Whole-body Control (WBC) has emerged as an important framework in locomotion control for legged robots. However, most of WBC frameworks fail to generalize beyond rigid terrains. Legged locomotion over soft terrain is difficult due to the presence of unmodeled contact dynamics that WBCs do not account for. This introduces uncertainty in locomotion and affects the stability and performance of the system. In this paper, we propose a novel soft terrain adaptation algorithm called STANCE: Soft Terrain Adaptation and Compliance Estimation. STANCE consists of a WBC that exploits the knowledge of the terrain to generate an optimal solution that is contact consistent and an online terrain compliance estimator that provides the WBC with terrain knowledge. We validated STANCE both in simulation and experiment on the Hydraulically actuated Quadruped (HyQ) robot, and we compared it against the state of the art WBC. We demonstrated the capabilities of STANCE with multiple terrains of different compliances, aggressive maneuvers, different forward velocities, and external disturbances. STANCE allowed HyQ to adapt online to terrains with different compliances (rigid and soft) without pre-tuning. HyQ was able to successfully deal with the transition between different terrains and showed the ability to differentiate between compliances under each foot.
To date, the only work on compliance estimation in legged locomotion was the one by Bosworth al @cite_1 . The authors presented two online (in-situ) approaches to estimate the ground properties (stiffness and friction). The results were promising and the approaches were validated on a quadruped robot while hopping over rigid and soft terrain. However, the estimated stiffness showed a trend, but was not accurate; the lab measurements of the terrain stiffness did not match the in-situ ones. Although the estimation algorithms could be implemented online, the robot had to stop what it was doing to perform the estimation.
@cite_1
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Dynamic behavior of legged robots is strongly affected by ground impedance. Empirical observation of robot hardware is needed because ground impedance and foot-ground interaction is challenging to predict in simulation. This paper presents experimental data of the MIT Super Mini Cheetah robot hopping on hard and soft ground. We show that controllers tuned for each surface perform better for each specific surface type, assessing performance using measurements of 1.) stability of the robot in response to self-disturbances applied by the robot onto itself and 2.) the peak accelerations of the robot that occur during ground impact, which should be minimized to reduce mechanical stress. To aid in controller selection on different ground types, we show that the robot can measure ground stiffness and friction in-situ by measuring its own interaction with the ground. To motivate future work in variable-terrain control and in-situ ground measurement, we show preliminary results of running gaits that transition between hard and soft ground.