<![CDATA[ IEEE Transactions on Robotics - new TOC ]]>
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TOC Alert for Publication# 8860 2017January 19<![CDATA[Table of Contents]]>326C1C168<![CDATA[IEEE Transactions on Robotics]]>326C2C258<![CDATA[Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age]]>32613091332811<![CDATA[Distributed Scheduling of Network Connectivity Using Mobile Access Point Robots]]>1 regularization scheme, to recover the desired integrality structure of the solution. We propose a decentralized method to solve the above relaxation that is based on the recently developed accelerated distributed augmented Lagrangians (ADAL) algorithm. Specifically, we modify ADAL by incorporating in the algorithm the reweighted ℓ_{1} scheme, which enables us to recover the desired sparsity structure of the original MIP at the final solution. Numerical results are presented that validate the effectiveness of the proposed framework.]]>326133313461176<![CDATA[Surface-Based Detection and 6-DoF Pose Estimation of 3-D Objects in Cluttered Scenes]]>326134713611618<![CDATA[Stabilizing Series-Elastic Point-Foot Bipeds Using Whole-Body Operational Space Control]]>326136213791488<![CDATA[Comparative Design, Scaling, and Control of Appendages for Inertial Reorientation]]>326138013981067<![CDATA[Design of a Transmission With Gear Trains for Underactuated Mechanisms]]>326139914071827<![CDATA[A Framework of Human–Robot Coordination Based on Game Theory and Policy Iteration]]>326140814181194<![CDATA[Design of 3-D Printed Concentric Tube Robots]]>-1, which can withstand strains of 20% and 5.5% for the outer and inner tubes, respectively.]]>326141914301501<![CDATA[Asymptotically Optimal Planning by Feasible Kinodynamic Planning in a State–Cost Space]]>326143114431174<![CDATA[Distributed Coverage Estimation and Control for Multirobot Persistent Tasks]]>326144414601647<![CDATA[Folding Clothes Autonomously: A Complete Pipeline]]>326146114784153<![CDATA[Caging Grasps of Rigid and Partially Deformable 3-D Objects With Double Fork and Neck Features]]>32614791497824<![CDATA[Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture]]>max)/(r_{min})) approximation algorithm for SAMPLINGTSPN, where r_{min} and r_{max} are the minimum and maximum radii of input disks. Second, we show how to model the UAV planning problem using a metric graph and formulate an orienteering instance to which a known approximation algorithm can be applied. Third, we apply the two algorithms to the problem of obtaining ground and aerial measurements in order to accurately estimate a nitrogen map of a plot. Along with theoretical results, we present results from simulations conducted using real soil data and preliminary field experiments with the UAV.]]>326149815111798<![CDATA[Reducing the Energy Consumption of Robots Using the Bidirectional Clutched Parallel Elastic Actuator]]>326151215232583<![CDATA[Landing of a Quadrotor on a Moving Target Using Dynamic Image-Based Visual Servo Control]]>326152415351426<![CDATA[A Sparse Separable SLAM Back-End]]>32615361549870<![CDATA[Dynamic Point-to-Point Trajectory Planning of a Three-DOF Cable-Suspended Parallel Robot]]>32615501557949<![CDATA[Moments-Based Ultrasound Visual Servoing: From a Mono- to Multiplane Approach]]>32615581564733<![CDATA[Effective Background Model-Based RGB-D Dense Visual Odometry in a Dynamic Environment]]>326156515731382<![CDATA[2016 Index IEEE Transactions on Robotics Vol. 32]]>32615741590271<![CDATA[Introducing IEEE Collabratec]]>326159115911911<![CDATA[Imagine a community hopeful for the future]]>326159215921453<![CDATA[IEEE Robotics and Automation Society]]>326C3C353<![CDATA[INFORMATION FOR AUTHORS]]>326C4C455