Soft wet grounds such as mud, sand, or forest soils, are difficult to navigate because it is hard to predict the response of the yielding ground and energy lost in deformation. In this article, we address the control of quadruped robots' static gait in deep mud. We present and compare six controller versions with increasing complexity that use a combination of a creeping gait, a foot-substrate interaction detection, a model-based center of mass positioning, and a leg speed monitoring, along with their experimental validation in a tank filled with mud, and demonstrations in natural environments. We implement and test the controllers on a Go1 quadruped robot and also compare the performance to the commercially available dynamic gait controller of Go1. While the commercially available controller was only sporadically able to traverse in 12 cm deep mud with a 0.35 water/solid matter ratio for a short time, all proposed controllers successfully traversed the test ground while using up to 4.42 times less energy. The results of this article can be used to deploy quadruped robots on soft wet grounds, so far inaccessible to legged robots.

There have been many investigations regarding water-ice depositions on the lunar surface and it is always been challenging. The previous studies were based on the circular polarization ratio (CPR). However, the CPR has proved to be inefficient in making distinctive classification of smooth (water-ice) and rough surface. Therefore, instead of using single polarimetric parameter CPR, it is required to analyze the CPR>1, along with other significant physical and electrical properties for better textural classification. In this paper, we have established the relationship between icy region and rough region based on physical property that is surface roughness measured with the help of fractal dimension method ('D') and electrical properties like real part of dielectric constant (epsilon'), imaginary part of dielectric constant (epsilon''), real (n) and imaginary (k) part of refractive index, skin depth (d) and reflectivity (R). The whole investigation indicates that the textural classification of the lunar surface with the help of physical and electrical properties gives superior results as compared to the single polarimetric parameter CPR.