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.

This work presents a novel spherical tensegrity robot, T12-R, which is designed and prototyped based on a twelve-rod tensegrity structure that resembles a rhombicuboctahedron. The geometry of T12-R allows for fast rolling and a detailed description of the robot design is provided. A simulation study of T12-R shows that the robot is capable of performing static locomotion. Control strategies for achieving dynamic locomotion in hardware are discussed.

Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control strategies for ball-shaped tensegrity robots have successfully enabled punctuated rolling, hill-climbing and obstacle climbing. These gains have been made possible through the use of machine learning and physics simulations that allow controls to be “learned” instead of being engineered in a top-down fashion. While effective in simulation, these emergent methods unfortunately give little insight into how to generalize the learned control strategies and evaluate their robustness. These robustness issues are especially important when applied to physical robots as there exists errors with respect to the simulation, which may prevent the physical robot from actually rolling. This paper describes how the robustness can be addressed in three ways: 1) We present a dynamic relaxation technique that describes the shape of a tensegrity structure given the forces on its cables; 2) We then show how control of a tensegrity robot “ball” for locomotion can be decomposed into finding its shape and then determining the position of the center of mass relative to the supporting polygon for this new shape; 3) Using a multi-step Monte Carlo based learning algorithm, we determine the structural geometry that pushes the center of mass out of the supporting polygon to provide the most robust basic mobility step that can lead to rolling. Combined, these elements will give greater insight into the control process, provide an alternative to the existing physics simulations and offer a greater degree of robustness to bridge the gap between simulation and hardware.