Microspine grippers allow robots to ascend steep rocky slopes and cliff faces, enabling scientific exploration of exposed strata on Earth and other solar system bodies. Historically, the Shape Deposition Manufacturing (SDM) process has been used to fabricate multi-material suspensions for load-sharing among multiple microspines. We instead apply the Hybrid Deposition Manufacturing (HDM) process to microspine fabrication, and we further propose a novel 3D-printed microspine suspension design that can be manufactured via Fused Deposition Manufacturing (FDM) alone, using a single flexible material with an embedded fishhook. We use a model of microspine stiffness that allows designers to compensate for order-of-magnitude changes in material tensile modulus by adjusting geometric parameters of the design. The stiffness model and the FDM microspine design are validated through tensile testing, and mechanical properties of the HDM and FDM designs are compared against a standard SDM microspine design. We demonstrate that the FDM process can produce microspines with equivalent normal and axial stiffness and superior maximum load and fatigue response to SDM microspines, and discuss additional advantages of the FDM process for rapid prototyping and broader accessibility.

Planetary exploration rovers are expensive, weight constrained, and cannot be serviced once deployed. Here, we explore one way to increase their capabilities while avoiding the cost, mass, and complexity leading to these issues. We propose to re-use the large wheel actuators for trenching and other digging operations, which will enable a range of missions such as sampling deeper layers of soil. We present a new, closed-form model of the soil displaced by an angled, spinning wheel to analyze the trenching potential of a driving strategy and inform the control of the wheel. The model is demonstrated with single wheel experiments under different driving conditions. The model suggests: that a deep trench does not require large tractive efforts; that the shape of the trench can be controlled; and that a rear wheel has a lower risk of entrapment when trenching than a front wheel. Ultimately this model could be used in a nonprehensile manipulation planning or learning algorithm to enable autonomous trenching.