检索结果-内蒙古大学图书馆

Active reward learning for co-robotic vision based exploration in bandwidth limited environments

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Jamieson, Stewart How, Jonathan P. Girdhar, Yogesh MIT-WHOI Joint Program in Applied Ocean Science and Engineering United States United States

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very highdimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine design

CUREE: A Curious Underwater Robot for Ecosystem Exploration

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Girdhar, Yogesh McGuire, Nathan Cai, Levi Jamieson, Stewart McCammon, Seth Claus, Brian Soucie, John San E. Todd, Jessica E. Mooney, T. Aran United States MIT-WHOI Joint Program in Oceanography Applied Ocean Science and Engineering United States

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. © 2023, CC BY-NC-ND.

关键词： Ecosystems

Multi-Robot Distributed Semantic Mapping in Unfamiliar Environments through Online Matching of Learned Representations

学校读者我要写书评

暂无评论

Multi-Robot Distributed Semantic Mapping in Unfamiliar Envir...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Kaveh Fathian Kasra Khosoussi Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Massachusetts Institute of Technology (MIT) Woods Hole Oceanographic Institution (WHOI)

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.

关键词： Conferences Semantics Supervised learning Buildings Bandwidth Path planning Classification algorithms

Streaming scene maps for co-robotic exploration in bandwidth limited environments

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Girdhar, Yogesh Cai, Levi Jamieson, Stewart McGuire, Nathan Flaspohler, Genevieve Suman, Stefano Claus, Brian MIT Media Lab MIT-WHOI Joint Program in Applied Ocean Science and Engineering Mechanical Engineering Department Northeastern University

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. Copyright © 2019, The Authors. All rights reserved.

关键词： Bandwidth

Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

学校读者我要写书评

暂无评论

Active Reward Learning for Co-Robotic Vision Based Explorati...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Applied Ocean Science and Engineering Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department The Woods Hole Oceanographic Institution (WHOI)

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very high-dimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.

关键词： Robot sensing systems Semantics Bandwidth Computational modeling Visualization Trajectory

DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods

学校读者我要写书评

暂无评论

DeepSeeColor: Realtime Adaptive Color Correction for Autonom...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Stewart Jamieson Jonathan P. How Yogesh Girdhar MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering Department of Aeronautics and Astronautics Massachusetts Institute of Technology (MIT) Applied Ocean Physics and Engineering Department Woods Hole Oceanographic Institution (WHOI)

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular “Sea-Thru” algorithm [1] while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.

关键词：

Multi-robot distributed semantic mapping in unfamiliar environments through online matching of learned representations

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Jamieson, Stewart Fathian, Kaveh Khosoussi, Kasra How, Jonathan P. Girdhar, Yogesh The MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering United States United States United States

关键词： Industrial robots

A modern approach to the Kelvin-Helmholtz instability on circular vortex sheets

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wilcox, Galen Murray, Ryan MIT WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering Woods Hole MA United States MIT WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering CambridgeMA United States North Carolina State University RaleighNC United States

We represent the outermost shear interface of a eddy by a circular vortex sheet in two dimensions, and provide a new proof of linear instability via the Birkhoff-Rott equation. Like planar vortex sheets, circular sheets are found to be susceptible to a violent shortwave instability known as the Kelvin-Helmholtz instability, with some modifications due to vortex sheet geometry. This result is in agreement with the classical derivation of [35, 49], but our modern approach provides greater clarity. We go on to show that the linear evolution problem can develop a singularity from analytic initial data in a time proportional to the square of the vortex sheet radius. Numerical evidence is presented that suggests this linear instability captures the wave-breaking mechanism observed in nonlinear point vortex simulations. Based on these results, we hypothesize that the Kelvin-Helmholtz instability can contribute to the development of secondary instability for eddies in two-dimensional turbulent flow. Copyright © 2024, The Authors. All rights reserved.

关键词： Turbulent flow

Soft-adaptive turbo equalization: Using soft information in adaptation

学校读者我要写书评

暂无评论

Soft-adaptive turbo equalization: Using soft information in ...

Asilomar Conference on Signals, Systems & Computers

作者： Atulya Yellepeddi James C. Preisig Electrical Engineering/Applied Ocean Science and Engineering WHOI Joint Program MIT USA Applied Ocean Science and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

ISBN: (纸本)9781467350501

Turbo equalizers have been exploited for underwater wireless communication due to their excellent practical performance. As the equalizer is adaptive, it is possible to take advantage of the soft information available from the decoder in the adaptation process. In this paper, we consider a method to do this and compare it with the use of soft information in feedback, which is a method that has been considered in the past. Data from the KAM11 experiment demonstrates that using soft information in adaptation can improve performance over using it only in feedback. We also consider combining these different approaches and how this affects performance.

关键词： Using how this soft information Equalizers Worm gears Turbo CARRIER SYSTEM Feedback Equalization decoders