Robust Trajectory Planning with Parametric Uncertainties - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Robust Trajectory Planning with Parametric Uncertainties


In this paper we extend the previously introduced notion of closed-loop state sensitivity by introducing the concept of input sensitivity and by showing how to exploit it in a trajectory optimization framework. This allows to generate an optimal reference trajectory for a robot that minimizes the state and input sensitivities against uncertainties in the model parameters, thus producing inherently robust motion plans. We parametrize the reference trajectories with Béziers curves and discuss how to consider linear and nonlinear constraints in the optimization process (e.g., input saturations). The whole machinery is validated via an extensive statistical campaign that clearly shows the interest of the proposed methodology.
Fichier principal
Vignette du fichier
2020a-InputSensitivityQuadrotor-final.pdf (566.4 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03260768 , version 1 (15-06-2021)


  • HAL Id : hal-03260768 , version 1


Pascal Brault, Quentin Delamare, Paolo Robuffo Giordano. Robust Trajectory Planning with Parametric Uncertainties. ICRA 2021 - IEEE International Conference on Robotics and Automation, May 2021, Xi'an, China. pp.11095-11101. ⟨hal-03260768⟩
131 View
323 Download


Gmail Mastodon Facebook X LinkedIn More