{"title":"移动机器人路径规划自主算法研究进展","authors":"Jian Zhang","doi":"10.1109/anzcc53563.2021.9628381","DOIUrl":null,"url":null,"abstract":"As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.","PeriodicalId":246687,"journal":{"name":"2021 Australian & New Zealand Control Conference (ANZCC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel Autonomous Algorithms of Path Planning for Mobile Robots: A Survey\",\"authors\":\"Jian Zhang\",\"doi\":\"10.1109/anzcc53563.2021.9628381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.\",\"PeriodicalId\":246687,\"journal\":{\"name\":\"2021 Australian & New Zealand Control Conference (ANZCC)\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 Australian & New Zealand Control Conference (ANZCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/anzcc53563.2021.9628381\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 Australian & New Zealand Control Conference (ANZCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/anzcc53563.2021.9628381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Novel Autonomous Algorithms of Path Planning for Mobile Robots: A Survey
As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.