Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

Arvin, Farshad; Attar, Abdolrahman; Turgut, Ali Emre; Yue, Shigang

doi:10.1007/978-3-319-20466-6_58

Cited by 5 publications

(3 citation statements)

References 20 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Schmickl et al [61] proposed macroscopic modelling of an aggregation scenario using a Stock & Flow model. In previous work [62], a power-law equation model to predict the behaviour of a swarm was proposed.…”

Section: Probabilistic Modellingmentioning

confidence: 99%

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Arvin

Watson

Turgut

et al. 2017

J Intell Robot Syst

Self Cite

View full text Add to dashboard Cite

Swarm robotics studies the intelligent collective behaviour emerging from long-term interactions of large number of simple robots. However, maintaining a large number of robots operational for long time periods requires significant battery capacity, which is an issue for small robots. Therefore, recharging systems such as automated battery-swapping stations have been implemented. These systems require that the robots interrupt, albeit shortly, their activity, which influences the swarm behaviour. In this paper, a low-cost on-thefly wireless charging system, composed of several charging cells, is proposed for use in swarm robotic research studies. To determine the system's ability to support perpetual swarm operation, a probabilistic model that takes into account the swarm size, robot behaviour and charging area configuration, is outlined. Based on the model, a prototype system with 12 charging cells and a small mobile robot, Mona, was developed. A series of long-term experiments with different arenas and behavioural configurations indicated the model's accuracy and demonstrated the system's ability to support perpetual operation of multi-robotic system.

show abstract

Section: Probabilistic Modellingmentioning

confidence: 99%

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Arvin

Watson

Turgut

et al. 2017

J Intell Robot Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Schmickl, Hamann, Worn, and Crailsheim (2009) proposed a macroscopic modeling of the cue-based aggregation using the stock & flow model. In our previous work (Arvin, Attar, Turgut, & Yue, 2015), we proposed a mathematical model using a power-law equation to predict the aggregate size over time. In this work, to model the influence of the environmental parameters on the swarm behavior, we use a rate equation that represents the three processes that influence the size of an aggregate in single cue experiments.…”

Section: Probabilistic Modeling Of Aggregationmentioning

confidence: 99%

Section: Probabilistic Modeling Of Aggregationmentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

P$$\mathrm {\Phi }$$SS: An Open-Source Experimental Setup for Real-World Implementation of Swarm Robotic Systems in Long-Term Scenarios

Arvin

Krajník

Turgut

2019

Modelling and Simulation for Autonomous Systems

View full text Add to dashboard Cite

Power-Law Distribution of Long-Term Experimental Data in Swarm Robotics

Cited by 5 publications

References 20 publications

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

Perpetual Robot Swarm: Long-Term Autonomy of Mobile Robots Using On-the-fly Inductive Charging

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

P$$\mathrm {\Phi }$$SS: An Open-Source Experimental Setup for Real-World Implementation of Swarm Robotic Systems in Long-Term Scenarios

Contact Info

Product

Resources

About