Adaptive Compliance Control of a Multi-legged Stair-Climbing Robot Based on Proprioceptive Data

Markus Eich, Felix Grimminger, Frank Kirchner

In: Industrial Robot: An International Journal 36 Issue 4 Seiten 331-339 Emerald Group Publishing Limited 2009.


In this work we describe an innovative compliance control architecture for hybrid multi-legged robots. The approach was verified on the hybrid legged-wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA-based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair-climbing capabilities. Contrary to existing approaches, we did not use a pre-defined walking pattern for stair-climbing, but an adaptive approach based only on internal sensor information. In this work we show how this adaptivity results in a versatile controller for hybrid legged-wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. In contrast to many other walking pattern based robots, we use the direct proprioceptive feedback in order to modify the internal control loop, thus adapting the compliance of each leg on-line. The robot is well suited for disaster mitigation as well as for urban search and rescue (USAR) missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust-proof corpus and the the ability to swim are additional features of the robot.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence