Highlights• ML algorithms can classify water and sea ice characteristics in the coastal Arctic.• Spring freshet runoff over sea ice is optically distinct from surface melt ponds.• Time-series analyses of satellite ima...
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Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point...
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The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to ...
The current approach to exploring and monitoring complex underwater ecosystems, such as coral reefs, is to conduct surveys using diver-held or static cameras, or deploying sensor buoys. These approaches often fail to capture the full variation and complexity of interactions between different reef organisms and their habitat. The CUREE platform presented in this paper provides a unique set of capabilities in the form of robot behaviors and perception algorithms to enable scientists to explore different aspects of an ecosystem. Examples of these capabilities include low-altitude visual surveys, soundscape surveys, habitat characterization, and animal following. We demonstrate these capabilities by describing two field deployments on coral reefs in the US Virgin Islands. In the first deployment, we show that CUREE can identify the preferred habitat type of snapping shrimp in a reef through a combination of a visual survey, habitat characterization, and a soundscape survey. In the second deployment, we demonstrate CUREE's ability to follow arbitrary animals by separately following a barracuda and stingray for several minutes each in midwater and benthic environments, respectively.
A. procedure is proposed for the numerical evaluation of the Hankel (Fourier-Bessel) transform of any integer order using die FFT algorithm. The basis for the procedure is the “projection-slice” theorem associated w...
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Describes the development and verification of a six degree of freedom, non-linear simulation model for the REMUS AUV, the first such model for this platform. In this model, the external forces and moments resulting fr...
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Describes the development and verification of a six degree of freedom, non-linear simulation model for the REMUS AUV, the first such model for this platform. In this model, the external forces and moments resulting from hydrostatics, hydrodynamic lift and drag, added mass, and the control inputs of the vehicle propeller and fins are all defined in terms of vehicle coefficients. The paper briefly describes the derivation of these coefficients. The equations determining the coefficients, as well as those describing the vehicle rigid-body dynamics, are left in non-linear form to better simulate the inherently non-linear behavior of the vehicle. Simulation of the vehicle motion is achieved through numeric integration of the equations of motion. The simulator output is then verified against vehicle dynamics data collected in experiments performed at sea. The simulator is shown to accurately model the motion of the vehicle. The paper concludes with recommendations for future model validation experiments.
We present an integrated framework for joint estimation and pursuit of dynamic features in the ocean, over large spatial scales and with multiple collaborating vehicles relying on limited communications. Our approach ...
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We present a solution to multi-robot distributed semantic mapping of novel and unfamiliar environments. Most state-of-the-art semantic mapping systems are based on supervised learning algorithms that cannot classify n...
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We present a solution to multi-robot distributed semantic mapping of novel and unfamiliar environments. Most state-of-the-art semantic mapping systems are based on supervised learning algorithms that cannot classify novel observations online. While unsupervised learning algorithms can invent labels for novel observations, approaches to detect when multiple robots have independently developed their own labels for the same new class are prone to erroneous or inconsistent matches. These issues worsen as the number of robots in the system increases and prevent fusing the local maps produced by each robot into a consistent global map, which is crucial for cooperative planning and joint mission summarization. Our proposed solution overcomes these obstacles by having each robot learn an unsupervised semantic scene model online and use a multiway matching algorithm to identify consistent sets of matches between learned semantic labels belonging to different robots. Compared to the state of the art, the proposed solution produces 20-60% higher quality global maps that do not degrade even as many more local maps are fused.
We make two contributions toward integrated monitoring over large spatial scales, with multiple collaborating vehicles. Our focus is dynamic ocean features such as fronts and plumes. To support strong networked-contro...
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ISBN:
(纸本)9781467363563
We make two contributions toward integrated monitoring over large spatial scales, with multiple collaborating vehicles. Our focus is dynamic ocean features such as fronts and plumes. To support strong networked-control designs, we first develop a clean linear time-invariant framework for tracking features, that directly couples the global structure of the process to vehicle positioning. To address the packet loss inherent in underwater acoustic communications, we then extend the synthesis technique of Imer et al. [1] to the case where measurements and control commands suffer loss with differing statistics among the multiple channels. Simulations show that the integrated feedback system achieves good performance in front tracking.
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