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Pré-Publication, Document De Travail Année : 2023

Learned Sensitivity Metrics for Robust and Accurate Motion Planning

Résumé

This paper addresses the problem of computing robust trajectories against uncertainties in the robot model. Based on the notion of closed-loop state sensitivity introduced in [1], [2] for identifying deviations of the closed-loop trajectories of any robot/controller pair against variations of uncertain parameters in the robot model, uncertainty tubes can be derived for bounded parameter variations [3]. Such tubes were integrated within a motion planner named SAMP [4] to produce robust global plans, emphasizing the generation of low sensitivity trajectories. However, a bottleneck of this method remains the very high computational cost of the uncertainty tubes. In this paper, we mitigate this problem by proposing a novel framework that first incorporates a Long Short-Term Memory [5] (LSTM) neural network (NN) to provide fast and accurate uncertainty tubes estimation, and then reduces tracking errors at given points on the trajectory. We validate our framework on a full 3D-quadrotor UAV model performing a 'ring-recovering' task requiring high accuracy. Our results show the computational gain of the LSTM-based robust planning and that the subsequent optimization stage significantly improves the accuracy of the trajectory execution.
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Dates et versions

hal-04027078 , version 1 (13-03-2023)

Identifiants

  • HAL Id : hal-04027078 , version 1

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Simon Wasiela, Marco Cognetti, Paolo Robuffo Giordano, Juan Cortés, Thierry Simeon. Learned Sensitivity Metrics for Robust and Accurate Motion Planning. 2023. ⟨hal-04027078⟩
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