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2021 ARC Research Seminar - Fall Series

October 1, Friday, 9:30-10:30am eastern time

Processing Image Data from Unstructured Environments

PI: Dr. Nick Vlahopoulos, Professor of Naval Architecture and Marine Engineering, University of Michigan
Link to project

The US Army Ground Vehicle Systems Center (GVSC) captures a large amount of data from ground vehicle systems during development and experimentation in both manned and autonomous operations. Currently, there is a lack of tools for processing unlabeled data in a semantic manner. This ARC research is developing two new capabilities for increasing low-shot classification accuracy, and for unsupervised soft labeling (i.e., clustering in groups with similar statistical characteristics, but without knowing ahead of time how many such groups exist) images and video frames that are collected but not currently labeled. This is done while integrating a robust unsupervised feature extractor, which is trained, using unlabeled images, collected from Army battlefield-like experiments. The low-shot classification is of interest in reconnaissance operations of autonomous Army vehicles. The autonomous vehicle is expected to collect information about specific targeted objects (relevant classes) and ignore the presence of any other unrelated object (irrelevant classes). The clustering capability allows for the cross-correlation of the image features with other relevant data to identify significant events and plan for the appropriate action through the control algorithms embedded in the vehicle. Various active GVSC robotics projects will benefit, such as the Autonomous Mobility thru Intelligent Collaboration (AMIC) program and Combat Vehicle Robotics (CoVeR) program. The completed research and the planned effort will be presented. The planning includes accessing the DDR (Data Director) through the D12E.net website in order to enable our software to "talk" to the ones that the Army has already available, thus bringing the information we produce to the Army ecosystem.

A Robust Semantic-aware Perception System using Proprioception, Geometry, and Semantics in Unstructured and Unknown Environments

PIs: Dr. Maani Ghaffari, Assistant Professor of Naval Architecture & Marine Engineering, University of Michigan and Dr. Kira Barton Associate Professor of Mechanical Engineering, University of Michigan
Link to project

In this talk, I will present a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. The existence of dynamic objects in the environment causes artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. I will discuss how we leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a continuous (i.e., can be queried at arbitrary resolution) Bayesian model that propagates the scene with flow measurements and infers a 3D semantic occupancy map with better performance than its static counterpart. I will also present some experimental results using publicly available data sets and discuss opportunities in this area for future work.

October 29, Friday, 9:30-10:30am eastern time

Remote connection via Microsoft Teams. Contact William Lim (williamlim@umich.edu) for details.

Ultrasound-based Perception with Convolutional Neural Networks
PI: Drs. Bogdan Popa and Bogdan Epureanu, Mechanical Engineering, University of Michigan
Link to project

Echolocating animals such as bats and dolphins demonstrate that ultrasound can be used effectively to classify and locate objects in complex environments in scenarios where optics-based imaging is ineffective, i.e. in fog, rain, snow. But how to replicate the performance of biosonar in artificial autonomous navigation systems is still an open question. In this talk I will show that convolutional neural networks are excellent at classifying and locating objects from single point echo measurements without time-consuming scanning of the object surface with very narrow beams. A full-wave 3D simulation framework will be presented which computes fast and accurately the echoes from distant objects produced by arbitrary impinging ultrasound beams. Simulations obtained with this framework show that the time domain echoes generated by different objects have rich structure. We show that CNNs, algorithms that are excellent at pattern recognition tasks, can efficiently find patterns in the echoes and use these patterns to map the echoes to the object identity and location. The robustness of these algorithms to noise will also be quantified.

Elicitation, Computational Representation, and Analysis of Mission and System Requirements
Rahul Rai, Chandan Kumar Sahu, Vinayak Khade, Mohan Surya Raja Elapolu, Nafiseh Masoudi, Guo Freeman, Georges Fadel, Margaret Wiecek, and Cameron Turner, Clemson University
Denise Rizzo, Jonathan Smereka, Matt Castanier, and David J. Gorsich, US Army Ground Vehicle Systems Center
Link to project

Strategies for evaluating the impact of mission requirements on the design of mission‐specific vehicles are needed to enable project managers to assess potential benefits and associated costs of changes in requirements. Top-level requirements that cause significant cascaded difficulties on lower‐level requirements should be identified and presented to decision-makers. This project aims to introduce formal methods and computational tools to enable the analysis and allocation of mission requirements and associated key performance indicators (KPI). The presentation will outline two complementary interrelated research thrusts that are being pursued to achieve the discussed objectives: (1) representing the technical requirements computationally using natural language processing (NLP) and identifying inter-relationships between technical requirements using graph-based algorithms, and (2) deploying gamification and serious game platforms to carry out requirement engineering tasks.

November 5, Friday, 9:30-11:00am eastern time

Remote connection via Microsoft Teams. Contact William Lim (williamlim@umich.edu) for details.

In-the-wild Question Answering: Toward Natural Human-Autonomy Interaction
PI: Dr. Rada Mihalcea, Janice M. Jenkins Collegiate Professor of Computer Science and Engineering, University of Michigan
Link to project

Abstract to be announced.

Integrated Transient Control and Thermal Management of Autonomous Off-Road Vehicle Propulsion Systems
PI: Dr. Robert Prucka, Alan Kulwicki Professor of Motorsports Engineering, Clemson University
Link to project

Abstract to be announced.

Learning Enabled Mission Adaptation for a Hybrid Opposed Piston Engine
PI: Dr. Jason Siegel, Assistant Research Scientist, Mechanical Engineering, University of Michigan
Link to project

Abstract to be announced.

December 3, Friday, 9:30-11:00am eastern time

Remote connection via Microsoft Teams. Contact William Lim (williamlim@umich.edu) for details.

Deep Reinforcement Learning Approach to CPS Vehicle Re-envisioning
PI: Dr. Venkat Krovi, Michelin Endowed Chair Professor of Vehicle Automation, Clemson University
Link to project

Abstract to be announced.

Cognitive Modeling of Human Operator Behavior during Interaction with Autonomous Systems
PI: Dr. Tulga Ersal, Associate Research Scientist, Mechanical Engineering, University of Michigan
Link to project

Abstract to be announced.

Language Communication and Collaboration with Autonomous Vehicles Under Unexpected Situations
PI: Dr. Joyce Chai, Professor of Computer Science and Engineering, University of Michigan
Link to project

Abstract to be announced.

December 10, Friday, 9:30-10:30am eastern time

Remote connection via Microsoft Teams. Contact William Lim (williamlim@umich.edu) for details.

Energy Management of Multi-Scale Vehicle Fleets
PI: Dr. Beshah Ayalew, Professor of Automotive Engineering, Clemson University
Link to project

Abstract to be announced.

Materials Design of Polycarbonates at the Atomistic Scale with Machine Learning
PI: Dr. Kip Barrett, Assistant Professor of Mechanical Engineering, Mississippi State University
Link to project

Abstract to be announced.

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