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• W3022919595 abstract "The goal of this thesis is to design a learning model predictive controller (LMPC) that allows multiple agents to race competitively on a predefined race track in real-time. This thesis addresses two major shortcomings in the already existing single-agent formulation. Previously, the agent determines a locally optimal trajectory but does not explore the state space, which may be necessary for overtaking maneuvers. Additionally, obstacle avoidance for LMPC has been achieved in the past by using a non-convex terminal set, which increases the complexity for determining a solution to the optimization problem. The proposed algorithm for multi-agent racing explores the state space by executing the LMPC for multiple different initializations, which yields a richer terminal safe set. Furthermore, a new method for selecting states in the terminal set is developed, which keeps the convexity for the terminal safe set and allows for taking suboptimal states." @default.
• W3022919595 created "2020-05-13" @default.
• W3022919595 creator A5011540585 @default.
• W3022919595 date "2020-05-02" @default.
• W3022919595 modified "2023-09-27" @default.
• W3022919595 title "Learning Model Predictive Control for Competitive Autonomous Racing." @default.
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• W3022919595 hasPublicationYear "2020" @default.
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