We present a motion planner for the perceptive rough-terrain locomotion with quadrupedal robots. The planner finds safe footholds along with collision-free swing-leg motions by leveraging an acquired terrain map. To this end, we present a novel pose optimization approach that enables the robot to climb over significant obstacles. We experimentally validate our approach with the quadrupedal robot ANYmal by autonomously traversing obstacles such steps, inclines, and stairs. The locomotion planner re-plans the motion at every step to cope with disturbances and dynamic environments. The robot has no prior knowledge of the scene, and all mapping, state estimation, control, and planning is performed in real-time onboard the robot.

This work has been published as:

Fankhauser, P., Bjelonic, M., Bellicoso, C. D., Miki, T., and Hutter, M. (2018). “Robust Rough-Terrain Locomotion with a Quadrupedal Robot”. In: IEEE International Conference on Robotics and Automation (ICRA).

This work has been conducted as part of ANYmal Research, a community to advance legged robotics.

www.anymal-research.com