Sandy environments present challenges for robotic space rovers and systems due to reduced traction, limiting mobility and tugging force. This paper presents an anchoring method that utilizes a winching system to create a sand mound in front of a mobile agent dragged through the media. The proposed controller is designed to consistently achieve realtime capture of close-to-maximal lateral sand mound resistive force, even when applied to varied uneven terrains, like holes or waves. Notably, tugging is non-reversible, so suitable peaks should be captured before breakdown and without necessarily knowing the global optimum a priori. The controller logic tracks both tugging force and agent pitch gradients to detect terrain conditions and peak force trends. Results show that the controller captures an average 92 % of the maximum forces, within the previously winched workspace tested, across three different granular media with four varying structured terrain features. The controller achieves higher resistive force peaks on terrains with geometric features, as opposed to flat sand. We conclude that sand mounding through tugging is a viable means to generate robotic resistive forces for unknown sandy terrains, a simple yet effective anchoring mechanism.

MOTIVATION: Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample. RESULTS: To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.