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• W3210205117 startingPage "1818" @default.
• W3210205117 abstract "Differential dynamic programming (DDP) is a well-recognized method for trajectory optimization due to its fast convergence characteristics. However, the original DDP requires a predefined final time and cannot handle nonlinear constraints in optimization. This prohibits the application of DDP for autonomous vehicles due to the heuristic nature of setting a final time beforehand and the existence of inherent physical limits. This article revisits DDP by dynamically optimizing the final time via the first-order optimality condition of the value function and using augmented Lagrangian method to tackle the issue of nonlinear constraints. The resultant algorithm is termed as flexible final time constrained differential dynamic programming (FFT-CDDP). Extensive numerical simulations for a three-dimensional guidance problem are used to demonstrate the working of FFT-CDDP. The results indicate that the proposed FFT-CDDP provides much higher computational efficiency and stronger robustness against the initial solution guess, compared with the off-the-shelf general purpose optimal control software (GPOPS) algorithm." @default.
• W3210205117 created "2021-11-08" @default.
• W3210205117 creator A5019285865 @default.
• W3210205117 creator A5050182143 @default.
• W3210205117 creator A5057856062 @default.
• W3210205117 date "2022-06-01" @default.
• W3210205117 modified "2023-10-11" @default.
• W3210205117 title "Constrained Trajectory Optimization With Flexible Final Time for Autonomous Vehicles" @default.
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• W3210205117 doi "https://doi.org/10.1109/taes.2021.3121668" @default.
• W3210205117 hasPublicationYear "2022" @default.
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