AI Vision Robot (Real-Time Edge Detection & Avoidance)
A compact edge-computing mobile robot equipped with computer vision to recognize objects, people, obstacles, and navigate dynamic physical environments.
The Problem
What difficult challenge is this experiment tackling?Traditional robotics relying on simple ultrasonic sensors get trapped in complex environments with reflective surfaces, soft obstacles, and moving people.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Deploying quantized neural vision models (YOLO / MobileNet) on low-power hardware (Raspberry Pi 5 / Jetson Nano) provides real-time 30fps obstacle avoidance.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human Intervention32 frames per second inference on edge hardware with <35ms latency from visual perception to motor steering adjustment.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsEdge processing ensures video stream never leaves the physical robot; zero cloud telemetry required.
Lessons Learned & Observations
Empirical findings from prototyping and testingQuantizing model weights to INT8 cut memory consumption by 70% with negligible loss in detection accuracy.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesIntegrate visual SLAM (Simultaneous Localization and Mapping) for 3D room floorplan generation.