Quadrupedal robots have great potential for deployment in challenging environments. However, one of the most significant challenges these robots face is maintaining stability on slippery surfaces due to inaccurate friction estimation. This thesis investigates the role of accurate friction estimation in improving locomotion and reducing slip in quadrupedal robots. A Model Predictive Controller (MPC) with a friction-aware constraint update is proposed and evaluated in a simulation environment using a physics-based simulation with Open Dynamics Engine (ODE).

The experimental results demonstrate that incorporating real-time friction measurement and constraint in the MPC framework significantly reduces slip occurrences, decreases energy consumption, and improves overall locomotion stability. Statistical hypothesis tests, including paired t-tests and Bonferroni corrections, confirm the significance of these improvements. The findings suggest that integrating real-time friction estimation into quadrupedal robot controllers can enhance their robustness and reduce the need for explicit slip recovery strategies. Future work should focus on extending this approach to real-world scenarios, incorporating actual friction sensors, and testing in diverse terrains.