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• W4314946929 abstract "Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise. However, recent technological advances in computing devices enable online optimization or sampling-based methods to solve control problems. For example, Control Barrier Functions (CBFs) have been proposed to numerically solve convex optimization problems that ensure the control input to stay in the safe set. Model Predictive Path Integral (MPPI) control uses forward sampling of stochastic differential equations to solve optimal control problems online. Both control algorithms are widely used for nonlinear systems because they avoid calculating the derivatives of the nonlinear dynamic functions. In this paper, we use Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the sampling-based algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation. We also show that our algorithm needs fewer samples than the original MPPI algorithm does by providing a sampling complexity analysis." @default.
• W4314946929 created "2023-01-10" @default.
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• W4314946929 date "2022-12-06" @default.
• W4314946929 modified "2023-09-27" @default.
• W4314946929 title "Path Integral Methods with Stochastic Control Barrier Functions" @default.
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• W4314946929 doi "https://doi.org/10.1109/cdc51059.2022.9993190" @default.
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