@inproceedings{iii:62350C,
	Abstract = {A recently developed fuzzy logic resource allocation algorithm that enables a collection of unmanned air vehicles (UAVs) to automatically cooperate as they make meteorological measurements will be discussed. The goal of the UAVs' coordinated effort is to measure the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy logic based planning algorithm determines the optimal trajectory and points each UAV will sample, while taking into account the UAVs' risk, reliability, and mission priority for sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting with any commander, requests other UAVs to help, changes the points that will be sampled when observing interesting phenomena, or to terminate the mission and return to base. The control algorithm allows three types of cooperation between UAVs. The underlying optimization procedures including the fuzzy logic based cost function, the fuzzy logic decision rule for UAV path assignment, the fuzzy algorithm that determines when a UAV should alter its mission to help another UAV and the underlying approach to quantifying risks are discussed. Significant simulation results will show the planning algorithm's effectiveness in initially selecting UAVs and determining UAV routes. Likewise, simulation shows the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team's likelihood of success.},
	Author = {{Smith, III}, James and Nguyen, ThanhVu},
	Booktitle = {International Society for Optical Engineering},
	Journal = {Signal Processing, Sensor Fusion, and Target Recognition XV},
	Keywords = {resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior},
	Pages = {118-129},
	Publisher = {SPIE},
	Title = {{Resource Manager for an Autonomous Coordinated Team of UAVs}},
	Url_paper = {Pub/2006.04.rm_PSI62350C.pdf},
	Year = {2006}}

{"_id":{"_str":"5345701f67062a5e680001e3"},"__v":1,"authorIDs":["545748b32abc8e9f3700025d"],"author_short":["Smith, III, J.","Nguyen, T."],"bibbaseid":"smith-nguyen-resourcemanagerforanautonomouscoordinatedteamofuavs-2006","bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"A recently developed fuzzy logic resource allocation algorithm that enables a collection of unmanned air vehicles (UAVs) to automatically cooperate as they make meteorological measurements will be discussed. The goal of the UAVs' coordinated effort is to measure the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy logic based planning algorithm determines the optimal trajectory and points each UAV will sample, while taking into account the UAVs' risk, reliability, and mission priority for sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting with any commander, requests other UAVs to help, changes the points that will be sampled when observing interesting phenomena, or to terminate the mission and return to base. The control algorithm allows three types of cooperation between UAVs. The underlying optimization procedures including the fuzzy logic based cost function, the fuzzy logic decision rule for UAV path assignment, the fuzzy algorithm that determines when a UAV should alter its mission to help another UAV and the underlying approach to quantifying risks are discussed. Significant simulation results will show the planning algorithm's effectiveness in initially selecting UAVs and determining UAV routes. Likewise, simulation shows the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team's likelihood of success.","author":[{"propositions":[],"lastnames":["Smith, III"],"firstnames":["James"],"suffixes":[]},{"propositions":[],"lastnames":["Nguyen"],"firstnames":["ThanhVu"],"suffixes":[]}],"booktitle":"International Society for Optical Engineering","journal":"Signal Processing, Sensor Fusion, and Target Recognition XV","keywords":"resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior","pages":"118-129","publisher":"SPIE","title":"Resource Manager for an Autonomous Coordinated Team of UAVs","url_paper":"Pub/2006.04.rm_PSI62350C.pdf","year":"2006","bibtex":"@inproceedings{iii:62350C,\n\tAbstract = {A recently developed fuzzy logic resource allocation algorithm that enables a collection of unmanned air vehicles (UAVs) to automatically cooperate as they make meteorological measurements will be discussed. The goal of the UAVs' coordinated effort is to measure the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy logic based planning algorithm determines the optimal trajectory and points each UAV will sample, while taking into account the UAVs' risk, reliability, and mission priority for sampling in certain regions. It also considers fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV renders the UAVs autonomous allowing them to change course immediately without consulting with any commander, requests other UAVs to help, changes the points that will be sampled when observing interesting phenomena, or to terminate the mission and return to base. The control algorithm allows three types of cooperation between UAVs. The underlying optimization procedures including the fuzzy logic based cost function, the fuzzy logic decision rule for UAV path assignment, the fuzzy algorithm that determines when a UAV should alter its mission to help another UAV and the underlying approach to quantifying risks are discussed. Significant simulation results will show the planning algorithm's effectiveness in initially selecting UAVs and determining UAV routes. Likewise, simulation shows the ability of the control algorithm to allow UAVs to effectively cooperate to increase the UAV team's likelihood of success.},\n\tAuthor = {{Smith, III}, James and Nguyen, ThanhVu},\n\tBooktitle = {International Society for Optical Engineering},\n\tJournal = {Signal Processing, Sensor Fusion, and Target Recognition XV},\n\tKeywords = {resource management, fuzzy logic, planning algorithms, decision support algorithms, cooperative behavior},\n\tPages = {118-129},\n\tPublisher = {SPIE},\n\tTitle = {{Resource Manager for an Autonomous Coordinated Team of UAVs}},\n\tUrl_paper = {Pub/2006.04.rm_PSI62350C.pdf},\n\tYear = {2006}}\n\n","author_short":["Smith, III, J.","Nguyen, T."],"key":"iii:62350C","id":"iii:62350C","bibbaseid":"smith-nguyen-resourcemanagerforanautonomouscoordinatedteamofuavs-2006","role":"author","urls":{" paper":"http://cse.unl.edu/~tnguyen/Pub/2006.04.rm_PSI62350C.pdf"},"keyword":["resource management","fuzzy logic","planning algorithms","decision support algorithms","cooperative behavior"],"downloads":0},"bibtype":"inproceedings","biburl":"http://cse.unl.edu/~tnguyen/vu_bibs1.bib","downloads":0,"keywords":["resource management","fuzzy logic","planning algorithms","decision support algorithms","cooperative behavior"],"search_terms":["resource","manager","autonomous","coordinated","team","uavs","smith","nguyen"],"title":"Resource Manager for an Autonomous Coordinated Team of UAVs","year":2006,"dataSources":["AAQcbSuJRTAKBGbG3"]}