Testing Load Cells & Foot Sensors for Quadruped Robot Legs

Added:

Open Dog Journey
Test Legs
Rigidity Issue
Sensor Setup
New Foot Design
Leg Wiring
Data Transfer
Future Plans

Open Dog Journey

0:00
Playing Section
  • 1

    Reflects on building open-source quadruped robots and lessons learned.

  • 2

    Considers challenges of balancing with rigid legs and motors.

  • 3

    Introduces the robot's technical components and locomotion logic.

Fundamentals of strain gauges and Wheatstone bridge circuits, including signal amplification (such as the HX711) used to measure physical force.
Basic microcontroller programming (C/C++) and sensor interfacing to read analog and digital inputs in real-time.
Introductory robotics concepts, specifically legged locomotion kinematics, joint torque distribution, and ground reaction forces.
Basic principles of closed-loop feedback control systems, where sensor data is used to dynamically adjust motor behavior.
Implementation of dynamic gait planning algorithms (e.g., trotting or bounding) that use real-time contact detection to trigger phase transitions.
Advanced state estimation and sensor fusion, combining foot sensor data with Inertial Measurement Unit (IMU) data using Kalman Filters.
Model Predictive Control (MPC) and Whole-Body Control (WBC) frameworks to achieve active balancing and external disturbance rejection.
Design and material selection for compliant robotic feet to absorb impact forces and improve traction across diverse terrains.
94.3K views3.3Klikes16:32@jamesbrutonOriginal Release: 2019-12-17

This video demonstrates how to integrate load cells with mechanical switches for robot foot sensing, enabling a rigid robot (openDog) to detect ground contact and measure pressure on each foot. The system uses a Sparkfun NAU7802 I2C load cell amplifier connected to a Teensy microcontroller, with a custom foot design containing both a load cell and a physical switch to confirm ground contact before measuring force. The project addresses the challenge of making rigid robot legs more dynamic by simulating spring behavior through real-time load feedback, which helps the robot maintain balance and respond appropriately to uneven terrain or unexpected disturbances.