Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.093
Qiujie Lu, Nicholas Baron, A. B. Clark, Nicolás Rojas
We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilises a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers—jointly controlled by a single actuator—connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system, one for controlling the force exerted on objects by the fingers and two for changing the configuration of the palm. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base position changes with palm reconfigurations. We analyse the theoretical grasping workspace and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.
{"title":"The RUTH Gripper: Systematic Object-Invariant Prehensile In-Hand Manipulation via Reconfigurable Underactuation","authors":"Qiujie Lu, Nicholas Baron, A. B. Clark, Nicolás Rojas","doi":"10.15607/rss.2020.xvi.093","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.093","url":null,"abstract":"We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilises a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers—jointly controlled by a single actuator—connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system, one for controlling the force exerted on objects by the fingers and two for changing the configuration of the palm. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base position changes with palm reconfigurations. We analyse the theoretical grasping workspace and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128933819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.021
Jiani Li, W. Abbas, Mudassir Shabbir, X. Koutsoukos
—In this paper, we study the resilient diffusion prob- lem in a network of robots aiming to perform a task by optimizing a global cost function in a cooperative manner. In distributed diffusion, robots combine the information collected from their local neighbors and incorporate this aggregated information to update their states. If some robots are adversarial, this cooperation can disrupt the convergence of robots to the desired state. We propose a resilient aggregation rule based on the notion of centerpoint , which is a generalization of the median in the higher dimensional Euclidean space. Robots exchange their d dimensional state vectors with neighbors. We show that if a normal robot implements the centerpoint-based aggregation rule and has n neighbors, of which at most (cid:100) nd +1 (cid:101)− 1 are adversarial, then the aggregated state always lies in the convex hull of the states of the normal neighbors of the robot. Consequently, all normal robots implementing the distributed diffusion algorithm converge resiliently to the true target state. We also show that commonly used aggregation rules based on the coordinate-wise median and geometric median are, in fact, not resilient to certain attacks. We also numerically evaluate our results on mobile multirobot networks and demonstrate the cases where diffusion with the weighted average, coordinate-wise median, and geometric median-based aggregation rules fail to converge to the true target state, whereas diffusion with the centerpoint-based rule is resilient in the same scenario.
{"title":"Resilient Distributed Diffusion for Multi-Robot Systems Using Centerpoint","authors":"Jiani Li, W. Abbas, Mudassir Shabbir, X. Koutsoukos","doi":"10.15607/rss.2020.xvi.021","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.021","url":null,"abstract":"—In this paper, we study the resilient diffusion prob- lem in a network of robots aiming to perform a task by optimizing a global cost function in a cooperative manner. In distributed diffusion, robots combine the information collected from their local neighbors and incorporate this aggregated information to update their states. If some robots are adversarial, this cooperation can disrupt the convergence of robots to the desired state. We propose a resilient aggregation rule based on the notion of centerpoint , which is a generalization of the median in the higher dimensional Euclidean space. Robots exchange their d dimensional state vectors with neighbors. We show that if a normal robot implements the centerpoint-based aggregation rule and has n neighbors, of which at most (cid:100) nd +1 (cid:101)− 1 are adversarial, then the aggregated state always lies in the convex hull of the states of the normal neighbors of the robot. Consequently, all normal robots implementing the distributed diffusion algorithm converge resiliently to the true target state. We also show that commonly used aggregation rules based on the coordinate-wise median and geometric median are, in fact, not resilient to certain attacks. We also numerically evaluate our results on mobile multirobot networks and demonstrate the cases where diffusion with the weighted average, coordinate-wise median, and geometric median-based aggregation rules fail to converge to the true target state, whereas diffusion with the centerpoint-based rule is resilient in the same scenario.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121282210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.005
Tong Yang, J. V. Miró, Yue Wang, R. Xiong
—A theoretically complete solution to the optimal Non-revisiting Coverage Path Planning (NCPP) problem of any arbitrarily-shaped object with a non-redundant manipulator is proposed in this work. Given topological graphs of surface cells corresponding to feasible and continuous manipulator configura- tions, the scheme is aimed at ensuring optimality with respect to the number of surface discontinuities, and extends the existing provable solution attained for simply-connected configuration cell topologies to any arbitrary shape. This is typically classified through their genus, or the number of “holes” which appear increasingly as configurations are further constrained with the introduction of additional metrics for the task at hand, e.g. manipulability thresholds, clearance from obstacles, end-effector orientations, tooling force/torque magnitudes, etc. The novel contribution of this paper is to show that no matter what the resulting topological shapes from such quality cell constraints may be, the graph is finitely solvable, and a multi- stage iterative solver is designed to find all such optimal solutions.
{"title":"Non-revisiting Coverage Task with Minimal Discontinuities for Non-redundant Manipulators","authors":"Tong Yang, J. V. Miró, Yue Wang, R. Xiong","doi":"10.15607/rss.2020.xvi.005","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.005","url":null,"abstract":"—A theoretically complete solution to the optimal Non-revisiting Coverage Path Planning (NCPP) problem of any arbitrarily-shaped object with a non-redundant manipulator is proposed in this work. Given topological graphs of surface cells corresponding to feasible and continuous manipulator configura- tions, the scheme is aimed at ensuring optimality with respect to the number of surface discontinuities, and extends the existing provable solution attained for simply-connected configuration cell topologies to any arbitrary shape. This is typically classified through their genus, or the number of “holes” which appear increasingly as configurations are further constrained with the introduction of additional metrics for the task at hand, e.g. manipulability thresholds, clearance from obstacles, end-effector orientations, tooling force/torque magnitudes, etc. The novel contribution of this paper is to show that no matter what the resulting topological shapes from such quality cell constraints may be, the graph is finitely solvable, and a multi- stage iterative solver is designed to find all such optimal solutions.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"75 52","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114005448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.047
Bernardo Aceituno-Cabezas, Alberto Rodriguez
Given a desired object trajectory, how should a robot make contact to achieve it? This paper proposes a global optimization model for this problem with alternated-sticking contact, referred to as Contact-Trajectory Optimization. We achieve this by reasoning on simplified geometric environments with a quasi-dynamic relaxation of the physics. These relaxations are the result of approximating bilinear torque effects and deprecating high-order forces and impacts. Moreover, we apply convex approximations that retain the fundamental properties of rigid multi-contact interaction. As result, we derive a mixedinteger convex model that provides global optimality, infeasibility detection and convergence guarantees. This approach does not require seeding and accounts for the shapes of the object and environment. We validate this approach with extensive simulated and real-robot experiments, demonstrating its ability to quickly and reliably optimize multi-contact manipulation behaviors.
{"title":"A Global Quasi-Dynamic Model for Contact-Trajectory Optimization in Manipulation","authors":"Bernardo Aceituno-Cabezas, Alberto Rodriguez","doi":"10.15607/rss.2020.xvi.047","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.047","url":null,"abstract":"Given a desired object trajectory, how should a robot make contact to achieve it? This paper proposes a global optimization model for this problem with alternated-sticking contact, referred to as Contact-Trajectory Optimization. We achieve this by reasoning on simplified geometric environments with a quasi-dynamic relaxation of the physics. These relaxations are the result of approximating bilinear torque effects and deprecating high-order forces and impacts. Moreover, we apply convex approximations that retain the fundamental properties of rigid multi-contact interaction. As result, we derive a mixedinteger convex model that provides global optimality, infeasibility detection and convergence guarantees. This approach does not require seeding and accounts for the shapes of the object and environment. We validate this approach with extensive simulated and real-robot experiments, demonstrating its ability to quickly and reliably optimize multi-contact manipulation behaviors.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114413301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.055
Jonathan Spencer, Sanjiban Choudhury, Matt Barnes, Matt Schmittle, M. Chiang, P. Ramadge, S. Srinivasa
—Scalable robot learning from seamless human-robot interaction is critical if robots are to solve a multitude of tasks in the real world. Current approaches to imitation learning suffer from one of two drawbacks. On the one hand, they rely solely on off-policy human demonstration, which in some cases leads to a mismatch in train-test distribution. On the other, they burden the human to label every state the learner visits, rendering it impractical in many applications. We argue that learning interactively from expert interventions enjoys the best of both worlds. Our key insight is that any amount of expert feedback, whether by intervention or non-intervention, provides information about the quality of the current state, the optimality of the action, or both. We formalize this as a constraint on the learner’s value function, which we can efficiently learn using no regret, online learning techniques. We call our approach Expert Intervention Learning (EIL), and evaluate it on a real and simulated driving task with a human expert, where it learns collision avoidance from scratch with just a few hundred samples (about one minute) of expert control.
{"title":"Learning from Interventions: Human-robot interaction as both explicit and implicit feedback","authors":"Jonathan Spencer, Sanjiban Choudhury, Matt Barnes, Matt Schmittle, M. Chiang, P. Ramadge, S. Srinivasa","doi":"10.15607/rss.2020.xvi.055","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.055","url":null,"abstract":"—Scalable robot learning from seamless human-robot interaction is critical if robots are to solve a multitude of tasks in the real world. Current approaches to imitation learning suffer from one of two drawbacks. On the one hand, they rely solely on off-policy human demonstration, which in some cases leads to a mismatch in train-test distribution. On the other, they burden the human to label every state the learner visits, rendering it impractical in many applications. We argue that learning interactively from expert interventions enjoys the best of both worlds. Our key insight is that any amount of expert feedback, whether by intervention or non-intervention, provides information about the quality of the current state, the optimality of the action, or both. We formalize this as a constraint on the learner’s value function, which we can efficiently learn using no regret, online learning techniques. We call our approach Expert Intervention Learning (EIL), and evaluate it on a real and simulated driving task with a human expert, where it learns collision avoidance from scratch with just a few hundred samples (about one minute) of expert control.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125927831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.074
R. Stagsted, A. Vitale, Jonas Binz, Alpha Renner, L. B. Larsen, Yulia Sandamirskaya
: In this work, we present a spiking neural network(SNN) based PID controller on a neuromorphic chip. On-chipSNNs are currently being explored in low-power AI applications.Due to potentially ultra-low power consumption, low latency,and high processing speed, on-chip SNNs are a promising toolfor control of power-constrained platforms, such as UnmannedAerial Vehicles (UAV). To obtain highly efficient and fast end-to-end neuromorphic controllers, the SNN-based AI architecturesmust be seamlessly integrated with motor control. Towards thisgoal, we present here the first implementation of a fully neu-romorphic PID controller. We interfaced Intel’s neuromorphicresearch chip Loihi to a UAV, constrained to a single degreeof freedom. We developed an SNN control architecture usingpopulations of spiking neurons, in which each spike carriesinformation about the measured, control, or error value, definedby the identity of the spiking neuron. Using this sparse code,we realize a precise PID controller. The P, I
在这项工作中，我们提出了一个基于脉冲神经网络(SNN)的PID控制器。片上snn目前正在低功耗人工智能应用中进行探索。由于潜在的超低功耗、低延迟和高处理速度，片上snn是一种很有前途的工具，用于控制功率受限的平台，如无人机(UAV)。为了获得高效、快速的端到端神经形态控制器，基于snn的AI架构必须与电机控制无缝集成。为了实现这一目标，我们在这里提出了第一个完全新形态PID控制器的实现。我们将英特尔的神经形态研究芯片Loihi连接到一架无人机上，它只有一个自由度。我们开发了一种SNN控制架构，使用峰值神经元群体，其中每个峰值携带有关测量，控制或误差值的信息，由峰值神经元的身份定义。利用这种稀疏代码，我们实现了精确的PID控制器。P, I
{"title":"Towards neuromorphic control: A spiking neural network based PID controller for UAV","authors":"R. Stagsted, A. Vitale, Jonas Binz, Alpha Renner, L. B. Larsen, Yulia Sandamirskaya","doi":"10.15607/rss.2020.xvi.074","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.074","url":null,"abstract":": In this work, we present a spiking neural network(SNN) based PID controller on a neuromorphic chip. On-chipSNNs are currently being explored in low-power AI applications.Due to potentially ultra-low power consumption, low latency,and high processing speed, on-chip SNNs are a promising toolfor control of power-constrained platforms, such as UnmannedAerial Vehicles (UAV). To obtain highly efficient and fast end-to-end neuromorphic controllers, the SNN-based AI architecturesmust be seamlessly integrated with motor control. Towards thisgoal, we present here the first implementation of a fully neu-romorphic PID controller. We interfaced Intel’s neuromorphicresearch chip Loihi to a UAV, constrained to a single degreeof freedom. We developed an SNN control architecture usingpopulations of spiking neurons, in which each spike carriesinformation about the measured, control, or error value, definedby the identity of the spiking neuron. Using this sparse code,we realize a precise PID controller. The P, I","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116298150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.012
Peng Gao, Rui Guo, Hongsheng Lu, Hao Zhang
Correspondence identification is a critical capability for multi-robot collaborative perception, which allows a group of robots to consistently refer to the same objects in their own fields of view. Correspondence identification is a challenging problem, especially caused by non-covisible objects that cannot be observed by all robots and the uncertainty in robot perception, which have not been well studied yet in collaborative perception. In this work, we propose a principled approach of regularized graph matching that addresses perception uncertainties and non-covisible objects in a unified mathematical framework to perform correspondence identification in collaborative perception. Our method formulates correspondence identification as a graph matching problem in the regularized constrained optimization framework. We introduce a regularization term to explicitly address perception uncertainties by penalizing the object correspondences with a high uncertainty. We also design a second regularization term to explicitly address non-covisible objects by penalizing the correspondences built by the non-covisible objects. The formulated constrained optimization problem is difficulty to solve, because it is not convex and it contains regularization terms. Thus, we develop a new samplingbased algorithm to solve our formulated regularized constrained optimization problem. We evaluate our approach in the scenarios of connected autonomous driving and multi-robot coordination in simulations and using real robots. Experimental results show that our method is able to address correspondence identification under uncertainty and non-covisibility, and it outperforms the previous techniques and achieves the state-of-the-art performance.
{"title":"Regularized Graph Matching for Correspondence Identification under Uncertainty in Collaborative Perception","authors":"Peng Gao, Rui Guo, Hongsheng Lu, Hao Zhang","doi":"10.15607/rss.2020.xvi.012","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.012","url":null,"abstract":"Correspondence identification is a critical capability for multi-robot collaborative perception, which allows a group of robots to consistently refer to the same objects in their own fields of view. Correspondence identification is a challenging problem, especially caused by non-covisible objects that cannot be observed by all robots and the uncertainty in robot perception, which have not been well studied yet in collaborative perception. In this work, we propose a principled approach of regularized graph matching that addresses perception uncertainties and non-covisible objects in a unified mathematical framework to perform correspondence identification in collaborative perception. Our method formulates correspondence identification as a graph matching problem in the regularized constrained optimization framework. We introduce a regularization term to explicitly address perception uncertainties by penalizing the object correspondences with a high uncertainty. We also design a second regularization term to explicitly address non-covisible objects by penalizing the correspondences built by the non-covisible objects. The formulated constrained optimization problem is difficulty to solve, because it is not convex and it contains regularization terms. Thus, we develop a new samplingbased algorithm to solve our formulated regularized constrained optimization problem. We evaluate our approach in the scenarios of connected autonomous driving and multi-robot coordination in simulations and using real robots. Experimental results show that our method is able to address correspondence identification under uncertainty and non-covisibility, and it outperforms the previous techniques and achieves the state-of-the-art performance.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"102 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121043247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.077
Johannes Englsberger, Alexander Dietrich, George Mesesan, Gianluca Garofalo, C. Ott, A. Albu-Schaeffer
This work introduces the so-called Modular Passive Tracking Controller (MPTC), a generic passivity-based controller, which aims at independently fulfilling several subtask objectives. These are combined in a stack of tasks (SoT) that serves as a basis for the synthesis of an overall system controller . The corresponding analysis and controller design are based on Lyapunov theory. An important contribution of this work is the design of a specific optimization weighting matrix that ensures passivity of an overdetermined and thus conflicting task setup. The proposed framework is validated through simulations and experiments for both fixed-base and free-floating robots. I. I NTRODUCTION Simultaneous control of multiple tasks has emerged as a major research topic in robotic control. While initial works considered the simpler case of a single task and its nullspac e for a kinematically redundant robot, nowadays there exist several well established frameworks for handling multiple tasks with and without priorities. In the literature one may distinguish between works that solve the task coordination problem first on akinematic level , and works that formulate the controldirectly for the dynamics . Another important classification can be done based on the use of strict task priorities via hierarchic controllers as compared to controllers whic h apply asoft prioritizationvia task weighting. At the kinematics level , hierachical controllers based on either successive or augmented nullspace projections have been proposed in order to ensure a strict task hierarchy [18, 2]. For the handling of task singularities, a singulari ty robust inverse kinematics has been proposed [4]. However, t his singularity robust inverse destroys the strict task hierar chy and effectively generates a weighting among different tasks. Other frameworks handle multiple tasks at the dynamics level. The operational space approach has been extended in this direction with applications in humanoid robotics [22, 3]. Other Inverse Dynamics (ID) based controllers use hierarch ic quadratic programs (QP) [20, 11, 3]. Most of these works aim at a strict task decoupling. The presented work is inspired by the family of Inverse Dynamics based tracking controllers that softly trade off a se t of tasks (collected in a stack of tasks (SoT)) via a single weigh t d QP [13, 15, 10]. Such controllers are straightforward to wri te and stand out due to their high flexibility. Yet, compared to passivity-based approaches such as [19, 5, 12, 16, 6], they are less robust w.r.t. modeling errors and contact uncertai nties [10, 6]. This causes real-world issues such as vibrations, w hich are often addressed using heuristic approaches [13, 10]. Furthermore, the weighting based multi-objective control le in [3] and the strictly hierarchical passivity-based contr ller from [6] served as inspiration for this work. Similar to [3] w e use a QP to combine individual control actions from separate task space controllers. H
本文介绍了所谓的模块化无源跟踪控制器(MPTC)，这是一种通用的基于无源的控制器，旨在独立完成几个子任务目标。这些组合在任务堆栈(SoT)中，作为综合整个系统控制器的基础。相应的分析和控制器设计基于李亚普诺夫理论。这项工作的一个重要贡献是设计了一个特定的优化加权矩阵，以确保过度确定和冲突的任务设置的被动性。通过固定基座和自由浮动机器人的仿真和实验验证了所提出的框架。多任务同时控制已成为机器人控制领域的一个重要研究课题。虽然最初的工作考虑了单个任务及其运动学冗余机器人的零空间e的简单情况，但现在有几个很好的框架来处理有优先级和没有优先级的多个任务。在文献中，人们可以区分首先在运动水平上解决任务协调问题的作品和直接为动力学制定控制的作品。另一个重要的分类可以基于使用严格的任务优先级通过分层控制器来完成，而不是通过任务加权应用软优先级的控制器。在运动学层面，已经提出了基于连续或增广零空间投影的分层控制器，以确保严格的任务层次[18,2]。针对任务奇异性的处理，提出了一种奇异鲁棒逆运动学方法。然而，这种奇异鲁棒逆破坏了严格的任务层次结构，有效地产生了不同任务之间的权值。其他框架在动态级别处理多个任务。操作空间方法在人形机器人中的应用已经向这个方向扩展[22,3]。其他基于逆动力学(ID)的控制器使用层次二次规划(QP)[20,11,3]。这些工作大多以严格的任务解耦为目标。所提出的工作受到基于逆动力学的跟踪控制器家族的启发，该控制器通过单个权重t d QP轻轻地权衡了一系列任务(收集在任务堆栈中(SoT))[13,15,10]。这样的控制器很容易编写，并且由于其高度的灵活性而脱颖而出。然而，与基于被动的方法(如[19,5,12,16,6])相比，它们在w.r.t.建模误差和接触不确定性方面的鲁棒性较差[10,6]。这导致了现实世界的问题，如振动，这通常使用启发式方法来解决[13,10]。此外，[3]中基于权的多目标控制器和[6]中基于严格分层的无源控制器为本工作提供了灵感。与[3]类似，我们使用QP来组合来自不同任务空间控制器的单个控制动作。然而，在[3]中，每个单独的控制动作都是基于ID计算的，单位矩阵作为期望的惯性(反馈线性化)。相比之下，这里提出的i个单独的任务控制器使用了被动的概念，避免了惯性形成，即我们的目标是每个任务[19]的PD+闭环。与[6]相比，[6]也保留了自然惯性，我们使用加权QP公式(软优先级)，这允许我们混合任意麻木的不同任务，并在某些情况下(例如，当单个任务变得单一时)表现得不那么激进。在这项工作中，我们推导了一种基于名义上被动子任务控制器的控制体系结构，即所谓的modar被动跟踪控制器(MPTC)。它们通过一堆任务进行组合和交换，这些任务分别通过单个加权伪逆或QP来解决。控制框架结合了逆动力学控制器和基于被动的控制器的优点，即:易于实现和使用，任务空间跟踪能力，被动和接触鲁棒性，以及自然冗余处理。相应的稳定性分析基于李亚普诺夫理论。在无冲突情况下，总体控制器是渐近稳定的无源控制器。所提出的工作的一个重要贡献是推导出一个特定的优化权重，即使在过度确定(即冲突)的情况下也能额外保持被动。对于竞争任务和相应的不一致任务引用，多次仿真证明了MPTC在跟踪情况下的稳定性和鲁棒性，但目前还没有正式的稳定性证明。本文组织如下:第二节在任务级推导了模块化无源跟踪控制器(MPTC)，第三节给出了相应的整体闭环分析和控制器推导。 第四节将MPTC与逆动力学(ID)和基于PD+的控制器进行比较，并提出了广泛的可能解耦水平。第五节给出了固定基座和自由漂浮机器人的仿真结果，第六节对本文进行了总结。2模块化无源跟踪控制(MPTC)的推导本工作考虑了多个任务，每个任务都有自己的单独目标。为了满足单任务目标，本节推导出模块化被动跟踪控制器(MPTC)，并在第三节a中组合成不同的整体控制器。机器人一般模型机器人一般运动方程可以写为M(q) q´+ C(q, q) q´+ τg(q) = τ，(1)其中q∈Rn表示广义坐标1,M(q)， C(q, q)和τg(q)分别为惯性矩阵，科里奥利矩阵和离心矩阵，重力力矩2。τ = S (τ j + τint) + L T all wall} {{} τext(2)表示广义力。它们由关节电机扭矩τ j和作用于机器人关节(例如关节摩擦)的内部摄动扭矩τint组成，它们都通过关节选择矩阵xs,3和外部扭矩τext映射到d到τ。后者由所有扳手wall = [wT 1，…]，w T nL]T作用于机器人连杆上，通过连杆JacobiansLall = [L1，…][T]T。(2)的单元素将在第III-E节中使用，在接下来的g中，我们将简单地使用τ来表示任意的广义力。广义加速度q´的解(1)得到q´= M−1 (τ−C q³−τg)
{"title":"MPTC - Modular Passive Tracking Controller for stack of tasks based control frameworks","authors":"Johannes Englsberger, Alexander Dietrich, George Mesesan, Gianluca Garofalo, C. Ott, A. Albu-Schaeffer","doi":"10.15607/rss.2020.xvi.077","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.077","url":null,"abstract":"This work introduces the so-called Modular Passive Tracking Controller (MPTC), a generic passivity-based controller, which aims at independently fulfilling several subtask objectives. These are combined in a stack of tasks (SoT) that serves as a basis for the synthesis of an overall system controller . The corresponding analysis and controller design are based on Lyapunov theory. An important contribution of this work is the design of a specific optimization weighting matrix that ensures passivity of an overdetermined and thus conflicting task setup. The proposed framework is validated through simulations and experiments for both fixed-base and free-floating robots. I. I NTRODUCTION Simultaneous control of multiple tasks has emerged as a major research topic in robotic control. While initial works considered the simpler case of a single task and its nullspac e for a kinematically redundant robot, nowadays there exist several well established frameworks for handling multiple tasks with and without priorities. In the literature one may distinguish between works that solve the task coordination problem first on akinematic level , and works that formulate the controldirectly for the dynamics . Another important classification can be done based on the use of strict task priorities via hierarchic controllers as compared to controllers whic h apply asoft prioritizationvia task weighting. At the kinematics level , hierachical controllers based on either successive or augmented nullspace projections have been proposed in order to ensure a strict task hierarchy [18, 2]. For the handling of task singularities, a singulari ty robust inverse kinematics has been proposed [4]. However, t his singularity robust inverse destroys the strict task hierar chy and effectively generates a weighting among different tasks. Other frameworks handle multiple tasks at the dynamics level. The operational space approach has been extended in this direction with applications in humanoid robotics [22, 3]. Other Inverse Dynamics (ID) based controllers use hierarch ic quadratic programs (QP) [20, 11, 3]. Most of these works aim at a strict task decoupling. The presented work is inspired by the family of Inverse Dynamics based tracking controllers that softly trade off a se t of tasks (collected in a stack of tasks (SoT)) via a single weigh t d QP [13, 15, 10]. Such controllers are straightforward to wri te and stand out due to their high flexibility. Yet, compared to passivity-based approaches such as [19, 5, 12, 16, 6], they are less robust w.r.t. modeling errors and contact uncertai nties [10, 6]. This causes real-world issues such as vibrations, w hich are often addressed using heuristic approaches [13, 10]. Furthermore, the weighting based multi-objective control le in [3] and the strictly hierarchical passivity-based contr ller from [6] served as inspiration for this work. Similar to [3] w e use a QP to combine individual control actions from separate task space controllers. H","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128912607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.008
Brian Reily, Christopher M. Reardon, Hao Zhang
Effective multi-agent teaming requires knowledgeable robots to have the capability of influencing their teammates. Robots are able to possess information that their human and other agent teammates do not, such as by scouting ahead in dangerous areas. To work as an effective team, robots must be able to influence their teammates when necessary and adapt to changing situations in order to move to goal positions that only they may be aware of, while remaining connected as a team. In this paper, we propose the problem of multiple robot teammates tasked with leading a multi-agent team to multiple goal positions while maintaining the ability to communicate with one another. We define utilities of making progress towards goals, maintaining communications with followers, and maintaining communications with fellow leaders. In addition, we introduce a novel regularized optimization formulation that balances these utilities and utilizes structured sparsity inducing norms to focus the leaders’ attention on specific goals and followers over time. The dynamically learned utility allows our approach to generate an action for each leader at each time step, which allows the leaders to reach goals without sacrificing communication. We show through extensive synthetic and high-fidelity simulations that our method effectively enables multiple robotic leaders to guide a multi-agent team to different goals while maintaining communication.
{"title":"Leading Multi-Agent Teams to Multiple Goals While Maintaining Communication","authors":"Brian Reily, Christopher M. Reardon, Hao Zhang","doi":"10.15607/rss.2020.xvi.008","DOIUrl":"https://doi.org/10.15607/rss.2020.xvi.008","url":null,"abstract":"Effective multi-agent teaming requires knowledgeable robots to have the capability of influencing their teammates. Robots are able to possess information that their human and other agent teammates do not, such as by scouting ahead in dangerous areas. To work as an effective team, robots must be able to influence their teammates when necessary and adapt to changing situations in order to move to goal positions that only they may be aware of, while remaining connected as a team. In this paper, we propose the problem of multiple robot teammates tasked with leading a multi-agent team to multiple goal positions while maintaining the ability to communicate with one another. We define utilities of making progress towards goals, maintaining communications with followers, and maintaining communications with fellow leaders. In addition, we introduce a novel regularized optimization formulation that balances these utilities and utilizes structured sparsity inducing norms to focus the leaders’ attention on specific goals and followers over time. The dynamically learned utility allows our approach to generate an action for each leader at each time step, which allows the leaders to reach goals without sacrificing communication. We show through extensive synthetic and high-fidelity simulations that our method effectively enables multiple robotic leaders to guide a multi-agent team to different goals while maintaining communication.","PeriodicalId":231005,"journal":{"name":"Robotics: Science and Systems XVI","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130863157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-07-12DOI: 10.15607/rss.2020.xvi.052
Chao Liu, Sencheng Yu, Mark H. Yim
—Self-reconfigurable modular robots are composed of many modules that can be rearranged into various structures with respect to different activities and tasks. The variable topology truss (VTT) is a class of modular truss robot. These robots are able to change their shape by not only controlling joint positions which is similar to robots with fixed morphologies, but also reconfiguring the connections among modules in order to change their morphologies. Motion planning for VTT robots is difficult due to their non-fixed morphologies, high-dimensionality, potential for self-collision, and complex motion constraints. In this paper, a new motion planning algorithm to dramatically alter the structure of a VTT is presented, as well as some comparative tests to show its effectiveness.
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