Paper Info
Title
DynFloR - A Flow Approach for Data Delivery Optimization in Multi-Robot Network Patrolling.
Abstract
Deploying fleets of mobile robots in real scenarios and environments raises several scientific challenges. One of them concerns the ability of the robots to adapt to the dynamics of their environment. We introduce DynFloR, a dynamic network flow based approach for finding optimal policies for data delivery in multi-robot network patrolling where the robots can communicate instantly and free of charge one to another when they meet, there is a periodicity of the robot meetings and the distribution of the data collected during the patrol is regular. Experiments on randomly generated synthetic examples are performed for evaluating the performance of the DynFloR method. The performed experiments empirically show that independent of the problem setting (such as number of robots, memory of the robots) the amount of data transferred to a base station per unit of time converges to an equilibrium state. The case of lost data has been also examined through various experiments, but it requires further experimentation as well as in-depth analysis.
Year
DOI
Venue
2019
10.1016/j.procs.2019.09.164
Procedia Computer Science
Keywords
Field
DocType
Optimization,Decision making,Multi-robot patrolling,Network flow 2000 MSC: 68T40,93C85,05C21
Dynamic network analysis,Data mining,Base station,Computer science,Flow (psychology),Patrolling,Real-time computing,Autonomous system (Internet),Data delivery,Robot,Mobile robot
Conference
Volume
Issue
ISSN
159
C
1877-0509
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Vlad-Sebastian Ionescu100.68
Zsuzsanna Onet-Marian200.34
Marin-Georgian Badita300.34
Gabriela Czibula48019.53
Mihai-Ioan Popescu500.34
Jilles Steeve Dibangoye612312.76
Olivier Simonin723133.99