{"title":"冗余机械手运动规划的降维避障新方案设计与分析","authors":"Dongsheng Guo, Qu Li, Naimeng Cang, Yilin Yu, Weidong Zhang, Weibing Li","doi":"10.1002/rob.22594","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Obstacle avoidance (OA) is an important issue in the motion planning of redundant robot manipulators. Various effective OA schemes have been reported, but they may suffer from a large amount of calculation for the situation of multiple obstacles and/or complex-shaped obstacles. In this paper, to address the aforementioned limitation, a new OA scheme with dimensionality reduction is proposed and studied for redundant robot manipulators. Specifically, by combining robot kinematics and geometry, a typical inequality criterion for OA is designed, which can reduce the calculation for an obstacle point from the general three dimensions to one dimension. Such an inequality criterion is further aided by (1) the dynamic selection for the situation of a large number of obstacle points, and (2) the feature extraction for the situation of complex-shaped obstacles. With the OA environment optimized and the obstacles' dimension limited, the computational efficiency of generating the inequality criterion for specific scenarios can thus be improved. By incorporating the inequality criterion and the joint physical constraint, the new dimensionality-reduction OA (DROA) scheme is developed for redundant robot manipulators. Such a DROA scheme is depicted as a quadratic program that is solved by the reinforcement learning method. Simulation and experiment results under the PA10 robot manipulator verify the efficacy and applicability of the proposed DROA scheme.</p>\n </div>","PeriodicalId":192,"journal":{"name":"Journal of Field Robotics","volume":"42 7","pages":"3694-3704"},"PeriodicalIF":5.2000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design and Analysis of New Obstacle Avoidance Scheme With Dimensionality Reduction for Motion Planning of Redundant Robot Manipulators\",\"authors\":\"Dongsheng Guo, Qu Li, Naimeng Cang, Yilin Yu, Weidong Zhang, Weibing Li\",\"doi\":\"10.1002/rob.22594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Obstacle avoidance (OA) is an important issue in the motion planning of redundant robot manipulators. Various effective OA schemes have been reported, but they may suffer from a large amount of calculation for the situation of multiple obstacles and/or complex-shaped obstacles. In this paper, to address the aforementioned limitation, a new OA scheme with dimensionality reduction is proposed and studied for redundant robot manipulators. Specifically, by combining robot kinematics and geometry, a typical inequality criterion for OA is designed, which can reduce the calculation for an obstacle point from the general three dimensions to one dimension. Such an inequality criterion is further aided by (1) the dynamic selection for the situation of a large number of obstacle points, and (2) the feature extraction for the situation of complex-shaped obstacles. With the OA environment optimized and the obstacles' dimension limited, the computational efficiency of generating the inequality criterion for specific scenarios can thus be improved. By incorporating the inequality criterion and the joint physical constraint, the new dimensionality-reduction OA (DROA) scheme is developed for redundant robot manipulators. Such a DROA scheme is depicted as a quadratic program that is solved by the reinforcement learning method. Simulation and experiment results under the PA10 robot manipulator verify the efficacy and applicability of the proposed DROA scheme.</p>\\n </div>\",\"PeriodicalId\":192,\"journal\":{\"name\":\"Journal of Field Robotics\",\"volume\":\"42 7\",\"pages\":\"3694-3704\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Field Robotics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rob.22594\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Field Robotics","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rob.22594","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
Design and Analysis of New Obstacle Avoidance Scheme With Dimensionality Reduction for Motion Planning of Redundant Robot Manipulators
Obstacle avoidance (OA) is an important issue in the motion planning of redundant robot manipulators. Various effective OA schemes have been reported, but they may suffer from a large amount of calculation for the situation of multiple obstacles and/or complex-shaped obstacles. In this paper, to address the aforementioned limitation, a new OA scheme with dimensionality reduction is proposed and studied for redundant robot manipulators. Specifically, by combining robot kinematics and geometry, a typical inequality criterion for OA is designed, which can reduce the calculation for an obstacle point from the general three dimensions to one dimension. Such an inequality criterion is further aided by (1) the dynamic selection for the situation of a large number of obstacle points, and (2) the feature extraction for the situation of complex-shaped obstacles. With the OA environment optimized and the obstacles' dimension limited, the computational efficiency of generating the inequality criterion for specific scenarios can thus be improved. By incorporating the inequality criterion and the joint physical constraint, the new dimensionality-reduction OA (DROA) scheme is developed for redundant robot manipulators. Such a DROA scheme is depicted as a quadratic program that is solved by the reinforcement learning method. Simulation and experiment results under the PA10 robot manipulator verify the efficacy and applicability of the proposed DROA scheme.
期刊介绍:
The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments.
The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.