Terrain-aware semantic mapping for cooperative subterranean exploration

Navigation over torturous terrain such as those in natural subterranean environments presents a significant challenge to field robots. The diversity of hazards, from large boulders to muddy or even partially submerged Earth, eludes complete definition. The challenge is amplified if the presence and nature of these hazards must be shared among multiple agents that are operating in the same space. Furthermore, highly efficient mapping and robust navigation solutions are absolutely critical to operations such as semi-autonomous search and rescue. We propose an efficient and modular framework for semantic grid mapping of subterranean environments. Our approach encodes occupancy and traversability information, as well as the presence of stairways, into a grid map that is distributed amongst a robot fleet despite bandwidth constraints. We demonstrate that the mapping method enables safe and enduring exploration of subterranean environments. The performance of the system is showcased in high-fidelity simulations, physical experiments, and Team MARBLE's entry in the DARPA Subterranean Challenge which received third place.

A Gecko-Inspired Robot with a Flexible Spine Driven by Shape Memory Alloy Springs

The majority of sprawling-posture quadrupedal vertebrates, such as geckos and lizards, adopt a cyclical lateral swing pattern of their trunk that is coordinated with limb movements to provide extraordinary flexibility and mobility. Inspired by the gecko's locomotory gait and posture, a gecko-like robot with a flexible spine driven by shape memory alloy (SMA) springs was proposed in this work. The static parameters of the SMA spring were experimentally measured, and the flexible spine driven by SMA springs can be deflected bidirectionally. A kinematic model of the spine mechanism was established, and the mathematical relationship between the thermodynamic behavior of the SMA springs and spinal deflection was systematically analyzed. When a gecko trots with a lateral swing pattern of its trunk, the body and the spine show a standing wave shape and a single-peak C-type curve, respectively. The lateral spine deflection and trotting gait were combined in a collaborative model of a flexible spine and limbs to describe in detail the specific relationships between leg joint variables and spine deflection angle. Planar motion tests of a prototype robot were also conducted by using four high-speed cameras to record the trajectory of each point of the body, which verified the proposed model. From the acquired results, it was demonstrated that, compared with a rigid body, a robot with a flexible spine has a longer stride length, higher speed, and a greatly reduced turning radius.

Uniform conductivity in stretchable silicones via multiphase inclusions

Many soft robotic components require highly stretchable, electrically conductive materials for proper operation. Often these conductive materials are used as sensors or as heaters for thermally responsive materials. However, there is a scarcity of stretchable materials that can withstand the high strains typically experienced by soft robots, while maintaining the electrical properties necessary for Joule heating (e.g., uniform conductivity). In this work, we present a silicone composite containing both liquid and solid inclusions that can maintain a uniform conductivity while experiencing 200% linear strains. This composite can be cast in thin sheets enabling it to be wrapped around thermally responsive soft materials that increase their volume or stretchability when heated. We show how this material opens up possibilities for electrically controllable shape changing soft robotic actuators, as well as all-silicone actuation systems powered only by electrical stimulus. Additionally, we show that this stretchable composite can be used as an electrode material in other applications, including a strain sensor with a linear response up to 200% strain and near-zero signal noise.

Attraction, Dynamics, and Phase Transitions in Fire Ant Tower-Building

Many insect species, and even some vertebrates, assemble their bodies to form multi-functional materials that combine sensing, computation, and actuation. The tower-building behavior of red imported fire ants, Solenopsis invicta, presents a key example of this phenomenon of collective construction. While biological studies of collective construction focus on behavioral assays to measure the dynamics of formation and studies of swarm robotics focus on developing hardware that can assemble and interact, algorithms for designing such collective aggregations have been mostly overlooked. We address this gap by formulating an agent-based model for collective tower-building with a set of behavioral rules that incorporate local sensing of neighboring agents. We find that an attractive force makes tower building possible. Next, we explore the trade-offs between attraction and random motion to characterize the dynamics and phase transition of the tower building process. Lastly, we provide an optimization tool that may be used to design towers of specific shapes, mechanical loads, and dynamical properties, such as mechanical stability and mobility of the center of mass.