PhD Defense by Christopher Banks

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Title: Specification-Based Task Orchestration for Multi-Robot Aerial Teams



Date: Thursday, August 4th 2022

Time: 11 a.m. – 1 p.m. ET

Location: TSRB 523; Zoom



Christopher Banks

Robotics PhD Candidate

School of Interactive Computing

Georgia Institute of Technology




Dr. Magnus Egerstedt (Primary Advisor) – Department of Electrical Engineering and Computer Science, University of California, Irvine

Dr. Samuel Coogan (Co-Advisor) — School of Electrical and Computer Engineering, Georgia Institute of Technology

Dr. Sonia Chernova – School of Interactive Computing, Georgia Institute of Technology

Dr. Seth Hutchinson —  School of Interactive Computing, Georgia Institute of Technology

Dr. Ye Zhao – School of Mechanical Engineering, Georgia Institute of Technology




As humans begin working more frequently in environments with multi-agent systems, they are presented with challenges on how to control these systems in an intuitive manner. Current approaches tend to limit either the interaction ability of the user or limit the expressive capacity of instructions given to the robots. Applications that utilize temporal logics provide a human-readable syntax for systems that ensures formal guarantees for specification completion. By providing a modality for global task specification, we seek to reduce cognitive load and allow for high-level objectives to be communicated to a multi-agent system. In addition to this, we also seek to expand the capabilities of swarms to understand desired actions via interpretable commands retrieved from a human.


In this thesis, we first present a method for specification-based control of a quadrotor. We utilize quadrotors throughout this thesis as a highly agile and maneuverable application platform that has a wide variety of uses in complex problem domains. Leveraging specification-based control allows us to formulate a specification-based planning framework that will be utilized throughout the thesis. We then present methods  for creating systems which allows us to provide task decomposition, allocation and planning for a team of quadrotors defined as task orchestration of multi-robot systems. Next, the task allocation portion of the task orchestration work is extended in the online case by considering cost agnostic sampling of trajectories from an online optimization problem. Then, we will introduce learning techniques where temporal logic specifications are learned and generated from a set of user given traces. Finally, we will conclude this thesis by presenting an extension to the Robotarium through hardware and software modifications that provides remote users access to control aerial swarms.




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