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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Angelica I. Aviles Samar M. Alsaleh Pilar Sobrevilla Alicia Casals Intelligent Robotics and Systems Group Universitat Politecnica de Cataluya Barcelona Spain Department of Computer Science George Washington University Washington DC U.S.A. Department of Applied Mathematics II Universitat Politecnica de Cataluya Barcelona Spain Institute for Bioengineering of Catalonia Universitat Politecnica de Cataluya Barcelona Spain

The lack of force feedback is considered one of the major limitations in Robot Assisted Minimally Invasive Surgeries. Since add-on sensors are not a practical solution for clinical environments, in this paper we present a force estimation approach that starts with the reconstruction of a 3D deformation structure of the tissue surface by minimizing an energy functional. A Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) based architecture is then presented to accurately estimate the applied forces. According to the results, our solution offers long-term stability and shows a significant percentage of accuracy improvement, ranging from about 54% to 78%, over existing approaches.

关键词： computer architecture Microprocessors Force Surgery Robot sensing systems Estimation

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Latent space policy search for robotics

Latent space policy search for robotics

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2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014

作者： Luck, Kevin Sebastian Neumann, Gerhard Berger, Erik Peters, Jan Amor, Heni Ben Department of Computer Science Technische Universität Darmstadt Darmstadt64289 Germany Department of Mathematics and Computer Science Technische Universität Bergakademie Freiberg Freiberg09599 Germany Institute for Robotics and Intelligent Machines Georgia Institute of Technology GA30332 United States Max Planck Institute for Intelligent Systems Tübingen72076 Germany

ISBN: (纸本)9781479969340

Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies. In contrast to previous attempts at combining reinforcement learning and dimensionality reduction, our approach does not perform dimensionality reduction as a preprocessing step but naturally combines it with policy search. Our evaluations show that the new approach outperforms existing algorithms for learning motor skills with high-dimensional robots. © 2014 IEEE.

关键词： Reinforcement learning

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The discrete minimum constraint removal motion planning problem

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AIP Conference Proceedings 2015年第1期1648卷

作者： Anna Gorbenko Vladimir Popov Ural Federal University Department of Intelligent Systems and Robotics Mathematics and Computer Science Institute Lenin st. 51 620083 Ekaterinburg Russian Federation

Many planning problems for robots are of considerable interest. In this paper, we consider the discrete minimum constraint removal motion planning problem that can be used for a motion planning formulation with explanations for failure. We consider an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

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The problem of robot swarms control with only global signals

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AIP Conference Proceedings 2015年第1期1648卷

Problems of swarm robotics are extensively studied in contemporary robotics. In this paper, we consider the problem of robot swarms control with only global signals. We propose an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. For different satisfiability algorithms, we present the results of computational experiments.

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Power-continuous synchronisation of oscillators: A novel, energy-free way to synchronise dynamical systems

Power-continuous synchronisation of oscillators: A novel, en...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Gerrit A. Folkertsma Arjan J. van der Schaft Stefano Stramigioli Robotics and Mechatronics group University of Twente Johann Bernoulli Institute for Mathematics and Computer Science University of Groningen

Synchronisation is an essential part of many controlled dynamical systems, in particular in the limb motion of legged robots. In this paper we introduce a novel control strategy that allows synchronisation of two oscillators without using any external power, but by modulating the power flow between the two oscillators. We then derive a separate energy-level controller that regulates the oscillation amplitude, again without changing the system's total energy. Finally, we show that the strategy works on a realistic mechanical system, by synchronising the phase difference and apex height of two bouncing masses.

关键词： Oscillators Springs Synchronization Energy states Robots Mathematical model Control systems

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Variable Impedance Control for Aerial Interaction

Variable Impedance Control for Aerial Interaction

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Abeje Y. Mersha Stefano Stramigioli Raffaella Carloni CTIT Institute Faculty of Electrical Engineering Mathematics and Computer Science Robotics and Mechatronics group University of Twente The Netherlands

ISBN: (纸本)9781479969357

This paper presents a versatile control architecture for aerial robots in interactive tasks. The control architecture is characterized by its unique capability of varying the apparent impedance of the controlled aerial robot as well as the interaction force, when in contact. This work finds its way in various applications where different impedance and interaction force controllers provide high task performances as well as safety. The feasibility and effectiveness of the proposed controller are demonstrated by experimental results preformed on a quadrotor aerial robot.

关键词： Force Impedance Robot kinematics Damping Aerodynamics

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Automatic Semantic Waypoint Mapping Applied to Autonomous Vehicles

Automatic Semantic Waypoint Mapping Applied to Autonomous Ve...

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IEEE Latin American robotics Symposium, LARS

作者： Matheus Zoccoler Patrick Y. Shinzato Alberto Hata Denis F. Wolf Mobile Robotics Laboratory Institute of Mathematics and Computer Science University of Sao Paulo - ICMC-USp Sao Carlos SP Brazil

Road network maps have been used for autonomous vehicle path planning. These maps are basically formed by GPS waypoints and can contain semantic information about the environment to help following traffic codes. This paper describes a novel method for automatic construction of a waypoint map containing semantic information about roads. The collected GPS points are stored into flexible waypoint data structures that can represent any relevant information for vehicle navigation. The mapping method also reduces the amount of waypoints by recognizing and converting them into traffic structures. The resulting waypoint map is stored in a text file which is both human and machine-readable. This work makes part of Carina II platform, an autonomous vehicle under development in the Mobile robotics Laboratory (LRM) - ICMC/USP. Tests were conducted in urban environment and the resulting maps were consistent when compared to publicly available satellite maps.

关键词： Roads Global Positioning System Semantics Equations Vehicles Robots Libraries

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Graph SLAM with Signed Distance Function Maps on a Humanoid Robot

Graph SLAM with Signed Distance Function Maps on a Humanoid ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Rene Wagner Udo Frese Berthold Bauml DLR Institute of Robotics and Mechatronics 82234 Wessling Germany Faculty 3 - Mathematics and Computer Science University of Bremen 28359 Bremen Germany

ISBN: (纸本)9781479969357

For such common tasks as motion planning or object recognition robots need to perceive their environment and create a dense 3D map of it. A recent breakthrough in this area was the KinectFusion algorithm [16], which relies on step by step matching a depth image to the map via ICP to recover the sensor pose and updating the map based on that pose. In so far it ignores techniques developed in the graph-SLAM area such as fusion with odometry, modeling of uncertainty and distributing an observed inconsistency over the map. This paper presents a method to integrate a dense geometric truncated signed distance function (TSDF) representation as KinectFusion uses with a sparse parametric representation as common in graph SLAM. The key idea is to have local TSDF sub-maps attached to reference nodes in the SLAM graph and derive graph-SLAM links via ICP by matching a map to a depth image. By moving these reference nodes according to the graph-SLAM estimate, the overall map can be deformed without touching individual sub-maps so that re-building of sub-maps is only needed in case of significant deformation within a sub-map. Also, further information can be added to the graph as common in graph SLAM. Examples are odometry or the fact that the ground is roughly but not exactly planar. Additionally, the paper proposes a modification of the KinectFusion algorithm to improve handling of long range data by taking the range dependent uncertainty into account.

关键词： Humanoid robots maps reference node odometry distance function Signed step-by-step

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Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot

Implementation and test of human-operated and human-like ada...

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15th Annual Conference on Advances in Autonomous robotics Systems, TAROS 2014

作者： Liang, Peidong Yang, Chenguang Wang, Ning Li, Zhijun Li, Ruifeng Burdet, Etienne Centre for Robotics and Neural Systems School of Computing and Mathematics Plymouth University United Kingdom MOE Key Lab. of Autonomous System and Network Control College of Automation Science and Engineering South China University of Technology China State Key Laboratory of Robotics and System Harbin Institute of Technology China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Department of Bioengineering Imperial College of Science Technology and Medicine London United Kingdom

ISBN: (纸本)9783319104003

This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremental stiffness extraction method is developed, which uses instantaneous amplitude identified from EMG in a high frequency band, compensating for non-linear residual error in the linear mapping and preventing muscle fatigue from affecting the control. Experiments on one joint of the Baxter robot are carried out to test the approach in a disturbance attenuation task, and to compare it with automatic human-like impedance adaptation. The experimental results demonstrate that the new human operated impedance method is successful at attenuating disturbance, and results similarly to as automatic disturbance attenuation, thus demonstrating its efficiency. © 2014 Springer International Publishing.

关键词： Stiffness

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Latent Space Policy Search for robotics

Latent Space Policy Search for Robotics

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kevin Sebastian Luck Gerhard Neumann Erik Berger Jan Peters Heni Ben Amor Department of Computer Science Technische Universitat Darmstadt 64289 Darmstadt Germany Department of Mathematics and Computer Science Technische Universitat Bergakademie Freiberg 09599 Freiberg Germany Institute for Robotics and Intelligent Machines Georgia Institute of Technology GA 30332 USA

ISBN: (纸本)9781479969357

关键词： Robots Aerospace electronics Mathematical model Learning (artificial intelligence) Vectors Equations Joints

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