TY - GEN
T1 - Distributed Potential iLQR
T2 - 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
AU - Williams, Zach
AU - Chen, Jushan
AU - Mehr, Negar
N1 - Funding Information:
This work is supported by the National Science Foundation, under grants ECCS-2145134 CAREER Award, CNS-2218759, and CCF-2211542.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this work, we develop a scalable, local tra-jectory optimization algorithm that enables robots to interact with other robots. It has been shown that agents' interactions 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 typically 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 equi-libria 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.11Code Repository - https://github.com/labicon/dp-ilqr
AB - In this work, we develop a scalable, local tra-jectory optimization algorithm that enables robots to interact with other robots. It has been shown that agents' interactions 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 typically 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 equi-libria 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.11Code Repository - https://github.com/labicon/dp-ilqr
UR - http://www.scopus.com/inward/record.url?scp=85168704971&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168704971&partnerID=8YFLogxK
U2 - 10.1109/ICRA48891.2023.10161176
DO - 10.1109/ICRA48891.2023.10161176
M3 - Conference contribution
AN - SCOPUS:85168704971
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 3476
EP - 3482
BT - Proceedings - ICRA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 May 2023 through 2 June 2023
ER -