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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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Solving Colored Traveling Salesman Problem via Multi-neighborhood Simulated Annealing Search 18

Solving Colored Traveling Salesman Problem via Multi-neighbo...

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18th IeeE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Zhou, Yangming Xu, Wenqiang Fu, Zhang-Hua Zhou, Mengchu East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Macau Institute of Systems Engineering Macau University of Science and Technology 999078 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China Institute of Robotics and Intelligent Manufacturing The Chinese University of Hong Kong Shenzhen Shenzhen518172 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Department of Cyber-Physical Systems St. Petersburg State Marine Technical University St. Petersburg198262 Russia

ISBN: (纸本)9781665440486

A colored traveling salesman problem (CTSP) is an important variant of the well-known multiple traveling salesman problem, which uses colors to differentiate salesmen's accessibility to individual cities to be visited. As a highly useful model for some complex scheduling problems, CTSP is NP-hard. A Multi-neighborhood Simulated Annealing Search (MSAS) approach is proposed to solve it in this paper. Starting from an initial solution, it iterates through two complementary neighborhoods: intra-route and inter-route neighborhoods. Experiments on three groups of 60 widely-used benchmark instances show that it achieves highly competitive performance compared to state-of-the-art algorithms. Moreover, MSAS can be integrated into other search methods to further improve performance, which is demonstrated by using a recently proposed iterated two-phase local search. © 2021 IeeE.

关键词： Traveling salesman problem

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Reference adaptive impedance control: a new paradigm for event-based robotic and telerobotic control

Reference adaptive impedance control: a new paradigm for eve...

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International Conference on Advanced robotics (ICAR)

作者： Sukhan Lee Ming-Feng Jean Jong-Oh Park Chong-Won Lee Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Department of Computer Science-EE Systems Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA Korea Institute of Science and Technology Seoul South Korea

ISBN: (纸本)0780341600

A new paradigm of event-based robotic and telerobotic control, referred to here as the reference adaptive impedance control, is presented for partially constrained robotic and telerobotic tasks under uncertain or unknown environmental constraints. The reference adaptive impedance control determines the desired next state adaptively, in such a way as to follow the optimal path from the current state, where the optimal path is generated with the environmental constraints updated through time. The embedded impedance control provides a mechanism for collecting more complete information on the environment. The reference adaptive impedance control also serves as a powerful tool for implementing shared control in teleoperation. A case study of robotic and telerobotic soil trenching is given with simulations.

关键词： Programmable control Adaptive control Impedance Telerobotics Robot control intelligent robots Trajectory Robot kinematics Optimal control Strain control

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Haptic terrain classification for legged robots

Haptic terrain classification for legged robots

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IeeE International Conference on robotics and Automation (ICRA)

作者： Mark A. Hoepflinger C. David Remy Marco Hutter Luciano Spinello Roland Siegwart Autonomous Systems Lab Institute of Robotics and Intelligent Systems Swiss Federal Institute of Technology ETH Zurich Zurich Switzerland Social Robotics Lab Department of Computer Science University of Freiburg Freiberg Germany

In this paper, we are presenting a method to estimate terrain properties (such as small-scale geometry or surface friction) to improve the assessment of stability and the guiding of foot placement of legged robots in rough terrain. Haptic feedback, expressed through joint motor currents and ground contact force measurements that arises when prescribing a predefined motion was collected for a variety of ground samples (four different shapes and four different surface properties). Features were extracted from this data and used for training and classification by a multiclass AdaBoost machine learning algorithm. In a single leg testbed, the algorithm could correctly classify about 94% of the terrain shapes, and about 73% of the surface samples.

关键词： Haptic interfaces Legged locomotion Rough surfaces Surface roughness Machine learning algorithms Computational geometry Friction Stability Foot Force feedback

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A real-time dual-arm collision avoidance algorithm for assembly

A real-time dual-arm collision avoidance algorithm for assem...

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IeeE International Symposium on Assembly and Manufacturing (ISAM)

作者： S. Lee H. Moradi Chunsik Yi Department of Computer Science-EE Systems Institute for Robotics and lntelligent Systems University of Southern California Los Angeles CA Jet Propulsion Laboratory California Institute of Technology Pasadena CA

Multi-robot workcells provide efficiency of space, time, and materials in assembly. However this advantage cannot be fully utilized unless an efficient offline/online collision-free trajectory planner is in charge of controlling the arms. In this paper a new algorithm which provides the online and real-time generation of dual-arm collision-free trajectories has been established. The algorithm is capable of generating dual-arm collision-free trajectories in real-time despite that the initial configurations of two arms are not synchronized. The key component of the algorithm is the virtual road map (VRM) which gives the best collision-free motion at each given arm configuration.

关键词： Collision avoidance Arm Trajectory Robotic assembly Assembly systems Delay Mobile robots Transportation Automatic control Motion control

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A pivoting gripper for feeding industrial parts

A pivoting gripper for feeding industrial parts

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IeeE International Conference on robotics and Automation (ICRA)

作者： B. Carlisle K. Goldberg A. Rao J. Wiegley Adept Technology Inc. San Jose CA USA Institute for Robotics and Intelligent Systems Computer Science Department University of Southern California Los Angeles CA USA Deparment of Computer Science University of Utrecht Utrecht Netherlands Institute for Robotics and Intelligent Systems Computer Science Department University of Southern California USA

To be cost effective and highly precise, many industrial assembly robots have only four degrees of freedom (DOF) plus a binary pneumatic gripper. Such robots commonly permit parts to be rotated only about a vertical axis. However it is often necessary to reorient parts about other axes prior to assembly. In this paper the authors describe a way to orient parts about an arbitrary axis by introducing a rotating bearing between the jaws of a simple gripper. Based on this mechanism, the authors are developing a rapidly configurable vision-based system for feeding parts. In this system, a camera determines initial part pose; the robot then reorients the part to achieve a desired final pose. The authors have implemented a prototype version in their laboratory using a commercially-available robot system.< >

关键词： Grippers Costs Robotic assembly Robot vision systems intelligent robots Machine vision Gravity Service robots robotics and automation intelligent systems

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A p300 model for cerebot - A mind-controlled humanoid robot 2

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2nd International Conference on Robot Intelligence Technology and Applications, RiTA 2013

作者： Li, Mengfan Li, Wei Zhao, Jing Meng, Qinghao Zeng, Ming Chen, Genshe Institute of Robotics and Autonomous Systems School of Electrical Engineering and Automation Tianjin University Tianjin300072 China Department of Computer & Electrical Engineering and Computer Science California State University BakersfieldCA93311 United States Intelligent Fusion Technology Inc. GermantownMD20876 United States

ISBN: (纸本)9783319055817

In this paper, we present a P300 model for control of Cerebot - a mind-controlled humanoid robot, including a procedure of acquiring P300 signals, topographical distribution analysis of P300 signals, and a classification approach to identifying subjects’ mental activities regarding robot-walking behavior. We design two groups of image contexts to visually stimulate subjects when acquiring neural signals that are used to control a simulated or real NAO robot. Our study shows that the group of contexts using images of robot behavior delivers better performance. © Springer International Publishing Switzerland 2014.

关键词： Anthropomorphic robots

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Use of monocular groupings and occlusion analysis in a hierarchical stereo system

Use of monocular groupings and occlusion analysis in a hiera...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： R.C. Chung R. Nevatia Institute for Robotics and Intelligent Systems Department of Electrical Engineering and Computer Science University of Southern California Los Angeles CA USA

A hierarchical stereo system is described that uses structural descriptions up to the surface level. Surface descriptions are computed from monocular images, by using a perceptual grouping technique. Occlusion can be a major problem in stereo analysis and is often not treated explicitly. An analysis is presented of occlusion effects in stereo, and it is shown how structural descriptions can be used to deal with them. Experimental results are given for scenes with curved objects and significant occlusions.< >

关键词： Layout intelligent robots Surface treatment Stereo image processing Image reconstruction intelligent systems intelligent structures Hierarchical systems Monitoring Contracts

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Efficient learning of robust quadruped bounding usingpretrained neural networks

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IET Cyber-systems and robotics 2022年第4期4卷 331-338页

作者： Zhicheng Wang Anqiao Li Yixiao Zheng Anhuan Xie Zhibin Li Jun Wu Qiuguo Zhu Institute of Cyber-Systems and Control Zhejiang UniversityZhejiangChina Intelligent Robot Research Center Zhejiang LabZhejiangChina Department of Computer Science University College LondonLondonUK State Key Laboratory of Industrial Control Technology Zhejiang UniversityZhejiangChina

Bounding is one of the important gaits in quadrupedal locomotion for negotiating *** authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body *** authors first pretrained the neural network(NN)based on data from a robot operated by conventional model-based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL).In particular,the authors designed a reward function considering contact points and phases to enforce the gait symmetry and periodicity,which improved the bounding *** NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully.A variety of environments are presented both indoors and outdoors with the authors’*** authors’approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven *** cover image is based on the Research Article Efficient learning of robust quadruped bounding using pretrained neural networks by Zhicheng Wang et al.,https://***/10.1049/csy2.12062.

关键词： legged locomotion reinforcement learning robot learning

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Knowledge Engineering with Big Data

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IeeE intelligent systems 2015年第5期30卷 46-55页

作者： Wu, Xindong Chen, Huanhuan Wu, Gongqing Liu, Jun Zheng, Qinghua He, Xiaofeng Zhou, Aoying Zhao, Zhong-Qiu Wei, Bifang Gao, Ming Li, Yang Zhang, Qiping Zhang, Shichao Lu, Ruqian Zheng, Nanning School of Computer Science and Information Engineering Hefei University of Technology China University of Vermont United States University of Edinburgh School of Computer Science United Kingdom School of Computer Science University of Science and Technology of China China Department of Computer Science Xi'An Jiaotong University China Institute for Data Science and Engineering East China Normal University China Pennsylvania State University United States Fudan University China Department of Computer Science Zhejiang Gongshang University China Deakin University Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University China Keio University Japan

In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation. © 2001-2011 IeeE.

关键词： intelligent systems

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