DIRECT: a differential dynamic programming based framework for trajectory generation
This letter introduces a differential dynamic programming (DDP) based framework for polynomial trajectory generation for differentially flat systems. In particular, instead of using a linear equation with increasing size to represent multiple polynomial segments as in literature, we take a new persp...
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sg-ntu-dr.10356-1624102022-10-18T04:40:14Z DIRECT: a differential dynamic programming based framework for trajectory generation Cao, Kun Cao, Muqing Yuan, Shenghai Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Constrained Motion Planning Optimization and Optimal Control This letter introduces a differential dynamic programming (DDP) based framework for polynomial trajectory generation for differentially flat systems. In particular, instead of using a linear equation with increasing size to represent multiple polynomial segments as in literature, we take a new perspective from state-space representation such that the linear equation reduces to a finite horizon control system with a fixed state dimension and the required continuity conditions for consecutive polynomials are automatically satisfied. Consequently, the constrained trajectory generation problem (both with and without time optimization) can be converted to a discrete-time finite-horizon optimal control problem with inequality constraints, which can be approached by a recently developed interior-point DDP (IPDDP) algorithm. Furthermore, for unconstrained trajectory generation with preallocated time, we show that this problem is indeed a linear-quadratic tracking (LQT) problem (DDP algorithm with exact one iteration). All these algorithms enjoy linear complexity with respect to the number of segments. Both numerical comparisons with state-of-the-art methods and physical experiments are presented to verify and validate the effectiveness of our theoretical findings. The implementation code will be open-sourced. [Online] Available: https://github.com/ntu-caokun/DIRECT National Research Foundation (NRF) This work was supported by the National Research Foundation, Singapore under its Medium Sized Center for Advanced Robotics Technology Innovation. 2022-10-18T04:40:14Z 2022-10-18T04:40:14Z 2022 Journal Article Cao, K., Cao, M., Yuan, S. & Xie, L. (2022). DIRECT: a differential dynamic programming based framework for trajectory generation. IEEE Robotics and Automation Letters, 7(2), 2439-2446. https://dx.doi.org/10.1109/LRA.2022.3142744 2377-3766 https://hdl.handle.net/10356/162410 10.1109/LRA.2022.3142744 2-s2.0-85123298646 2 7 2439 2446 en IEEE Robotics and Automation Letters © 2022 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Constrained Motion Planning Optimization and Optimal Control Cao, Kun Cao, Muqing Yuan, Shenghai Xie, Lihua DIRECT: a differential dynamic programming based framework for trajectory generation |
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This letter introduces a differential dynamic programming (DDP) based framework for polynomial trajectory generation for differentially flat systems. In particular, instead of using a linear equation with increasing size to represent multiple polynomial segments as in literature, we take a new perspective from state-space representation such that the linear equation reduces to a finite horizon control system with a fixed state dimension and the required continuity conditions for consecutive polynomials are automatically satisfied. Consequently, the constrained trajectory generation problem (both with and without time optimization) can be converted to a discrete-time finite-horizon optimal control problem with inequality constraints, which can be approached by a recently developed interior-point DDP (IPDDP) algorithm. Furthermore, for unconstrained trajectory generation with preallocated time, we show that this problem is indeed a linear-quadratic tracking (LQT) problem (DDP algorithm with exact one iteration). All these algorithms enjoy linear complexity with respect to the number of segments. Both numerical comparisons with state-of-the-art methods and physical experiments are presented to verify and validate the effectiveness of our theoretical findings. The implementation code will be open-sourced. [Online] Available: https://github.com/ntu-caokun/DIRECT |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Cao, Kun Cao, Muqing Yuan, Shenghai Xie, Lihua |
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Article |
author |
Cao, Kun Cao, Muqing Yuan, Shenghai Xie, Lihua |
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Cao, Kun |
title |
DIRECT: a differential dynamic programming based framework for trajectory generation |
title_short |
DIRECT: a differential dynamic programming based framework for trajectory generation |
title_full |
DIRECT: a differential dynamic programming based framework for trajectory generation |
title_fullStr |
DIRECT: a differential dynamic programming based framework for trajectory generation |
title_full_unstemmed |
DIRECT: a differential dynamic programming based framework for trajectory generation |
title_sort |
direct: a differential dynamic programming based framework for trajectory generation |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/162410 |
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1749179211960025088 |