Zobrazeno 1 - 10
of 233
pro vyhledávání: '"Andersson, Sean B."'
While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most existing robotic applications require the maintenance of a detailed environment representation which consumes a significant amou
Externí odkaz:
http://arxiv.org/abs/2410.06263
We consider the problem of using an autonomous agent to persistently monitor a collection of dynamic targets distributed in an environment. We generalize existing work by allowing the agent's dynamics to vary throughout the environment, leading to a
Externí odkaz:
http://arxiv.org/abs/2403.19769
Deep learning methods have been widely used in robotic applications, making learning-enabled control design for complex nonlinear systems a promising direction. Although deep reinforcement learning methods have demonstrated impressive empirical perfo
Externí odkaz:
http://arxiv.org/abs/2403.10621
Autor:
Zhu, Yancheng, Andersson, Sean B.
This paper considers the problem of localizing a set of nodes in a wireless sensor network when both their positions and the parameters of the communication model are unknown. We assume that a single agent moves through the environment, taking measur
Externí odkaz:
http://arxiv.org/abs/2402.11483
Autor:
Shen, Guoyao, Zhu, Yancheng, Jara, Hernan, Andersson, Sean B., Farris, Chad W., Anderson, Stephan, Zhang, Xin
Recent works have demonstrated success in MRI reconstruction using deep learning-based models. However, most reported approaches require training on a task-specific, large-scale dataset. Regularization by denoising (RED) is a general pipeline which e
Externí odkaz:
http://arxiv.org/abs/2308.10968
In this paper we study an infinite-horizon persistent monitoring problem in a two-dimensional mission space containing a finite number of statically placed targets, at each of which we assume a constant rate of uncertainty accumulation. Equipped with
Externí odkaz:
http://arxiv.org/abs/2304.03667
This work shows the existence of optimal control laws for persistent monitoring of mobile targets in a one-dimensional mission space and derives explicit solutions. The underlying performance metric consists of minimizing the total uncertainty accumu
Externí odkaz:
http://arxiv.org/abs/2210.01294
Bearing-based distributed formation control is attractive because it can be implemented using vision-based measurements to achieve a desired formation. Gradient-descent-based controllers using bearing measurements have been shown to have many benefic
Externí odkaz:
http://arxiv.org/abs/2203.11967
Autor:
Pinto, Samuel C., Welikala, Shirantha, Andersson, Sean B., Hendrickx, Julien M., Cassandras, Christos G.
We investigate the problem of optimally observing a finite set of targets using a mobile agent over an infinite time horizon. The agent is tasked to move in a network-constrained structure to gather information so as to minimize the worst-case uncert
Externí odkaz:
http://arxiv.org/abs/2201.06607
Publikováno v:
Small 2022, 18, 2107024
Real-time feedback-driven single-particle tracking (RT-FD-SPT) is a class of techniques in the field of single-particle tracking that uses feedback control to keep a particle of interest in a detection volume. These methods provide high spatiotempora
Externí odkaz:
http://arxiv.org/abs/2111.09178