19 citations · 40 across the 11 of their papers we have counts for
12 papers
Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
Hansung Kim, Edward L. Zhu, Chang Seok Lim +1
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic i…
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria for Autonomous Racing
Edward L. Zhu, Francesco Borrelli
Dynamic games can be an effective approach for modeling interactive behavior between multiple competitive agents in autonomous racing and they provide a theoretical framework for s…
Learning Model Predictive Control with Error Dynamics Regression for Autonomous Racing
Haoru Xue, Edward L. Zhu, John M. Dolan +1
This work presents a novel Learning Model Predictive Control (LMPC) strategy for autonomous racing at the handling limit that can iteratively explore and learn unknown dynamics in…
A Gaussian Process Model for Opponent Prediction in Autonomous Racing
Edward L. Zhu, Finn Lukas Busch, Jake Johnson +1
In head-to-head racing, an accurate model of interactive behavior of the opposing target vehicle (TV) is required to perform tightly constrained, but highly rewarding maneuvers suc…
A Sequential Quadratic Programming Approach to the Solution of Open-Loop Generalized Nash Equilibria
Edward L. Zhu, Francesco Borrelli
Dynamic games can be an effective approach to modeling interactive behavior between multiple non-cooperative agents and they provide a theoretical framework for simultaneous predic…
Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
Xu Shen, Edward L. Zhu, Yvonne R. Stürz +1
We present a hierarchical control approach for maneuvering an autonomous vehicle (AV) in tightly-constrained environments where other moving AVs and/or human driven vehicles are pr…
Distributed Learning Model Predictive Control for Linear Systems
Yvonne R. Stürz, Edward L. Zhu, Ugo Rosolia +2
This paper presents a distributed learning model predictive control (DLMPC) scheme for distributed linear time invariant systems with coupled dynamics and state constraints. The pr…
Trajectory Optimization for Nonlinear Multi-Agent Systems using Decentralized Learning Model Predictive Control
Edward L. Zhu, Yvonne R. Stürz, Ugo Rosolia +1
We present a decentralized minimum-time trajectory optimization scheme based on learning model predictive control for multi-agent systems with nonlinear decoupled dynamics and coup…
Trajectory Tracking Control of a Flexible Spine Robot, With and Without a Reference Input
Andrew P. Sabelhaus, Shirley Huajing Zhao, Mallory C. Daly +6
The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to develop a flexible, actuated backbone for quadruped robots. In this w…
Model-Predictive Control with Inverse Statics Optimization for Tensegrity Spine Robots
Andrew P. Sabelhaus, Huajing Zhao, Edward L. Zhu +2
Robots with flexible spines based on tensegrity structures have potential advantages over traditional designs with rigid torsos. However, these robots can be difficult to control d…
Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots
Andrew P. Sabelhaus, Lara Janse van Vuuren, Ankita Joshi +6
Walking quadruped robots face challenges in positioning their feet and lifting their legs during gait cycles over uneven terrain. The robot Laika is under development as a quadrupe…
Inclined Surface Locomotion Strategies for Spherical Tensegrity Robots
Lee-Huang Chen, Brian Cera, Edward L. Zhu +8
This paper presents a new teleoperated spherical tensegrity robot capable of performing locomotion on steep inclined surfaces. With a novel control scheme centered around the simul…