{"title":"Demo abstract: Simbeeotic: A simulation-emulation platform for large scale micro-aerial swarms","authors":"J. Waterman, Bryan Kate, Karthik Dantu, M. Welsh","doi":"10.1145/2185677.2185717","DOIUrl":null,"url":null,"abstract":"Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Designing the next generation of such swarms requires the ability to rapidly test algorithms, sensors, and support infrastructure at scale. Simulation is useful in the early stages of such large-scale system design, when hardware is unavailable or deployment at scale is im-practical. To faithfully represent the problem domain, an MAV swarm simulator must be able to model all key aspects of the system: actuation, sensing, and communication. Further, it is important to be able to quickly test swarm behavior using different control algorithms in a varied set of environments, and with a variety of sensors. We demonstrate Simbeeotic, a simulation framework that is capable of modeling large-scale MAV swarms. Simbeeotic enables algorithm development and rapid prototyping through both simulation and hardware-in-the-loop experimentation. We demonstrate Simbeeotic running simulated applications and videos demonstrating hybrid experiments with simulated MAV s as well as helicopters flying in our test bed that show the power and versatility required to assist next generation swarm design.","PeriodicalId":231003,"journal":{"name":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2185677.2185717","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Micro-aerial vehicle (MAV) swarms are an emerging class of mobile sensing systems. Designing the next generation of such swarms requires the ability to rapidly test algorithms, sensors, and support infrastructure at scale. Simulation is useful in the early stages of such large-scale system design, when hardware is unavailable or deployment at scale is im-practical. To faithfully represent the problem domain, an MAV swarm simulator must be able to model all key aspects of the system: actuation, sensing, and communication. Further, it is important to be able to quickly test swarm behavior using different control algorithms in a varied set of environments, and with a variety of sensors. We demonstrate Simbeeotic, a simulation framework that is capable of modeling large-scale MAV swarms. Simbeeotic enables algorithm development and rapid prototyping through both simulation and hardware-in-the-loop experimentation. We demonstrate Simbeeotic running simulated applications and videos demonstrating hybrid experiments with simulated MAV s as well as helicopters flying in our test bed that show the power and versatility required to assist next generation swarm design.