An on-site mobile robotic platform autonomously constructed an open dome with complex curvature and variable wall thickness.

This paper proposes a bandwidth tunable technique for real-time probabilistic scene modeling and mapping to enable co-robotic exploration in communication constrained environments such as the deep sea. The parameters of the system enable the user to characterize the scene complexity represented by the map, which in turn determines the bandwidth requirements. The approach is demonstrated using an underwater robot that learns an unsupervised scene model of the environment and then uses this scene model to communicate the spatial distribution of various high-level semantic scene constructs to a human operator. Preliminary experiments in an artificially constructed tank environment as well as simulated missions over a 10m×10m coral reef using real data show the tunability of the maps to different bandwidth constraints and science interests. To our knowledge this is the first paper to quantify how the free parameters of the unsupervised scene model impact both the scientific utility of and bandwidth required to communicate the resulting scene model. I. INTRODUCTIONThe challenges of exploration in remote and extreme environments such as the deep seas [1], [2], cave systems [3], outer space [4] and during or after a natural disaster [5], [6] have much in common. It is expensive and inherently dangerous for humans to explore such locations directly; hence, the use of mobile robots is desirable. However, if communication bottlenecks exist in the environment, prohibiting live streaming of video or other sensor data, then direct control of the robots is generally not possible. This paper describes a novel approach to co-robotic exploration in communication starved environments, and presents a system implementation of an under-sea exploration robot for corobotic exploration of marine environments.Although physically controlling a robot can be achieved over relatively low bandwidth, it is difficult to transmit the scene information necessary for an operator or scientist to make high level navigational decisions. We propose a spatially correlated Chinese Restaurant Process (CRP)-based [7] scene understanding model, that can be tuned to operate with

Construction is a labor-intensive industry that relies on dependent processes being completed in series. Redesigning fabrication processes to allow for parallelization and replacing workers with mobile multi-robot construction systems are strategies to expedite construction, but they typically require extensive supporting infrastructure and strictly constrain fabricable designs. Here we present Fiberbots, a platform that represents a step towards autonomous, collaborative robotic fabrication. This system comprises a team of identical robots that work in parallel to build different parts of the same structure up to tens of times larger than themselves from raw, homogeneous materials. By winding fiber and resin around themselves, each robot creates an independent composite tube that it can climb and extend. The robots' trajectories are controlled to construct intertwining tubes that result in a computationally-derived woven architecture. This end-to-end system is scalable, allowing additional robots to join the system without substantially increasing design complexity or fabrication time. As an initial demonstration of system viability, a structural case study was performed. The robots constructed a 4.5 meter tall tubular composite structure in an outdoor environment in under 12 hours. While further improvements must be made before this can be used in industry or in truly cooperative settings, this is the largest known demonstration of on-site construction with multiple, homogeneous mobile robots. This work offers a scalable step forward in autonomous, site-specific fabrication systems.

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Swarm-based fabrication of interwoven composite tubes via a fully autonomous, cooperative system can help create architectural-scale structures in effective and efficient ways, including in remote environments.