Combining Lévy Walks and Flocking for Cooperative Surveillance Using Aerial Swarms

Sardinha, Hugo R. M.; Dragone, Mauro; Vargas, Patrícia A.

doi:10.1007/978-3-030-66412-1_15

Cited by 5 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their target search task Dadgar et al (2017) and Dadgar et al (2020), measured the diversity of individual robots within their MRS by calculating the amount of overlap between a robot and its neighbors' search areas. Agent sensor area overlap was also used by Sardinha et al (2020a) who calculated the size of area covered by at least two agents as a measure of the amount of effective exploration performed by their swarm in a surveillance task. This metric is similar to the one used by Esterle and Lewis (2017), Esterle and Lewis (2020) and Frasheri et al (2020) who used a k-coverage metric that calculated the amount of time a target spent being tracked by at least k agents.…”

Section: Spatial Distribution Based Metricsmentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

View full text Add to dashboard Cite

Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

show abstract