{"title":"机器人腿在障碍物环境下运动规划技术的实际比较","authors":"Tristan B. Smith, D. Chavez-Clemente","doi":"10.1109/SMC-IT.2009.26","DOIUrl":null,"url":null,"abstract":"ATHLETE is a large six-legged tele-operated robot. Each foot is a wheel; travel can be achieved by walking, rolling, or some combination of the two. Operators control ATHLETE by selecting parameterized commands from a command dictionary. While rolling can be done efficiently, any motion involving steps is cumbersome - each step can require multiple commands and take many minutes to complete. In this paper, we consider four different algorithms that generate a sequence of commands to take a step. We consider a baseline heuristic, a randomized motion planning algorithm, and two variants of A* search. Results for a variety of terrains are presented, and we discuss the quantitative and qualitative tradeoffs between the approaches.","PeriodicalId":422009,"journal":{"name":"2009 Third IEEE International Conference on Space Mission Challenges for Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Practical Comparison of Motion Planning Techniques for Robotic Legs in Environments with Obstacles\",\"authors\":\"Tristan B. Smith, D. Chavez-Clemente\",\"doi\":\"10.1109/SMC-IT.2009.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ATHLETE is a large six-legged tele-operated robot. Each foot is a wheel; travel can be achieved by walking, rolling, or some combination of the two. Operators control ATHLETE by selecting parameterized commands from a command dictionary. While rolling can be done efficiently, any motion involving steps is cumbersome - each step can require multiple commands and take many minutes to complete. In this paper, we consider four different algorithms that generate a sequence of commands to take a step. We consider a baseline heuristic, a randomized motion planning algorithm, and two variants of A* search. Results for a variety of terrains are presented, and we discuss the quantitative and qualitative tradeoffs between the approaches.\",\"PeriodicalId\":422009,\"journal\":{\"name\":\"2009 Third IEEE International Conference on Space Mission Challenges for Information Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Third IEEE International Conference on Space Mission Challenges for Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMC-IT.2009.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Third IEEE International Conference on Space Mission Challenges for Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMC-IT.2009.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Practical Comparison of Motion Planning Techniques for Robotic Legs in Environments with Obstacles
ATHLETE is a large six-legged tele-operated robot. Each foot is a wheel; travel can be achieved by walking, rolling, or some combination of the two. Operators control ATHLETE by selecting parameterized commands from a command dictionary. While rolling can be done efficiently, any motion involving steps is cumbersome - each step can require multiple commands and take many minutes to complete. In this paper, we consider four different algorithms that generate a sequence of commands to take a step. We consider a baseline heuristic, a randomized motion planning algorithm, and two variants of A* search. Results for a variety of terrains are presented, and we discuss the quantitative and qualitative tradeoffs between the approaches.