Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm

Arvin, Farshad, Emre Turgut, Ali, Krajnik, Tomas and Yue, Shigang (2016) Investigation of cue-based aggregation in static and dynamic environments with a mobile robot swarm. Adaptive Behavior, 24 (2). pp. 102-118. ISSN 1059-7123

Adaptive Behavior-2016.pdf
[img] PDF
Adaptive Behavior-2016.pdf - Whole Document
Restricted to Repository staff only

Aggregation-Final.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


Aggregation is one of the most fundamental behaviors that has been studied in swarm robotic researches for more than two decades. The studies in biology revealed that environment is a preeminent factor in especially cue-based aggregation that can be defined as aggregation at a particular location which is a heat or a light source acting as a cue indicating an optimal zone. In swarm robotics, studies on cue-based aggregation mainly focused on different methods of aggregation and different parameters such as population size. Although of utmost importance, environmental effects on aggregation performance have not been studied systematically. In this paper, we study the effects of different environmental factors; size, texture and number of cues in a static setting and moving cues in a dynamic setting using real robots. We used aggregation time and size of the aggregate as the two metrics to measure aggregation performance. We performed real robot experiments with different population sizes and evaluated the performance of aggregation using the defined metrics. We also proposed a probabilistic aggregation model and predicted the aggregation performance accurately in most of the settings. The results of the experiments show that environmental conditions affect the aggregation performance considerably and have to be studied in depth.

Keywords:Swarm Robotics, adaptive behavior, Robotics, NotOAChecked
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:22466
Deposited On:09 Mar 2016 15:58

Repository Staff Only: item control page