Robots on a slippery slope

What is the best way for a robot to tackle challenging surfaces? Prediction? Sensor data? AI processing? Parallell computing?

Options in this project are virtually endless, here are some ideas:

  • Create and or use (robotic) test rigs to test how surfaces work with different motors, legs or wheels. 
    • direct driven vs geared motors
    • legs of different shapes
  • Compare platforms (processor, FPGA or SoCs) for computing traction and deciding torque and or gait.
    • In practice: To what degree do we benefit from knowing the surface properties?
      • Path planning vs traction control
    • What is an ideal platform for controlling robots?
      • How should information be shared between various parts of a robot?
      • etc
  • Can we benefit from methods found in the motorcycle and car industry for creating robots that will move on challenging surfaces?

We are currently (2021H) finalizing an open dynamic robot that can and will be used for testing various methods for control of a direct-driven, fast paced robot. Future projects may use this robot as a platform for testing various methods and hardware.

Open Dynamic Robot


Emneord: traction control, robotics, control system, AI, Mechatronics, direct drive, opendynamicrobot, FPGA
Publisert 21. aug. 2020 17:25 - Sist endret 17. aug. 2021 14:41