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In this paper we present a potential energy map based approach that provides a framework for the design and control of a robotic grasper. Unlike other potential energy map approaches, our framework is able to consider friction for a more realistic perspective on grasper performance. Our analysis establishes the importance of including variable geometry in a grasper design, namely with regards to palm width, link lengths, and transmission ratio. We demonstrate the use of this method specifically for a two-phalanx tendon-pulley underactuated grasper, and show how various design parameters - palm width, link lengths, and transmission ratios - impact the grasping and manipulation performance of a specific design across a range of object sizes and friction coefficients. Optimal grasping designs have palms that scale with object size, and transmission ratios that scale with the coefficient of friction. Using a custom manipulation metric we compared a grasper that only dynamically varied its geometry to a grasper with a variable palm and distinct actuation commands. The analysis revealed the advantage of the compliant reconfiguration ability intrinsic to underactuated mechanisms; by varying only the geometry of the grasper, manipulation of a wide range of objects could be performed., Comment: 7 pages, 7 figures

The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and reorienting of grasped objects. In this work, we present a novel non-anthropomorphic robot grasper with the ability to manipulate objects by means of active surfaces at the fingertips. Active surfaces are achieved by spherical rolling fingertips with two degrees of freedom (DoF) -- a pivoting motion for surface reorientation -- and a continuous rolling motion for moving the object. A further DoF is in the base of each finger, allowing the fingers to grasp objects over a range of size and shapes. Instantaneous kinematics was derived and objects were successfully manipulated both with a custom handcrafted control scheme as well as one learned through imitation learning, in simulation and experimentally on the hardware., Comment: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 25-29, 2020, Las Vegas, NV, USA (Virtual)

We propose a K-Means based prediction system, nicknamed SERVANT (Scene Recognition Through Visual and Acoustic Cues), that is capable of recognizing environmental scenes through analysis of ambient sound and color cues. The concept and implementation originated within the Learning branch of the Intelligent Wearable Robotics Project (also known as the Third Arm project) at the Stanford Artificial Intelligence Lab-Toyota Center (SAIL-TC). The Third Arm Project focuses on the development and conceptualization of a robotic arm that can aid users in a whole array of situations: i.e. carrying a cup of coffee, holding a flashlight. Servant uses a K-Means fit-and-predict architecture to classify environmental scenes, such as that of a coffee shop or a basketball gym, using visual and auditory cues. Following such classification, Servant can recommend contextual actions based on prior training., Comment: 4 pages, 7 figures, work done under Intelligent Wearable Robotics Project at Stanford Artificial Intelligence Lab

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks., Comment: IEEE International Conference on Robotics and Automation (ICRA), 2017

We evaluate potential tsunami hazards for several communities in lower Cook Inlet, including Anchor Point, Kenai, Ninilchik, and Tyonek, by numerically modeling the extent of inundation from tsunami waves generated by hypothetical earthquakes. We define an updated suite of earthquakes—including Tohoku-style megathrust ruptures and other sources in the eastern part of the Alaska–Aleutian megathrust—to calculate vertical seafloor displacements and model resulting tsunami dynamics. A hypothetical earthquake spanning from Kodiak Island to Prince William Sound with maximum slip distributed between depths of 5 and 22 km (3.1 and 13.7 mi) results in “worst case” tsunami inundation for all communities. If the tsunami arrives at high tide, the maximum predicted overland flow depths in the communities can reach up to 10 m (32.8 ft), and the currents could be as strong as 12 m/sec (23.4 knots). Dangerous wave activity is expected to last for more than 24 hours. Results presented here are intended to provide guidance to local emergency management agencies for tsunami inundation assessment, evacuation planning, and public education to mitigate future tsunami damage. [ABSTRACT FROM AUTHOR]

The Front End Driver, FED, is a 9U 400mm VME64x card designed for reading out the Compact Muon Solenoid, CMS, silicon tracker signals transmitted by the APV25 analogue pipeline Application Specific Integrated Circuits. The FED receives the signals via 96 optical fibers at a total input rate of 3.4 GB/sec. The signals are digitized and processed by applying algorithms for pedestal and common mode noise subtraction. Algorithms that search for clusters of hits are used to further reduce the input rate. Only the cluster data along with trigger information of the event are transmitted to the CMS data acquisition system using the S-LINK64 protocol at a maximum rate of 400 MB/sec. All data processing algorithms on the FED are executed in large on-board Field Programmable Gate Arrays. Results on the design, performance, testing and quality control of the FED are presented and discussed.

This document contains lidar-derived elevation data for Deadhorse, Northern Alaska, collected in July 2021. The data includes a classified point cloud, digital surface model, digital terrain model, and intensity model. The survey was conducted using aerial lidar technology and ground control data. The document provides information on the mission plan, weather conditions, data processing, and accuracy assessments. The data is available for download on the Alaska Division of Geological & Geophysical Surveys website. The survey area is located on the traditional homelands of the Iñupiat peoples. The document includes coordinates and elevation data for various ground control points and non-vegetated check points, as well as average elevation differences, minimum and maximum elevation differences, average magnitude error, root mean square error, and standard deviation. There is also a separate section for vegetated checkpoints. This data can be used for research or analysis purposes. [Extracted from the article]

The Alaska Division of Geological & Geophysical Surveys recently conducted an aerial lidar survey in Deadhorse, Northern Alaska, to collect snow surface elevation data. The survey utilized a Riegl VUX1-LR laser scanner and a helicopter, and the resulting data, including classified points, DSM and DTM, intensity image, and metadata, are available for download on the DGGS website. The accuracy of the data was assessed through ground control points and checkpoints, with a vertical accuracy of 5.7 cm. The document acknowledges the traditional homelands of the Iñupiat peoples and provides references and appendices for further information. [Extracted from the article]

This article provides an overview of the landslides that occurred in Wrangell, Alaska in November 2023. The landslides resulted in the destruction of homes and loss of life. Geologists conducted field visits and collected aerial lidar data to assess the extent and volume of the landslides. The article also discusses the weather conditions leading up to the landslides, including heavy rainfall and wind gusts. The text highlights the mapping of landslides in the Tongass National Forest and the use of LIDAR datasets for remote sensing. Additionally, the document describes the collection of lidar data for Wrangell Island and the identification of several landslides in the area. The investigation also found no recent mass movement near water reservoirs. [Extracted from the article]

We evaluate potential tsunami hazards for upper Cook Inlet, including Anchorage, Girdwood, and Hope, Alaska, by numerically modeling the extent of inundation from tsunami waves generated by hypothetical earthquakes. We define an updated suite of earthquakes--including Tohoku-style megathrust ruptures and other sources in the eastern part of the Alaska--Aleutian megathrust--to calculate vertical seafloor displacements and model resulting tsunami dynamics. We also perform a numerical analysis of tsunami--tide interactions for the 1964 Great Alaska Earthquake in upper Cook Inlet, an area with some of the most extreme tidal ranges in the U.S. This analysis reveals why the 1964 earthquakegenerated tsunami largely went undetected in upper Cook Inlet and highlights that the tidal stage at the time of future great earthquakes determines the severity of tsunami impacts around Anchorage. A rare combination of earthquake magnitude, location, and timing must be satisfied for tsunami wave energy to reach upper Cook Inlet coincident with a natural high tide. A hypothetical earthquake with maximum slip distributed between depths of 17 and 32 km (10.6 and 19.9 mi) results in "worst case" tsunami inundation for Anchorage. If the tsunami arrives to the Anchorage area at high tide, the maximum predicted overland flow depths in the community can reach up to 10 m (32.8 ft), and the currents could be as strong as 4 m/sec (7.8 knots). Dangerous wave activity is expected to last for more than 24 hours. Results presented here are intended to provide guidance to local emergency management agencies for tsunami inundation assessment, evacuation planning, and public education to mitigate future tsunami damage. [ABSTRACT FROM AUTHOR]