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• W2797037384 abstract "We present an approach to path following using so-called funnel functions. Synthesizing controllers to robustly follow a reference trajectory is a fundamental problem for autonomous vehicles. Robustness, in this context, requires our controllers to handle a specified amount of deviation from the desired trajectory. Our approach considers a timing law that describes how fast to move along a given reference trajectory and a feedback law for reducing deviations from the reference. We synthesize both feedback laws using control funnel functions that jointly encode the law as well as its correctness argument over a mathematical model of the vehicle dynamics. We adapt a previously described demonstration-based learning algorithm to synthesize a funnel function as well as the associated feedback law. We implement this law on top of a 1/8th scale autonomous vehicle called the Parkour car. We compare the performance of our path following approach against a trajectory tracking approach by specifying trajectories of varying lengths and curvatures. Our experiments demonstrate the improved robustness obtained from the use of funnel functions." @default.
• W2797037384 created "2018-04-24" @default.
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• W2797037384 date "2018-04-14" @default.
• W2797037384 modified "2023-09-27" @default.
• W2797037384 title "Path-Following through Control Funnel Functions" @default.
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