Huazhang Zhu;Tian Lan;Shunzheng Ma;Xuan Zhao;Huiliang Shang;Ruijiao Li
{"title":"代码:完全覆盖AAV勘探计划使用双重视点多层次复杂环境","authors":"Huazhang Zhu;Tian Lan;Shunzheng Ma;Xuan Zhao;Huiliang Shang;Ruijiao Li","doi":"10.1109/LRA.2024.3521179","DOIUrl":null,"url":null,"abstract":"We present an autonomous exploration method for autonomous aerial vehicles (AAVs) for three-dimensional (3D) exploration tasks. Our approach, utilizing a cooperation strategy between common viewpoints and frontier viewpoints, fully leverages the agility and flexibility of AAVs, demonstrating faster and more comprehensive exploration than the current state-of-the-art. Common viewpoints, specifically designed for AAVs exploration, are evenly distributed throughout the 3D space for 3D exploration tasks. Frontier viewpoints are positioned at the centroids of clusters of frontier points to help the AAV maintain motivation to explore unknown complex 3D environments and navigate through narrow corners and passages. This strategy allows the AAV to access every corner of the 3D environment. Additionally, our method includes a refined relocation mechanism for AAVs specifically. Experimental comparisons show that our method ensures complete exploration coverage in environments with complex terrain. Our method outperforms TARE DSVP, GBP and MBP by the coverage rate of 64%, 63%, 54% and 49% respectively in garage-D. In narrow tunnels, ours and DSVP are the only two evaluated methods that achieve complete coverage, with ours outperforming DSVP by 35% in exploration efficiency.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 2","pages":"1880-1887"},"PeriodicalIF":4.6000,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CODE: Complete Coverage AAV Exploration Planner Using Dual-Type Viewpoints for Multi-Layer Complex Environments\",\"authors\":\"Huazhang Zhu;Tian Lan;Shunzheng Ma;Xuan Zhao;Huiliang Shang;Ruijiao Li\",\"doi\":\"10.1109/LRA.2024.3521179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an autonomous exploration method for autonomous aerial vehicles (AAVs) for three-dimensional (3D) exploration tasks. Our approach, utilizing a cooperation strategy between common viewpoints and frontier viewpoints, fully leverages the agility and flexibility of AAVs, demonstrating faster and more comprehensive exploration than the current state-of-the-art. Common viewpoints, specifically designed for AAVs exploration, are evenly distributed throughout the 3D space for 3D exploration tasks. Frontier viewpoints are positioned at the centroids of clusters of frontier points to help the AAV maintain motivation to explore unknown complex 3D environments and navigate through narrow corners and passages. This strategy allows the AAV to access every corner of the 3D environment. Additionally, our method includes a refined relocation mechanism for AAVs specifically. Experimental comparisons show that our method ensures complete exploration coverage in environments with complex terrain. Our method outperforms TARE DSVP, GBP and MBP by the coverage rate of 64%, 63%, 54% and 49% respectively in garage-D. In narrow tunnels, ours and DSVP are the only two evaluated methods that achieve complete coverage, with ours outperforming DSVP by 35% in exploration efficiency.\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":\"10 2\",\"pages\":\"1880-1887\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10811860/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10811860/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
CODE: Complete Coverage AAV Exploration Planner Using Dual-Type Viewpoints for Multi-Layer Complex Environments
We present an autonomous exploration method for autonomous aerial vehicles (AAVs) for three-dimensional (3D) exploration tasks. Our approach, utilizing a cooperation strategy between common viewpoints and frontier viewpoints, fully leverages the agility and flexibility of AAVs, demonstrating faster and more comprehensive exploration than the current state-of-the-art. Common viewpoints, specifically designed for AAVs exploration, are evenly distributed throughout the 3D space for 3D exploration tasks. Frontier viewpoints are positioned at the centroids of clusters of frontier points to help the AAV maintain motivation to explore unknown complex 3D environments and navigate through narrow corners and passages. This strategy allows the AAV to access every corner of the 3D environment. Additionally, our method includes a refined relocation mechanism for AAVs specifically. Experimental comparisons show that our method ensures complete exploration coverage in environments with complex terrain. Our method outperforms TARE DSVP, GBP and MBP by the coverage rate of 64%, 63%, 54% and 49% respectively in garage-D. In narrow tunnels, ours and DSVP are the only two evaluated methods that achieve complete coverage, with ours outperforming DSVP by 35% in exploration efficiency.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.