Distributed game-theoretic trajectory planning for multi-agent interactions

Williams, Zachary James

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https://hdl.handle.net/2142/121564 Copy

Description

Title

Distributed game-theoretic trajectory planning for multi-agent interactions

Author(s)

Williams, Zachary James

Issue Date

2023-07-21

Director of Research (if dissertation) or Advisor (if thesis)

Mehr, Negar Z

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• dynamic game theory
• multi-agent navigation
• potential games
• path planning for multiple robots
• multi-robot systems
• interactive trajectory planning

Abstract

In this work, we develop a scalable, local trajectory optimization algorithm that enables robots to interact with other agents. It has been shown that the interactions of multiple agents can be successfully captured in game-theoretic formulations, where the interaction outcome can be best modeled via the equilibria of the underlying dynamic game. However, it is challenging to compute equilibria of dynamic games as it involves simultaneously solving a set of coupled optimal control problems. Existing solvers operate in a centralized fashion and do not scale up tractably to multiple interacting agents. We enable scalable distributed game-theoretic planning by leveraging the structure inherent in multi-agent interactions, namely, interactions belonging to the class of dynamic potential games. Since equilibria of dynamic potential games can be found by minimizing a single potential function, we can apply distributed and decentralized control techniques to seek equilibria of multi-agent interactions in a scalable and distributed manner. We compare the performance of our algorithm with a centralized interactive planner in a number of simulation studies and demonstrate that our algorithm results in better efficiency and scalability. We further evaluate our method in hardware experiments involving multiple quadcopters.

Graduation Semester

2023-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Zachary James Williams

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