Zobrazeno 1 - 10
of 354
pro vyhledávání: '"Bruckstein, A. M."'
Standard convolutions are prevalent in image processing and deep learning, but their fixed kernel design limits adaptability. Several deformation strategies of the reference kernel grid have been proposed. Yet, they lack a unified theoretical framewo
Externí odkaz:
http://arxiv.org/abs/2406.05400
This paper introduces a novel bio-mimetic approach for distributed control of robotic swarms, inspired by the collective behaviors of swarms in nature such as schools of fish and flocks of birds. The agents are assumed to have limited sensory percept
Externí odkaz:
http://arxiv.org/abs/2405.13941
Autor:
Amir, Michael, Bruckstein, Alfred M.
We investigate the algorithmic problem of uniformly dispersing a swarm of robots in an unknown, gridlike environment. In this setting, our goal is to comprehensively study the relationships between performance metrics and robot capabilities. We intro
Externí odkaz:
http://arxiv.org/abs/2404.19564
This work addresses the challenge of patrolling regular grid graphs of any dimension using a single mobile agent with minimal memory and limited sensing range. We show that it is impossible to patrol some grid graphs with $0$ bits of memory, regardle
Externí odkaz:
http://arxiv.org/abs/2307.09214
Several distributed algorithms are presented for the exploration of unknown indoor regions by a swarm of flying, energy constrained agents. The agents, which are identical, autonomous, anonymous and oblivious, uniformly cover the region and thus expl
Externí odkaz:
http://arxiv.org/abs/2305.08957
Consider a given planar circular region, in which there is an unknown number of smart mobile evaders. We wish to detect evaders using a line formation of sweeping agents whose total sensing length is predetermined. We propose procedures for designing
Externí odkaz:
http://arxiv.org/abs/2305.08137
Assume that inside an initial planar area there are smart mobile evaders attempting to avoid detection by a team of sweeping searching agents. All sweepers detect evaders with fan-shaped sensors, modeling the field of view of real cameras. Detection
Externí odkaz:
http://arxiv.org/abs/2305.00533
Traditional signal processing methods relying on mathematical data generation models have been cast aside in favour of deep neural networks, which require vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate,
Externí odkaz:
http://arxiv.org/abs/2303.10608
Neural networks are omnipresent, but remain poorly understood. Their increasing complexity and use in critical systems raises the important challenge to full interpretability. We propose to address a simple well-posed learning problem: estimating the
Externí odkaz:
http://arxiv.org/abs/2303.06638
The goal of this research is to devise guaranteed defense policies that allow to protect a given region from the entrance of smart mobile invaders by detecting them using a team of defending agents equipped with identical line sensors. By designing c
Externí odkaz:
http://arxiv.org/abs/2210.16063