{"title":"基于可变大小网格计算可达集的高速方法。","authors":"Wei Liao, Ming Tang, Yu Zhang, Taotao Liang","doi":"10.1016/j.isatra.2024.11.021","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we improve the dynamic programming based reachable set computation method by replacing the constant size grid in the original method with a variable size grid. With this improvement, the computational time consumption can be significantly reduced while maintaining the accuracy. The proposed method represents the reachable set as a sublevel set of a discount cost-to-go function, which is generated by dynamic programming. In order to compute the discount cost-to-go function quickly and accurately, the proposed method consists of three steps: (1) Rough computation. This step uses a coarse grid to obtain an interpolation function that is close to the real discount cost-to-go function; (2) Upsampling. This step is for generating a fine grid; (3) Fine tuning. This step generates an interpolation function that exactly approximates the real discount cost-to-go function. This paper theoretically proves the correctness of the proposed method and verifies its effectiveness by some examples.</p>","PeriodicalId":94059,"journal":{"name":"ISA transactions","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A high-speed method for computing reachable sets based on variable-size grid.\",\"authors\":\"Wei Liao, Ming Tang, Yu Zhang, Taotao Liang\",\"doi\":\"10.1016/j.isatra.2024.11.021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this paper, we improve the dynamic programming based reachable set computation method by replacing the constant size grid in the original method with a variable size grid. With this improvement, the computational time consumption can be significantly reduced while maintaining the accuracy. The proposed method represents the reachable set as a sublevel set of a discount cost-to-go function, which is generated by dynamic programming. In order to compute the discount cost-to-go function quickly and accurately, the proposed method consists of three steps: (1) Rough computation. This step uses a coarse grid to obtain an interpolation function that is close to the real discount cost-to-go function; (2) Upsampling. This step is for generating a fine grid; (3) Fine tuning. This step generates an interpolation function that exactly approximates the real discount cost-to-go function. This paper theoretically proves the correctness of the proposed method and verifies its effectiveness by some examples.</p>\",\"PeriodicalId\":94059,\"journal\":{\"name\":\"ISA transactions\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.isatra.2024.11.021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.isatra.2024.11.021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A high-speed method for computing reachable sets based on variable-size grid.
In this paper, we improve the dynamic programming based reachable set computation method by replacing the constant size grid in the original method with a variable size grid. With this improvement, the computational time consumption can be significantly reduced while maintaining the accuracy. The proposed method represents the reachable set as a sublevel set of a discount cost-to-go function, which is generated by dynamic programming. In order to compute the discount cost-to-go function quickly and accurately, the proposed method consists of three steps: (1) Rough computation. This step uses a coarse grid to obtain an interpolation function that is close to the real discount cost-to-go function; (2) Upsampling. This step is for generating a fine grid; (3) Fine tuning. This step generates an interpolation function that exactly approximates the real discount cost-to-go function. This paper theoretically proves the correctness of the proposed method and verifies its effectiveness by some examples.