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Soft robots actuate themselves and their world through induced pressure and strain, and can often sense these quantities as well. We hypothesize that coordination in a tightly coupled collective of soft robots can be achieved with purely proprioceptive sensing and no direct communication. In this paper, we target a platform of soft pneumatic modules capable of sensing strain on their perimeter, with the goal of using only the robots' own soft actuators and sensors as a medium for distributed coordination. However, methods for modelling, sensing, and controlling strain in such soft robot collectives are not well understood. To address this challenge, we introduce and validate a computationally efficient spring-based model for two-dimensional sheets of soft pneumatic robots. We then translate a classical consensus algorithm to use only proprioceptive data, test in simulation, and show that due to the physical coupling between robots we can achieve consensus-like coordination. We discuss the unique challenges of strain sensors and next steps to bringing these findings to hardware. These findings have promising potential for smart materials and large-scale collectives, because they omit the need for additional communication infrastructure to support coordination. 

Large-scale construction projects can benefit from having a team of heterogeneous building robots operating autonomously and cooperatively on unstructured environments. In this work, we propose a flexible system architecture, MARSala, that allows teams of distributed mobile robots to construct motion support structures in large and unstructured environments using purely local interactions. The paper primarily focuses on the deliberative layer of the architecture which provides a means for formulating a construction project as a motion support structure construction problem. We implemented the architecture in simulation and demonstrated the benefits of such a formulation in two different construction scenarios operating in large unstructured environments. 

Honeybees are renowned for their perfectly hexagonal honeycomb, hailed as the pinnacle of biological architecture for its ability to maximize storage area while minimizing building material. However, in natural nests, workers must regularly transition between different cell sizes, merge inconsistent combs, and optimize construction in constrained geometries. These spatial obstacles pose challenges to workers building perfect hexagons, but it is unknown to what extent workers act as architects versus simple automatons during these irregular building scenarios. Using automated image analysis to extract the irregularities in natural comb building, we show that some building configurations are more difficult for the bees than others, and that workers overcome these challenges using a combination of building techniques, such as: intermediate-sized cells, regular motifs of irregular shapes, and gradual modifications of cell tilt. Remarkably, by anticipating these building challenges, workers achieve high-quality merges using limited local sensing, on par with analytical models that require global optimization. Unlike automatons building perfectly replicated hexagons, these building irregularities showcase the active role that workers take in shaping their nest and the true architectural abilities of honeybees.

 

The ability to build structures with autonomous robots using only found, minimally processed stones would be immensely useful, especially in remote areas. Assembly planning for dry-stacked structures, however, is difficult since both the state and action spaces are continuous, and stability is strongly affected by complex friction and contact constraints. We propose a planning algorithm for such assemblies that uses a physics simulator to find a small set of feasible poses and then evaluates them using a hierarchical filter. We carefully designed the heuristics for the filters to match our goal of building stable, free-standing walls. These plans are then executed open-loop with a robotic arm equipped with a wrist RGB-D camera. Experimental results show that the proposed planning algorithm can significantly improve the state of the art in robotic dry stacking. 

Multiscale Granular Stacking (MSGS) is a technology for assembling planetary-surface infrastructure from unprocessed regolith. The unprocessed grains serve directly as additive manufacturing feedstock in a process that exploits their natural variation in size and shape. With precise, single-grain scanning, computation, and packing, MSGS minimizes and potentially eliminates the need for adhesives, fluids, and other binders, saving the associated mass and energy. Preliminary calculations suggest that MSGS requires less mass transport and energy for construction than traditional terrestrial building methods, drastically reducing the reliance on earth resources for sustaining a deep-space human presence and long-term exploration goals. Constructing a desired structure may require stacking millions of grains which demands extensive computation. Packing solutions with many objects exist in the literature, e.g. some versions of the knapsack problem. However, micro- to macro-scale particle dry stacking itself has never been investigated, let alone in the context of space additive manufacturing. Modeling these fabrication process dynamics as a discrete-step linear system allows for tuning of parameters such as build speed, surface finish, and contour smoothing while providing the opportunity to leverage controls theory for determining system convergence, steady-state error, and overshoot of desired build height. This paper details attempts to bring multivariable control theory to bear on additive manufacturing by using feedback on overall build geometry, a technique proven to yield more accurate results than using feedback at the process level in traditional additive manufacturing. 

In the literature we can find many kinds of modular robot that can build a wide variety of structures. In general, finding an assembly order to reach the final configuration, while respecting the insertion constraints of each kind of modular robot is a difficult process that requires system-specific tuning. In this article, we introduce a generic assembly planner by constrained disassembly (GAPCoD) which works with all kinds of modular robots. It outputs a directed acyclic graph where vertices are modules needing to be placed before his child nodes. This graph is obtained through the disassembly of the desired structure submitted to user chosen constraints. We detail the compiler as well as the way to choose constraints and their influence on performance. The robots embed simple path planning algorithm to reach the destination and act as decentralized agents. Examples are provided to show the possibilities that the compiler offers with two very different robot systems and constraints. 

The ability to autonomously modify their environment dramatically increases the capability of robots to operate in unstructured environments. We develop a specialized construction algorithm and robotic system that can autonomously build motion support structures with previously unseen objects. The approach is based on our prior work on adaptive ramp building algorithms, but it eliminates the assumption of having specialized building materials that simplify manipulation and planning for stability. Utilizing irregularly shaped stones makes the problem significantly more challenging since the outcome of individual placements is sensitive to details of contact geometry and friction, which are difficult to observe. To reuse the same high-level algorithm, we develop a new physics-based planner that explicitly considers the uncertainty produced by incomplete in-situ sensing and imprecision during pickup and placement. We demonstrate the approach on a robotic system that uses a newly developed gripper to reliably pick up stones with minimal additional sensors or complex grasp planning. The resulting system can build structures with more than 70 stones, which in turn provide traversable paths to previously inaccessible locations.