{"title":"基于改进 RRT* 的冗余光纤放置机械手平滑关节运动规划","authors":"","doi":"10.1016/j.rcim.2024.102851","DOIUrl":null,"url":null,"abstract":"<div><p>In automated fiber placement (AFP), addressing the continuous motion planning challenge of redundant layup manipulators in complex environments, this paper proposes an offline redundancy optimization algorithm based on improved RRT* (Rapidly-exploring Random Trees). This algorithm maximizes the utilization of kinematic redundancy to derive smooth joint trajectories devoid of collisions and singularities. Firstly, the algorithm entails constructing a search map by eliminating joint configurations that violate constraints, and subsequently planning and optimizing the joint path by minimizing a multi-objective cost under the map constraint. Furthermore, several strategies are introduced to enhance RRT* for redundancy optimization. These strategies include a piecewise Gaussian sampling strategy (PGSS) to guide efficient tree growth within complex channels and enable joint sampling constrained by task coordinates. Additionally, the improved Steering and Local Optimization method are proposed to plan joint motion while considering intermediate task sequences. The effectiveness of the proposed algorithm is demonstrated in handling complex motion planning scenarios, such as layup involving complex path curves or dense obstacles. Experimental results validate the algorithm's capability to find feasible collision-free and singularity-free paths in relevant scenarios, provided such paths exist. Moreover, trajectory smoothness is optimized with increasing iterations.</p></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":null,"pages":null},"PeriodicalIF":9.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smooth joint motion planning for redundant fiber placement manipulator based on improved RRT*\",\"authors\":\"\",\"doi\":\"10.1016/j.rcim.2024.102851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In automated fiber placement (AFP), addressing the continuous motion planning challenge of redundant layup manipulators in complex environments, this paper proposes an offline redundancy optimization algorithm based on improved RRT* (Rapidly-exploring Random Trees). This algorithm maximizes the utilization of kinematic redundancy to derive smooth joint trajectories devoid of collisions and singularities. Firstly, the algorithm entails constructing a search map by eliminating joint configurations that violate constraints, and subsequently planning and optimizing the joint path by minimizing a multi-objective cost under the map constraint. Furthermore, several strategies are introduced to enhance RRT* for redundancy optimization. These strategies include a piecewise Gaussian sampling strategy (PGSS) to guide efficient tree growth within complex channels and enable joint sampling constrained by task coordinates. Additionally, the improved Steering and Local Optimization method are proposed to plan joint motion while considering intermediate task sequences. The effectiveness of the proposed algorithm is demonstrated in handling complex motion planning scenarios, such as layup involving complex path curves or dense obstacles. Experimental results validate the algorithm's capability to find feasible collision-free and singularity-free paths in relevant scenarios, provided such paths exist. Moreover, trajectory smoothness is optimized with increasing iterations.</p></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736584524001388\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524001388","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Smooth joint motion planning for redundant fiber placement manipulator based on improved RRT*
In automated fiber placement (AFP), addressing the continuous motion planning challenge of redundant layup manipulators in complex environments, this paper proposes an offline redundancy optimization algorithm based on improved RRT* (Rapidly-exploring Random Trees). This algorithm maximizes the utilization of kinematic redundancy to derive smooth joint trajectories devoid of collisions and singularities. Firstly, the algorithm entails constructing a search map by eliminating joint configurations that violate constraints, and subsequently planning and optimizing the joint path by minimizing a multi-objective cost under the map constraint. Furthermore, several strategies are introduced to enhance RRT* for redundancy optimization. These strategies include a piecewise Gaussian sampling strategy (PGSS) to guide efficient tree growth within complex channels and enable joint sampling constrained by task coordinates. Additionally, the improved Steering and Local Optimization method are proposed to plan joint motion while considering intermediate task sequences. The effectiveness of the proposed algorithm is demonstrated in handling complex motion planning scenarios, such as layup involving complex path curves or dense obstacles. Experimental results validate the algorithm's capability to find feasible collision-free and singularity-free paths in relevant scenarios, provided such paths exist. Moreover, trajectory smoothness is optimized with increasing iterations.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.