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Agenda

This year’s ARC Annual Program Review was held online.

Please contact arc-event-inquiries@umich.edu for any issues with accessing the session. (note: Microsoft Edge browser is not supported.)

Day 1 - May 10 (Monday)

9:00 AM

Welcome & Opening Remarks (Day 1 - Main Hall)

 

Bogdan Epureanu
Director, Automotive Research Center

David Gorsich
Chief Scientist, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC)

 

The Honorable Gary Peters (Day 1 - Main Hall)
United States Senate

Dr. Alec D. Gallimore (Day 1 - Main Hall)
Robert J. Vlasic Dean of Engineering at the University of Michigan

9:25

Keynote: (Day 1 - Main Hall)

Michael Cadieux
Director, U.S. Army DEVCOM GVSC

9:45

Keynote: (Day 1 - Main Hall)

Christopher Davey
Global Manager of Systems Engineering, System Safety, Modelling & Simulation at Ford Motor Company

10:05

Break

10:15

Case Study 1

There is no "I" in Team: Optimal task allocation in human-autonomy teaming

Abstract: Autonomous vehicles are increasingly thought of as team members alongside humans in both military and civilian applications. Such autonomous agents are capable of handling dangerous tasks but are limited in their reactions to unforeseen events. At the same time, humans have more adaptive and creative problem-solving skills but are limited in terms of handling some specific tasks and managing cognitive loads. The inclusion of autonomy within a team requires a significant effort to train the agents and dynamically distribute tasks among the agents to perform optimally during operations.

In this case study, we brought together three projects and constructed a unique framework to train a team of heterogeneous agents, composed of both humans and autonomous agents, to reliably perform tasks in uncertain environments. A computational trust model for multi-agent teams was created and deployed in trust-based path planning algorithms. The cost and limitations of the mobility of the agents was accounted for when training the team in a synthetic environment. An artificial intelligence algorithm was then developed for autonomous agents to learn how to collaborate with humans and other autonomous agents through reinforcement learning. To showcase the application of the developed algorithms, a disaster relief scenario was simulated in a high-fidelity game engine environment where a human interacts with the environment in real-time using virtual reality. An adaptive algorithm was developed to assist the humans in their decision-making and improve their performance by continuously evaluating the human’s cognitive task loads. The heterogeneity of the team was described by the differences in agent capabilities of task handling, sensing, and communication, as well as the level of risk aversion in humans’ decision-making processes. Results of this study show that the trained autonomous agents using the developed algorithms can reliably collaborate with humans and clear all the assigned tasks in a complex environment. In this study, the team performance when the human-autonomy communication used an adaptive interface based on a data-driven cognitive task load model increased by over 180% compared to a heuristic interface, and over 72% compared to a fixed and most detailed interface. The proposed framework is a basis for further developments and design of human-autonomy teams.

11:00

Break

11:10

Technical Session 1

 

Session 1.A

Session Chairs:
Jon Smereka, Mike Cole

Session 1.B

Session Chairs:
Matt Castanier, Steve Rapp

11.10 AM 1.25 A Decision-Based Mobility Model for Semi and Fully Autonomous Vehicles
PI: Vijitashwa Pandey
1.28 Robust Terrain Identification and Path Planning for Autonomous Ground Vehicles in Unstructured Environments
PI: Jeremy Bos
11.30 AM 2.12 Cognitive Modeling of Human Operator Behavior During Interaction With Autonomous Systems
PI: Tulga Ersal
1.32 Tensor data compression and dimensionality reduction for autonomous mobility
PI: Shravan Veerapaneni
11.50 AM 5.20 Dynamic Teaming of Autonomous Vehicles to Address Intelligent Adversarial Actions
PI: Bogdan Epureanu
5.14 Advances in computation of safety envelopes for autonomous systems
PI: Necmiye Ozay
12.10 PM 5.19 Adversarial Scene Generation
PI: Ram Vasudevan
5.15 Integrating Safe Learning into Supervisory Limit Protection for Autonomous Vehicular Systems
PI: Ilya Kolmanovsky, Anouck Girard
   
12:30 PM Break
1:30 Poster Session 1 (poster and networking schedule)
2:45 Poster Session 2 (poster and networking schedule)
4:00 Adjourn, Day 1

Day 2 - May 11 (Tuesday)

9:00 AM

Introductions (Day 2 - Main Hall)

Kira Barton & Ram Vasudevan
Associate Directors, Automotive Research Center

9:05

Keynote: (Day 2 - Main Hall)

Dr. Patrick Baker
Director, U.S. Army DEVCOM Army Research Laboratory

9:25

Keynote: (Day 2 - Main Hall)

Tony Bromwell
Vice President, Engineering Operations at MSC Software

9:45

Keynote: (Day 2 - Main Hall)

Kevin Dutcher
Sr Systems Engineer, Ricardo Defense

10:05 Break
10:15

Case Study 2

Right Resources, Right Place, Right Time: Using real-time physics-based simulation to optimize high-level mission planning under uncertainty

Abstract: As Army vehicles become more intelligent with autonomous behaviors, planning their missions under uncertainties becomes more complex. On the one-hand, low-level `path planning algorithms’ solve the challenge of finding an optimal trajectory between a pair of source and destination while considering obstacles, and provide closed-loop control signals to each vehicle so that they can follow the trajectory with minimum deviations. On the other hand, a high-level ‘mission planning algorithm’ provides the sequence of visits for the vehicle while considering points of interests, environment uncertainties, and other operational constraints. Though these low-level and high-level algorithms work in tandem, due to overwhelming complexities, the research works have traditionally been done independent to each other. In this case study, we address this gap by bringing two ARC projects together to integrate real-time physics-based simulation of autonomous ground vehicles with high-level mission planning algorithms.

The summary of the case study are as follows: best and worst scenarios inferred from data summarization may not necessarily be the best and worst scenarios for mission planning; for a fixed importance of distance and HRI costs, the mission plan across scenarios from low-level planning may change, and this is specifically observed in scenarios with lower goal speed. The deviations in mission plan due to scenarios is around 25%; and also, deviations within scenarios due to weights for distance and HRI costs is at 35%. These imply the importance of synergy between low-level and high-level planning algorithms.

11:00 Break
11:10

Technical Session 2 - Part I

 

Session 2.A

Session Chairs:
Victor Paul, Matt Castanier

Session 2.B

Session Chairs:
Katie Sebek, David Lamb

11.10 AM 1.35 Novel Algorithms for Multi-Agent Autonomous Telerobotic Surveillance and Reconnaissance System
PI: Manish Bansal
3.A84 Split Traction Characteristic Analytics and Digital Image Correlation (DIC) Strain Analysis
PI: Lee Moradi
11.30 AM 1.27 Mutually-Aaptive Shared Control Between Human Operators and Autonomy in Ground Vehicles
PI: Tulga Ersal
3.17 Enhanced Multiscale Off‐Road Mobility Prediction Capability with Machine Learning Constitutive Modeling for Large Deformable Granular Terrain
PI: Hiroyuki Sugiyama
11.50 AM 5.16 Resilient Teaming: Fleet organization and decision making in heterogeneous vehicle teams
PI: Kira Barton
1.29 Characterizing the Terrain Strength and Type Using Remote Sensing for Mobility
PI: Thomas Oommen
   
12:10 PM Break
12:20 PM

Technical Session 2 - Part II

 

Session 2.C

Session Chairs:
Matt Castanier, Jon Smereka

Session 2.D

Session Chairs:
Vamshi Korivi, Steve Rapp

12.20 PM 1.34 Processing Image Data from Unstructured Environments
PI: Nick Vlahopoulos
4.34 Discovery of Salt Hydrates for Thermal Energy Storage
PI: Donald Siegel
12.40 PM 5.17 Communication-Constrained Multi-Robot Coordination
PI: Ed Olson
5.18 Probability of Mobility for Mission Planning of Autonomous Ground Vehicles at “High Stress” Environments"
PI: Zissimos Mourelatos, Zhen Hu
1.00 PM Best Lightning Talk Competition Finalists
Announced by Andre Boehman
2.A72 Assessing the Quality of Driving of Off-Road Vehicles: A Set of Test Courses for the U.S. Army
PI: Paul Green
   
1:20 Awards and Closing
 

Bogdan Epureanu
Director, Automotive Research Center

David Gorsich
Chief Scientist, U.S. Army DEVCOM Ground Vehicle Systems Center (GVSC)

1:30 Adjourn, Day 2

Contact: arc-event-inquiries@umich.edu