Note on learning sensitivity metrics for a quadrotor - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes
Pré-Publication, Document De Travail Année : 2024

Note on learning sensitivity metrics for a quadrotor

Résumé

Robust trajectory planning can involve the use of ’uncertainty tubes’ which bound the system’s states and inputs. These tubes can be derived from a variety of metrics, involving complex simulation computations. This is the case for the sensitivity-based uncertainty tubes computation we consider in this work. In fact, this computation was shown to be com- putationally expensive, and even more in the context of sampling-based planners, where it has to be performed at least once for each new sample. In order to solve this problem, a learning-based approach is presented in this work to predict these sensitivity-based uncertainty tubes. The report is organized as follows: first, it recalls the sensitivity basis and the tube computation, then presents the chosen system and controller. Next, the network architecture is presented, and finally the results including dataset generation, training to different types of network, from LSTMs and RNNs to GRUs, and evaluation are given in a final section
Fichier principal
Vignette du fichier
Technical_note_Quadrotor_learning (1).pdf (1.93 Mo) Télécharger le fichier

Dates et versions

hal-04642304 , version 1 (11-07-2024)

Identifiants

  • HAL Id : hal-04642304 , version 1

Citer

Simon Wasiela, Smail Ait Bouhsain, Marco Cognetti, Juan Cortés, Thierry Simeon. Note on learning sensitivity metrics for a quadrotor. 2024. ⟨hal-04642304⟩
167 Consultations
34 Téléchargements

Partager

More