For a self-balancing bipedal robot, raw gyroscope and accelerometer data is
virtually useless on its own due to high-frequency noise and gyroscopic drift.
You need robust sensor fusion (combining the data to determine absolute
orientation).
Here are the two most reliable IMU paths for this architecture:
 * Bosch BNO085 (or BNO055): The Coprocessor Approach. This is the gold
standard for DIY builds. It contains a dedicated onboard 32-bit ARM Cortex-M0+
microcontroller that runs complex sensor fusion algorithms internally. Instead
of sending noisy raw data, it directly outputs highly stable, drift-free
quaternions and gravity vectors at 100Hz+. This entirely offloads the math
from your control system.
 * TDK InvenSense ICM-42688-P: The Raw Performance Approach. If you prefer to
write your own highly optimized C-based fusion engine, this is the current
favorite in high-end FPV drones and agile robotics. It offers extremely low
noise and ultra-low latency (up to 8kHz update rates), but you must run a
Madgwick or Kalman filter on a local microcontroller to make sense of the
data.
The Distributed "Reflex" Architecture
Your intuition about parallel micro boards is spot on. In advanced robotics,
routing every minor balancing adjustment through the main high-level operating
system introduces catastrophic latency.
You can architect the robot's hardware exactly like a distributed microservice
mesh, utilizing local "spinal reflex" loops.
Here is how that distributed IPC topology works in practice:
1. The High-Level Brain (Android/Browser)
The Android device operates strictly at the macro level (e.g., 3060Hz). It
handles vision processing, intent, and path planning. Instead of commanding
specific motor angles, it publishes target state vectors (e.g., "Move Center
of Mass forward at 0.5 m/s") to the hardware mesh.
2. The Deterministic Bus (CAN FD or RS485)
Instead of a simple serial connection, the internal hardware communicates over
a deterministic bus like CAN FD. This operates as a lightweight pub/sub
network. The central torso IMU broadcasts the robot's global orientation to
the bus, allowing any node on the network to read the state simultaneously
without polling.
3. The Edge Reflex Nodes (RP2040 or ESP32)
You place dedicated micro boards at key junctions (e.g., one controller for
the left leg, one for the right). These edge nodes subscribe to the Android
brain's high-level intent and the central IMU's data, but they run their own
isolated, high-speed PID control loops at 1000Hz+.
The result: If the robot steps on an uneven surface, the local leg controller
detects the shift in the IMU data and immediately adjusts the localized ankle
and knee angles to maintain the Center of Pressure. It corrects the balance in
millisecondslong before the Android device even processes the frame showing
the obstacle. The Android brain simply receives an asynchronous update that
the leg has absorbed a shock, keeping the main thread entirely lock-free and
focused on higher-level execution.