety manager.
• vision_pipeline (C++/Rust) — preprocessing, depth map (MiDaS), detection (YOLOv5nu).
• audio_pipeline (C/Rust) — wakeword (Porcupine), VAD (Silero), ASR (whisper.cpp).
• ffi — stable C API to expose high-level commands to companion apps or plugin modules.

D) Integration & CI

• Unit tests, integration tests (hardware-in-loop), and end-to-end validation (simulated obstacles).
• Pre-deployment checklist: sensor calibration, thermal test, power failover, and safe stop latency measured.

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🔁 Mobile & Desktop: TrackieStudio (Trackie companion app)

• TrackieStudio is the companion app and configuration tool for Trackie / Trackway devices. It provides firmware updates, model deployment, remote logs, and live telemetry.

• Mobile editions:

  • iOS — Metal-accelerated model inference (on-device CoreML/Metal for models that can be converted).
  • Android — NDK libraries compiled for armeabi-v7a / arm64-v8a; uses Vulkan/CUDA (where supported) for acceleration.

• Desktop editions provide a simulator, model analysis, and developer tools.

Download landing pages:

• https://trackway.org/downloads/trackiestudio-ios — iOS App Store / TestFlight link
• https://trackway.org/downloads/trackiestudio-android — Play Store link
• Releases & installers mirrored at https://github.com/phkaiser13/trackway/releases

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⚠️ Security, Privacy & Data Handling (mandatory)

• All raw audio/video processing must, by default, occur locally and be securely wiped from RAM/temporary storage after inference.
• Models and telemetry must be opt-in for cloud uploads; use secure TLS endpoints and token-based auth.
• Keys and secrets: use OS keyrings (libsecret/keyring) or encrypted on-disk vaults; never store plaintext tokens in config files.
• Follow least-privilege: create a dedicated trackie system user, restrict file permissions on /opt/trackie and model directories.

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🧪 Validation & Certification (safety-critical)

• Provide test harnesses that measure detection latency, false-positive/negative rates, and worst-case CPU/GPU load.
• For devices intended for industrial use, document safety validation: EMC, electrical safety, and fail-safe behaviors.
• Provide an accessible set of test protocols for field technicians (calibration, sensor replacement, software rollback).

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⚙️ Example Commands (developer quickstart)

Clone + build example:

git clone https://github.com/phkaiser13/trackway.git
cd trackway
cmake -S . -B build -DTRACKIE_ENABLE_CUDA=OFF
cmake --build build -- -j$(nproc)
cargo build --workspace --release
# install to /opt/trackie (requires sudo)
sudo mkdir -p /opt/trackie && sudo cp -r build/bin /opt/trackie/

Run locally:

/opt/trackie/bin/trackie-core --config /etc/trackie/config.yaml

Create Docker image (developer template):

docker build -t trackie/core:latest -f docker/Dockerfile .
docker run --device /dev/video0 --cap-add=SYS_NICE -v /opt/trackie/models:/opt/trackie/models trackie/core:latest

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🔗 Related assets & resources

• Project repository: https://github.com/phkaiser13/trackway
• Model assets Training & data: https://github.com/phkaiser13/TrackieAssets (ONNX, GGUF bundles)
• Official landing & downloads: https://trackway.org

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🎯 Product Notes (form-factors & marketing)

• Trackway Hat — premium product: integrated compute, highest autonomy, supports full offline LLM runs.
• SpotWay Hat — accessible product: sensor hub with lightweight pre-processing; smartphone runs LLM and heavy models.
• Trackway Glasses — mid-tier: stereo vision and thermal management; better for indoor/outdoor mixed use cases.

Each form factor includes:

• Device registration flow (paired with TrackieStudio)
• OTA update pipeline (secure signed packages)
• Safety & calibration wizard on first boot

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🤝 Social Impact & Donations

We believe in building solutions that serve communities. Consider supporting:

• APAE — Association of Parents and Friends of the Exceptional
• Fundação Dorina Nowill — Support for visually impaired individuals
• UNICEF

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🧾 License & Contribution

See LICENSE, CONTRIBUTING.md, SECURITY.md in the repository root for legal, contribution, and vulnerability disclosure policies.

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❤️ Closing — where to go next

• Visit the official site and downloads: https://trackway.org
• Clone repo and view hardware blueprints & code: https://github.com/phkaiser13/trackway