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Deep Learning-Based Negotiation Strategy Selection for Cooperative Conflict Resolution in Urban Air MobilityThis paper presents a collaborative conflict resolution technique using deep neural network-based intelligent search of the solution space. This approach offers a rapid convergence to a mutually acceptable solution for real-time conflict resolution, suitable for urban air mobility operations. Furthermore, the presented technique allows operational flexibility to the urban air mobility agents where these agents can collaboratively devise the solution via integrative negotiation, based on their local utility functions, as long as such a solution does not violate the global safety thresholds. The presented machine-to-machine negotiation method is built on our prior work on holistic assessment of the airspace and potential conflict detection implemented at-the-edge, onboard the unmanned aircraft systems. This paper extends the prior work to augment decision-making at-the-edge, thereby, promising a true distributed control architecture for urban air mobility. In this approach, each agent (a) builds a potential in-flight conflict map, (b) identifies the conflicting agents, (c) dynamically prepares a list of alternatives based on its current utility functions, (d) negotiates with the conflicting agents to pick one of these alternatives, and (e) implements the negotiated alternative to mutually resolve the conflict. Note that such an approach does not require a contingency plan to be made pre-flight, as the conflict resolution strategies are decided and negotiated in real time based on the present state of the agent. The contingency plan, if available, can serve as an input to the real-time conflict resolution strategy formulation, and also can be used as a fallback plan in case the negotiation fails and the impacted agents need to switch to a rule-based/supervisory resolution mode from the discussed distributed resolution mode. The presented collaborative negotiation-based conflict resolution technique incorporates a time-dependent reward function to catalyze collaborative resolution by incentivizing the agents with local and global rewards beneficial to their business operations.
Document ID
20205010540
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Aditya N Das
(Ames Research Center Mountain View, California, United States)
Kristina Marotta
(Ames Research Center Mountain View, California, United States)
Husni Idris
(Ames Research Center Mountain View, California, United States)
Date Acquired
November 21, 2020
Subject Category
Air Transportation And Safety
Meeting Information
Meeting: AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)
Location: virtual
Country: US
Start Date: January 11, 2021
End Date: January 21, 2021
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
PROJECT: 109492
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Advanced Air Mobility
AAM
Artificial Intelligence
AI
Collaborative control
Negotiation
Simulation
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