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+# BETL-001 — BPU Education Transport & Lidar Robot
+
+- **Participant:** IsaacRF
+- **Stage completed:** 2
+- **Repository:** https://github.com/RFisaac/BETL-001
+- **Community post:** https://discord.com/channels/1300358874280230994/1509220927462969575/1514489192158330931
+
+## Summary
+
+BETL-001 ("BPU-Education, Transport, Lidar" — pronounced "Beetle") is my scratch-built,
+bio-inspired expressive lidar mobile robot on the D-Robotics RDK X5: a modular research
+platform with personality baked in from the start. The long-term goal is an educational
+transport robot combining BPU-accelerated vision with lidar-based navigation.
+
+**Stage 1 (Ignite) — brain online.** Starting from first contact with the board, I flashed a
+supported OS image with RDK Studio, brought up Wi-Fi with verified public DNS resolution, and
+established SSH for remote development. For sensor bring-up I went beyond a basic camera
+preview: using the D-Robotics GS130W MIPI stereo camera I ran on-device stereo depth
+(DStereoV2.4, BPU-accelerated) at ~14.6 fps, and ran YOLOv8n object detection on the X5's BPU
+via ROS 2 with annotated output (person detection in my workshop). I documented a real
+integration quirk: the GS130W mounts rotated 90 degrees and only that orientation has valid GDC
+rectification, so the depth preview renders upside-down — cosmetic only, the fix is mount
+design, not software rotation.
+
+**Stage 2 (Build) — full robot architecture.** Stage 2 moves BETL-001 from running demos to a
+complete design: a 4-wheel skid-steer base on hoverboard hub motors driven by four ODESC
+controllers over the X5's native CAN FD bus, with the two required concurrent workloads being
+YOLOv8s detection on the BPU and slam_toolbox 2D SLAM on the CPU — separate compute units.
+Forward depth fuses the D-Robotics stereo camera with a RealSense D435i so their failure modes
+cancel; an optional "poor-man's spherical lidar" payload spins an RPLIDAR A1 on a
+closed-loop-stepper slip-ring stage and encoder-reprojects it to a 3D cloud. The architecture
+rests on a baseline sensor tier as a hard safety contract plus software-declared optional
+payloads, and a single arbitrated path to the motors with soft + hardware e-stop.
+
+## Technical Highlights
+
+**Stage 1 (demonstrated on hardware)**
+- **Platform:** RDK X5 (Ubuntu 22.04.5, ROS 2 Humble / tros.b), flashed via RDK Studio; Wi-Fi + SSH workflow
+- **Sensor:** D-Robotics GS130W MIPI stereo (dual SC132GS global-shutter, MIPI CSI, I2C 0x32/0x33)
+- **Depth:** DStereoV2.4_int16 (IGEV-based) on the BPU — ~14.6 fps at ~95-100% BPU utilization
+- **AI task:** YOLOv8n (640x640 NV12, stock .bin) on the BPU via dnn_node_example, fed from the stereo left eye
+
+**Stage 2 (designed architecture)**
+- **Concurrent workloads:** YOLOv8s INT8 640x640 on the **BPU** (target >=30 FPS) + slam_toolbox 2D SLAM on the **CPU** — separate units
+- **Drivetrain:** 4-wheel skid steer, hoverboard hub motors, 4x Flipsky ODESC V4.2 on the X5's **native CAN FD** bus; AS5600 wheel odometry
+- **Sensing:** D-Robotics stereo + RealSense D435i (active-IR + base IMU), fused; 2x side 8x8 ToF (VL53L7CX); optional spinning-lidar payload via M6 cheese plate
+- **Architecture:** baseline-vs-optional capability tiers (software-declared); single cmd_mux path to motors with soft + hardware e-stop
+- **Microcontrollers:** RP2350/Pico 2 class (stage, odometry, power, load cell, LED) over micro-ROS
+- **On-robot learning (goals):** mass-conditioned energy-efficient locomotion + goal-conditioned Y-horn ball-pushing, trained in Isaac Lab with roller-dyno sim-to-real calibration
+
+## Links & Evidence
+
+- **Stage 2 proposal** (Challenges 1-3, diagrams, BOM, risks): https://github.com/RFisaac/BETL-001/blob/main/PROPOSAL.md
+- **Roadmap** (milestones through July 15): https://github.com/RFisaac/BETL-001/blob/main/ROADMAP.md
+- **Stage 1 screenshots:** https://github.com/RFisaac/BETL-001/tree/main/media/stage1
+- **Stage 1 documentation:** https://github.com/RFisaac/BETL-001#stage-1--ignite-challenge
+
+---
+
+I agree that this showcase document may be used by the Robotics Dream Keeper Challenge organizers as described in the official README (promotion, judging, and archives).
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