activity
20192026
most citedCOME TOGETHER: Multi-Agent Geometric Consensus (Gathering, Rendezvous, Clustering, Aggregation)

5 citations · 6 across the 9 of their papers we have counts for

collaborators

10 papers

cs.MA2026

Connectivity Preservation and Graph Stretching in Range-Only Swarm Dispersion

Ariel Barel

We study connectivity-preserving finite-jump dispersion of anonymous, identical, and oblivious agents under an idealized range-only sensing model. Each agent measures only the dist…

cs.MA2024

Distributed and Decentralized Control and Task Allocation for Flexible Swarms

Yigal Koifman, Ariel Barel, Alfred M. Bruckstein

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 f…

cs.RO2023

Learning to Explore Indoor Environments using Autonomous Micro Aerial Vehicles

Yuezhan Tao, Eran Iceland, Beiming Li +7

In this paper, we address the challenge of exploring unknown indoor aerial environments using autonomous aerial robots with Size Weight and Power (SWaP) constraints. The SWaP const…

cs.MA2022

Multi-Agent Distributed and Decentralized Geometric Task Allocation

Michael Amir, Yigal Koifman, Yakov Bloch +2

We consider the general problem of geometric task allocation, wherein a large, decentralised swarm of simple mobile agents must detect the locations of tasks in the plane and posit…

cs.LG2021

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

Elchanan Zwecher, Eran Iceland, Sean R. Levy +3

The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effe…

cs.LG2020★ 1 cited

Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping

Elchanan Zwecher, Eran Iceland, Shmuel Y. Hayoun +3

The problem of autonomous indoor mapping is addressed. The goal is to minimize the time to achieve a predefined percentage of exposure with some desired level of certainty. The use…

cs.MA2019

Local Interactions for Cohesive Flexible Swarms

Rotem Manor, Ariel Barel, Alfred M. Bruckstein

Distributed gathering algorithms aim to achieve complete visibility graphs via a "never lose a neighbour" policy. We suggest a method to maintain connected graph topologies, while…

cs.MA2019★ 5 cited

COME TOGETHER: Multi-Agent Geometric Consensus (Gathering, Rendezvous, Clustering, Aggregation)

Ariel Barel, Rotem Manor, Alfred M. Bruckstein

This report surveys results on distributed systems comprising mobile agents that are identical and anonymous, oblivious and interact solely by adjusting their motion according to t…

cs.MA2019

On Steering Swarms

Ariel Barel, Rotem Manor, Alfred M. Bruckstein

The main contribution of this paper is a novel method allowing an external observer/controller to steer and guide swarms of identical and indistinguishable agents, in spite of the…

cs.MA2019

Probabilistic Gathering Of Agents With Simple Sensors

Ariel Barel, Thomas Dagès, Rotem Manor +1

Gathering is a fundamental task for multi-agent systems and the problem has been studied under various assumptions on the sensing capabilities of mobile agents. This paper addresse…