International Journal of Robotics Research最新文献_第9页

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling GRSTAPS：图形递归的同时任务分配、规划和调度

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-11-09 DOI: 10.1177/02783649211052066

Andrew Messing, Glen Neville, S. Chernova, S. Hutchinson, H. Ravichandar

{"title":"GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling","authors":"Andrew Messing, Glen Neville, S. Chernova, S. Hutchinson, H. Ravichandar","doi":"10.1177/02783649211052066","DOIUrl":"https://doi.org/10.1177/02783649211052066","url":null,"abstract":"Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47403327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint NTU VIRAL:从飞行器视角的视觉-惯性测距-激光雷达数据集

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-11-06 DOI: 10.1177/02783649211052312

Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu, T. Nguyen, Lihua Xie

{"title":"NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint","authors":"Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu, T. Nguyen, Lihua Xie","doi":"10.1177/02783649211052312","DOIUrl":"https://doi.org/10.1177/02783649211052312","url":null,"abstract":"In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_dataset/.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47219948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

A large-scale dataset for indoor visual localization with high-precision ground truth 基于高精度地面真值的大规模室内视觉定位数据集

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-10-26 DOI: 10.1177/02783649211052064

Yuchen Liu, Wei Gao, Zhanyi Hu

引用次数: 4

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper 通过可重构欠驱动的系统对象不变在手操作:介绍RUTH夹具

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-10-22 DOI: 10.1177/02783649211048929

Qiujie Lu, Nicholas Baron, A. B. Clark, Nicolás Rojas

{"title":"Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper","authors":"Qiujie Lu, Nicholas Baron, A. B. Clark, Nicolás Rojas","doi":"10.1177/02783649211048929","DOIUrl":"https://doi.org/10.1177/02783649211048929","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 utilizes 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 in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. 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 a 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 positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping 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":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42035977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Constrained stochastic optimal control with learned importance sampling: A path integral approach 具有学习重要性抽样的约束随机最优控制:一种路径积分方法

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-10-12 DOI: 10.1177/02783649211047890

Jan Carius, René Ranftl, Farbod Farshidian, M. Hutter

{"title":"Constrained stochastic optimal control with learned importance sampling: A path integral approach","authors":"Jan Carius, René Ranftl, Farbod Farshidian, M. Hutter","doi":"10.1177/02783649211047890","DOIUrl":"https://doi.org/10.1177/02783649211047890","url":null,"abstract":"Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46117700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Real-time recognition of team behaviors by multisensory graph-embedded robot learning 基于多感官图嵌入机器人学习的团队行为实时识别

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-09-23 DOI: 10.1177/02783649211043155

Brian Reily, Peng Gao, Fei Han, Hua Wang, Hao Zhang

{"title":"Real-time recognition of team behaviors by multisensory graph-embedded robot learning","authors":"Brian Reily, Peng Gao, Fei Han, Hua Wang, Hao Zhang","doi":"10.1177/02783649211043155","DOIUrl":"https://doi.org/10.1177/02783649211043155","url":null,"abstract":"Awareness of team behaviors (e.g., individual activities and team intents) plays a critical role in human–robot teaming. Autonomous robots need to be aware of the overall intent of the team they are collaborating with in order to effectively aid their human peers or augment the team’s capabilities. Team intents encode the goal of the team, which cannot be simply identified from a collection of individual activities. Instead, teammate relationships must also be encoded for team intent recognition. In this article, we introduce a novel representation learning approach to recognizing team intent awareness in real-time, based upon both individual human activities and the relationship between human peers in the team. Our approach formulates the task of robot learning for team intent recognition as a joint regularized optimization problem, which encodes individual activities as latent variables and represents teammate relationships through graph embedding. In addition, we design a new algorithm to efficiently solve the formulated regularized optimization problem, which possesses a theoretical guarantee to converge to the optimal solution. To evaluate our approach’s performance on team intent recognition, we test our approach on a public benchmark group activity dataset and a multisensory underground search and rescue team behavior dataset newly collected from robots in an underground environment, as well as perform a proof-of-concept case study on a physical robot. The experimental results have demonstrated both the superior accuracy of our proposed approach and its suitability for real-time applications on mobile robots.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44115004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Autonomous navigation of underactuated bipedal robots in height-constrained environments 高度受限环境下欠驱动两足机器人的自主导航

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-09-13 DOI: 10.1177/02783649231187670

Zhongyu Li, Jun Zeng, Shuxiao Chen, K. Sreenath

{"title":"Autonomous navigation of underactuated bipedal robots in height-constrained environments","authors":"Zhongyu Li, Jun Zeng, Shuxiao Chen, K. Sreenath","doi":"10.1177/02783649231187670","DOIUrl":"https://doi.org/10.1177/02783649231187670","url":null,"abstract":"Navigating a large-scaled robot in unknown and cluttered height-constrained environments is challenging. Not only is a fast and reliable planning algorithm required to go around obstacles, the robot should also be able to change its intrinsic dimension by crouching in order to travel underneath height-constrained regions. There are few mobile robots that are capable of handling such a challenge, and bipedal robots provide a solution. However, as bipedal robots have nonlinear and hybrid dynamics, trajectory planning while ensuring dynamic feasibility and safety on these robots is challenging. This paper presents an end-to-end autonomous navigation framework which leverages three layers of planners and a variable walking height controller to enable bipedal robots to safely explore height-constrained environments. A vertically actuated spring-loaded inverted pendulum (vSLIP) model is introduced to capture the robot’s coupled dynamics of planar walking and vertical walking height. This reduced-order model is utilized to optimize for long-term and short-term safe trajectory plans. A variable walking height controller is leveraged to enable the bipedal robot to maintain stable periodic walking gaits while following the planned trajectory. The entire framework is tested and experimentally validated using a bipedal robot Cassie. This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42243280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Representation, learning, and planning algorithms for geometric task and motion planning 几何任务和运动规划的表示、学习和规划算法

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-09-08 DOI: 10.1177/02783649211038280

Beomjoon Kim, Luke Shimanuki, L. Kaelbling, Tomas Lozano-Perez

{"title":"Representation, learning, and planning algorithms for geometric task and motion planning","authors":"Beomjoon Kim, Luke Shimanuki, L. Kaelbling, Tomas Lozano-Perez","doi":"10.1177/02783649211038280","DOIUrl":"https://doi.org/10.1177/02783649211038280","url":null,"abstract":"We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42922517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Hamiltonian coordination primitives for decentralized multiagent navigation 分散多智能体导航的哈密顿协调基元

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-08-13 DOI: 10.1177/02783649211037731

Christoforos Mavrogiannis, Ross A. Knepper

{"title":"Hamiltonian coordination primitives for decentralized multiagent navigation","authors":"Christoforos Mavrogiannis, Ross A. Knepper","doi":"10.1177/02783649211037731","DOIUrl":"https://doi.org/10.1177/02783649211037731","url":null,"abstract":"We focus on decentralized navigation among multiple non-communicating agents in continuous domains without explicit traffic rules, such as sidewalks, hallways, or squares. Following collision-free motion in such domains requires effective mechanisms of multiagent behavior prediction. Although this prediction problem can be shown to be NP-hard, humans are often capable of solving it efficiently by leveraging sophisticated mechanisms of implicit coordination. Inspired by the human paradigm, we propose a novel topological formalism that explicitly models multiagent coordination. Our formalism features both geometric and algebraic descriptions enabling the use of standard gradient-based optimization techniques for trajectory generation but also symbolic inference over coordination strategies. In this article, we contribute (a) HCP (Hamiltonian Coordination Primitives), a novel multiagent trajectory-generation pipeline that accommodates spatiotemporal constraints formulated as symbolic topological specifications corresponding to a desired coordination strategy; (b) HCPnav, an online planning framework for decentralized collision avoidance that generates motion by following multiagent trajectory primitives corresponding to high-likelihood, low-cost coordination strategies. Through a series of challenging trajectory-generation experiments, we show that HCP outperforms a trajectory-optimization baseline in generating trajectories of desired topological specifications in terms of success rate and computational efficiency. Finally, through a variety of navigation experiments, we illustrate the efficacy of HCPnav in handling challenging multiagent navigation scenarios under homogeneous or heterogeneous agents across a series of environments of different geometry.","PeriodicalId":54942,"journal":{"name":"International Journal of Robotics Research","volume":null,"pages":null},"PeriodicalIF":9.2,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42326419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Special Issue on the Thirteenth Workshop on the Algorithmic Foundations of Robotics (WAFR) 2018 2018年第13届机器人算法基础研讨会特刊

IF 9.2 1区计算机科学

International Journal of Robotics Research Pub Date : 2021-08-13 DOI: 10.1177/02783649211038146

Marco Morales, Lydia Tapia, Gildardo Sánchez-Ante, S. Hutchinson

引用次数: 0