{"title":"利用遗传算法掌握三维物体的规划","authors":"Zichen Zhang, J. Gu","doi":"10.1109/ICAL.2012.6308157","DOIUrl":null,"url":null,"abstract":"In this paper, we apply genetic algorithm (GA) to the optimization problem in grasp planning. This method can be used to find “pregrasps” for 3D objects in arbitrary shape and different dexterous hands, which serve as the first step of a complete grasping action. Each component of the GA planner is discussed in detail. The proposed algorithm is implemented in GraspIt! simulator [1]. It is tested on different hand-object combinations and the result shows that genetic algorithm is effective in finding high-quality pregrasps.","PeriodicalId":373152,"journal":{"name":"2012 IEEE International Conference on Automation and Logistics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Grasp planning of 3D objects using genetic algorithm\",\"authors\":\"Zichen Zhang, J. Gu\",\"doi\":\"10.1109/ICAL.2012.6308157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we apply genetic algorithm (GA) to the optimization problem in grasp planning. This method can be used to find “pregrasps” for 3D objects in arbitrary shape and different dexterous hands, which serve as the first step of a complete grasping action. Each component of the GA planner is discussed in detail. The proposed algorithm is implemented in GraspIt! simulator [1]. It is tested on different hand-object combinations and the result shows that genetic algorithm is effective in finding high-quality pregrasps.\",\"PeriodicalId\":373152,\"journal\":{\"name\":\"2012 IEEE International Conference on Automation and Logistics\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Automation and Logistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAL.2012.6308157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Automation and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAL.2012.6308157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Grasp planning of 3D objects using genetic algorithm
In this paper, we apply genetic algorithm (GA) to the optimization problem in grasp planning. This method can be used to find “pregrasps” for 3D objects in arbitrary shape and different dexterous hands, which serve as the first step of a complete grasping action. Each component of the GA planner is discussed in detail. The proposed algorithm is implemented in GraspIt! simulator [1]. It is tested on different hand-object combinations and the result shows that genetic algorithm is effective in finding high-quality pregrasps.
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