Bin Zhang , Chenghai Yin , Yuxing Fu , Yuyang Xia , Wei Fu
{"title":"基于改进型 RRT-Connect 的芒果采摘机器人收获运动规划","authors":"Bin Zhang , Chenghai Yin , Yuxing Fu , Yuyang Xia , Wei Fu","doi":"10.1016/j.biosystemseng.2024.10.008","DOIUrl":null,"url":null,"abstract":"<div><div>Aiming at the problems of long motion path planning time and low picking efficiency of picking robots in unstructured orchard environments, a heuristic dynamic Rapidly-exploring Random Tree Connect motion planning algorithm (HDRRT-Connect) for picking robots for fast mango harvesting path planning was proposed in this study. The algorithm was obtained by introducing adaptive target gravitation strategy and heuristic dynamic step strategy based on RRT-Connect algorithm. It adjusts the step-size according to the information of the orchard environment as well as the path searching situation, so as to avoid falling into the local optimum of the path. The prototype based on the algorithm was used to carry out picking experiments in the natural orchard environment. The prototype picking test under the natural environment of the orchard is carried out, and the test results showed that the average path cost of the HDRRT-Connect algorithm was 95.7739, the average planning time was 0.448 s, and the success rate was 90%. Compared with the RRT, RRT-Connect and Probabilistic Roadmaps (PRM) algorithms, the HDRRT-Connect planning speed was improved by 95%, 24% and 59%, respectively, and the path cost was reduced by 35%, 13% and 18%, respectively. The results of the experiment verified the feasibility and efficiency of the improved algorithm. The HDRRT-Connect algorithm proposed in this study could effectively shorten the planning time, reduce the search path cost and improve the planning success rate. The research provides technical support for the fast-harvesting operation of mango picking robot.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 177-189"},"PeriodicalIF":4.4000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harvest motion planning for mango picking robot based on improved RRT-Connect\",\"authors\":\"Bin Zhang , Chenghai Yin , Yuxing Fu , Yuyang Xia , Wei Fu\",\"doi\":\"10.1016/j.biosystemseng.2024.10.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Aiming at the problems of long motion path planning time and low picking efficiency of picking robots in unstructured orchard environments, a heuristic dynamic Rapidly-exploring Random Tree Connect motion planning algorithm (HDRRT-Connect) for picking robots for fast mango harvesting path planning was proposed in this study. The algorithm was obtained by introducing adaptive target gravitation strategy and heuristic dynamic step strategy based on RRT-Connect algorithm. It adjusts the step-size according to the information of the orchard environment as well as the path searching situation, so as to avoid falling into the local optimum of the path. The prototype based on the algorithm was used to carry out picking experiments in the natural orchard environment. The prototype picking test under the natural environment of the orchard is carried out, and the test results showed that the average path cost of the HDRRT-Connect algorithm was 95.7739, the average planning time was 0.448 s, and the success rate was 90%. Compared with the RRT, RRT-Connect and Probabilistic Roadmaps (PRM) algorithms, the HDRRT-Connect planning speed was improved by 95%, 24% and 59%, respectively, and the path cost was reduced by 35%, 13% and 18%, respectively. The results of the experiment verified the feasibility and efficiency of the improved algorithm. The HDRRT-Connect algorithm proposed in this study could effectively shorten the planning time, reduce the search path cost and improve the planning success rate. The research provides technical support for the fast-harvesting operation of mango picking robot.</div></div>\",\"PeriodicalId\":9173,\"journal\":{\"name\":\"Biosystems Engineering\",\"volume\":\"248 \",\"pages\":\"Pages 177-189\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1537511024002332\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024002332","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
Harvest motion planning for mango picking robot based on improved RRT-Connect
Aiming at the problems of long motion path planning time and low picking efficiency of picking robots in unstructured orchard environments, a heuristic dynamic Rapidly-exploring Random Tree Connect motion planning algorithm (HDRRT-Connect) for picking robots for fast mango harvesting path planning was proposed in this study. The algorithm was obtained by introducing adaptive target gravitation strategy and heuristic dynamic step strategy based on RRT-Connect algorithm. It adjusts the step-size according to the information of the orchard environment as well as the path searching situation, so as to avoid falling into the local optimum of the path. The prototype based on the algorithm was used to carry out picking experiments in the natural orchard environment. The prototype picking test under the natural environment of the orchard is carried out, and the test results showed that the average path cost of the HDRRT-Connect algorithm was 95.7739, the average planning time was 0.448 s, and the success rate was 90%. Compared with the RRT, RRT-Connect and Probabilistic Roadmaps (PRM) algorithms, the HDRRT-Connect planning speed was improved by 95%, 24% and 59%, respectively, and the path cost was reduced by 35%, 13% and 18%, respectively. The results of the experiment verified the feasibility and efficiency of the improved algorithm. The HDRRT-Connect algorithm proposed in this study could effectively shorten the planning time, reduce the search path cost and improve the planning success rate. The research provides technical support for the fast-harvesting operation of mango picking robot.
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.