Zobrazeno 1 - 10
of 1 140
pro vyhledávání: '"Tsiotras, A."'
Autor:
Knoedler, Luzia, So, Oswin, Yin, Ji, Black, Mitchell, Serlin, Zachary, Tsiotras, Panagiotis, Alonso-Mora, Javier, Fan, Chuchu
Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a
Externí odkaz:
http://arxiv.org/abs/2410.11157
Autor:
Knaup, Jacob W., Tsiotras, Panagiotis
Model predictive control solves a constrained optimization problem online in order to compute an implicit closed-loop control policy. Recursive feasibility -- guaranteeing that the optimal control problem will have a solution at every time step -- is
Externí odkaz:
http://arxiv.org/abs/2410.11107
We propose a standalone monocular visual Simultaneous Localization and Mapping (vSLAM) initialization pipeline for autonomous space robots. Our method, a state-of-the-art factor graph optimization pipeline, extends Structure from Small Motion (SfSM)
Externí odkaz:
http://arxiv.org/abs/2409.16465
Autor:
Zhang, Zhiyuan, Tsiotras, Panagiotis
We present Residual Descent Differential Dynamic Game (RD3G), a Newton-based solver for constrained multi-agent game-control problems. The proposed solver seeks a local Nash equilibrium for problems where agents are coupled through their rewards and
Externí odkaz:
http://arxiv.org/abs/2409.12152
Safe and accurate control of unmanned aerial vehicles in the presence of winds is a challenging control problem due to the hard-to-model and highly stochastic nature of the disturbance forces acting upon the vehicle. To meet performance constraints,
Externí odkaz:
http://arxiv.org/abs/2409.10369
This paper studies the problem of steering the distribution of a discrete-time dynamical system from an initial distribution to a target distribution in finite time. The formulation is fully nonlinear, allowing the use of general control policies, pa
Externí odkaz:
http://arxiv.org/abs/2409.02272
We consider the problem of data-driven stochastic optimal control of an unknown LTI dynamical system. Assuming the process noise is normally distributed, we pose the problem of steering the state's mean and covariance to a target normal distribution,
Externí odkaz:
http://arxiv.org/abs/2408.02777
This paper introduces a novel nonlinear stochastic model predictive control path integral (MPPI) method, which considers chance constraints on system states. The proposed belief-space stochastic MPPI (BSS-MPPI) applies Monte-Carlo sampling to evaluat
Externí odkaz:
http://arxiv.org/abs/2408.00494
Autor:
Liu, Fengjiao, Tsiotras, Panagiotis
This paper studies the set of terminal state covariances that are reachable over a finite time horizon from a given initial state covariance for a linear stochastic system with additive noise. For discrete-time systems, a complete characterization of
Externí odkaz:
http://arxiv.org/abs/2406.14740
Autor:
Xue, Shangjie, Dill, Jesse, Mathur, Pranay, Dellaert, Frank, Tsiotras, Panagiotis, Xu, Danfei
This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable
Externí odkaz:
http://arxiv.org/abs/2406.06948