{"title":"基于机动性约束的无人机路径规划自适应网格模型","authors":"Kun Yao, Jingwen Li, Bing Sun, Jieqiong Zhang","doi":"10.1109/CCSSE.2016.7784383","DOIUrl":null,"url":null,"abstract":"The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpolation after path planning increases the amount and complexity of algorithm. Considering to solve UAV path planning smoothing problem in the process of scenario modelling, this paper proposed an adaptive grid model, which provide the sparsest grid division under the UAV mobility constraints. The simulation results implicate that the path using proposed model satisfies mobility constraints and decreases the computational complexity effectively in both 2-D and 3-D path planning.","PeriodicalId":136809,"journal":{"name":"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"An adaptive grid model based on mobility constraints for UAV path planning\",\"authors\":\"Kun Yao, Jingwen Li, Bing Sun, Jieqiong Zhang\",\"doi\":\"10.1109/CCSSE.2016.7784383\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpolation after path planning increases the amount and complexity of algorithm. Considering to solve UAV path planning smoothing problem in the process of scenario modelling, this paper proposed an adaptive grid model, which provide the sparsest grid division under the UAV mobility constraints. The simulation results implicate that the path using proposed model satisfies mobility constraints and decreases the computational complexity effectively in both 2-D and 3-D path planning.\",\"PeriodicalId\":136809,\"journal\":{\"name\":\"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCSSE.2016.7784383\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Control Science and Systems Engineering (ICCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCSSE.2016.7784383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An adaptive grid model based on mobility constraints for UAV path planning
The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpolation after path planning increases the amount and complexity of algorithm. Considering to solve UAV path planning smoothing problem in the process of scenario modelling, this paper proposed an adaptive grid model, which provide the sparsest grid division under the UAV mobility constraints. The simulation results implicate that the path using proposed model satisfies mobility constraints and decreases the computational complexity effectively in both 2-D and 3-D path planning.