{"title":"投影正动力学的等价性和动态一致逆解","authors":"João Moura, V. Ivan, M. S. Erden, S. Vijayakumar","doi":"10.15607/RSS.2019.XV.036","DOIUrl":null,"url":null,"abstract":"—The analysis, design, and motion planning of robotic systems, often relies on its forward and inverse dynamic models. When executing a task involving interaction with the environ- ment, both the task and the environment impose constraints on the robot’s motion. For modeling such systems, we need to incorporate these constraints in the robot’s dynamic model. In this paper, we define the class of Task-based Constraints (TbC) to prove that the forward dynamic models of a constrained system obtained through the Projection-based Dynamics (PbD), and the Operational Space Formulation (OSF) are equivalent. In order to establish such equivalence, we first generalize the OSF to a rank deficient Jacobian. This generalization allow us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. We then reformulate the PbD constraint inertia matrix, generalizing all its previous distinct algebraic variations. We also analyse the condition number of different constraint inertia matrices, which affects the numerical stability of its inversion. Furthermore, we show that we can recover the operational space control with constraints from a multiple Task-based Constraint abstraction.","PeriodicalId":307591,"journal":{"name":"Robotics: Science and Systems XV","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Equivalence of the Projected Forward Dynamics and the Dynamically Consistent Inverse Solution\",\"authors\":\"João Moura, V. Ivan, M. S. Erden, S. Vijayakumar\",\"doi\":\"10.15607/RSS.2019.XV.036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"—The analysis, design, and motion planning of robotic systems, often relies on its forward and inverse dynamic models. When executing a task involving interaction with the environ- ment, both the task and the environment impose constraints on the robot’s motion. For modeling such systems, we need to incorporate these constraints in the robot’s dynamic model. In this paper, we define the class of Task-based Constraints (TbC) to prove that the forward dynamic models of a constrained system obtained through the Projection-based Dynamics (PbD), and the Operational Space Formulation (OSF) are equivalent. In order to establish such equivalence, we first generalize the OSF to a rank deficient Jacobian. This generalization allow us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. We then reformulate the PbD constraint inertia matrix, generalizing all its previous distinct algebraic variations. We also analyse the condition number of different constraint inertia matrices, which affects the numerical stability of its inversion. Furthermore, we show that we can recover the operational space control with constraints from a multiple Task-based Constraint abstraction.\",\"PeriodicalId\":307591,\"journal\":{\"name\":\"Robotics: Science and Systems XV\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics: Science and Systems XV\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15607/RSS.2019.XV.036\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics: Science and Systems XV","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15607/RSS.2019.XV.036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
摘要
机器人系统的分析、设计和运动规划,往往依赖于其正、逆动力学模型。当执行涉及与环境交互的任务时,任务和环境都对机器人的运动施加了约束。为了对这样的系统建模,我们需要将这些约束纳入机器人的动态模型中。本文定义了一类基于任务的约束(Task-based Constraints, TbC),证明了基于投影动力学(Projection-based Dynamics, PbD)得到的约束系统的前向动态模型与操作空间公式(Operational Space Formulation, OSF)是等价的。为了建立这种等价性,我们首先将OSF推广到缺秩雅可比矩阵。这种推广允许我们在数值上处理冗余约束和奇异配置,而不必在这些配置附近使用不同的控制器。然后,我们重新制定PbD约束惯性矩阵,推广其所有以前不同的代数变化。分析了不同约束惯性矩阵的条件数对其反演数值稳定性的影响。此外,我们证明了我们可以从基于多个任务的约束抽象中恢复具有约束的操作空间控制。
Equivalence of the Projected Forward Dynamics and the Dynamically Consistent Inverse Solution
—The analysis, design, and motion planning of robotic systems, often relies on its forward and inverse dynamic models. When executing a task involving interaction with the environ- ment, both the task and the environment impose constraints on the robot’s motion. For modeling such systems, we need to incorporate these constraints in the robot’s dynamic model. In this paper, we define the class of Task-based Constraints (TbC) to prove that the forward dynamic models of a constrained system obtained through the Projection-based Dynamics (PbD), and the Operational Space Formulation (OSF) are equivalent. In order to establish such equivalence, we first generalize the OSF to a rank deficient Jacobian. This generalization allow us to numerically handle redundant constraints and singular configurations, without having to use different controllers in the vicinity of such configurations. We then reformulate the PbD constraint inertia matrix, generalizing all its previous distinct algebraic variations. We also analyse the condition number of different constraint inertia matrices, which affects the numerical stability of its inversion. Furthermore, we show that we can recover the operational space control with constraints from a multiple Task-based Constraint abstraction.