<![CDATA[ IEEE Transactions on Robotics - new TOC ]]>
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TOC Alert for Publication# 8860 2016June 23<![CDATA[Table of Contents]]>323C1C169<![CDATA[IEEE Transactions on Robotics]]>323C2C261<![CDATA[Asymptotically Near-Optimal RRT for Fast, High-Quality Motion Planning]]>lower bound tree-RRT (LBT-RRT), a single-query sampling-based motion-planning algorithm that is asymptotically near-optimal. Namely, the solution extracted from LBT-RRT converges to a solution that is within an approximation factor of of the optimal solution. Our algorithm allows for a continuous interpolation between the fast RRT algorithm and the asymptotically optimal RRT* and RRG algorithms when the cost function is the path length. When the approximation factor is (i.e., no approximation is allowed), LBT-RRT behaves like RRG. When the approximation factor is unbounded, LBT-RRT behaves like RRT. In between, LBT-RRT is shown to produce paths that have higher quality than RRT would produce and run faster than RRT* would run. This is done by maintaining a tree that is a subgraph of the RRG roadmap and a second, auxiliary graph, which we call the lower-bound graph. The combination of the two roadmaps, which is faster to maintain than the roadmap maintained by RRT*, efficiently guarantees asymptotic near-optimality. We suggest to use LBT-RRT for high-quality anytime motion planning. We demonstrate the performance of the algorithm for scenarios ranging from 3 to 12 degrees of freedom and show that even for small approximation factors, the algorithm produces high-quality solutions (comparable with RRG and RRT*) with little running-time overhead when compared with RRT.]]>323473483729<![CDATA[A Novel 4-DOF Origami Grasper With an SMA-Actuation System for Minimally Invasive Surgery]]>3234844981456<![CDATA[Stochastic Dynamic Trapping in Robotic Manipulation of Micro-Objects Using Optical Tweezers]]>3234995121211<![CDATA[Learning Physical Collaborative Robot Behaviors From Human Demonstrations]]>3235135271674<![CDATA[Coarse-to-Fine Localization for a Mobile Robot Based on Place Learning With a 2-D Range Scan]]>3235285441649<![CDATA[Planar Pose Graph Optimization: Duality, Optimal Solutions, and Verification]]>certify optimality of a given estimate. Our first contribution is to frame planar PGO in the complex domain. This makes analysis easier and allows drawing connections with existing literature on unit gain graphs. The second contribution is to formulate and analyze the properties of the Lagrangian dual problem in the complex domain. Our analysis shows that the duality gap is connected to the number of zero eigenvalues of the penalized pose graph matrix. We prove that if this matrix has a single zero eigenvalue, then 1) the duality gap is zero, 2) the primal PGO problem has a unique solution (up to an arbitrary roto-translation), and 3) the primal solution can be computed by scaling an eigenvector of the penalized pose graph matrix. The third contribution is algorithmic: We leverage duality to devise and algorithm that computes the optimal solution when the penalized matrix has a single zero eigenvalue. We also propose a suboptimal variant when the zero eigenvalues are multiple. Finally, we show that duality provides computational tools to verify if a given estimate (e.g., computed using iterative solvers) is globally optimal. We conclude the paper with an extensive numerical analysis. Empirical evidence shows that, in the vast majority of cases ( of the tests under noise regimes of practical robotics applications), the penalized pose graph matrix has a single zero eigenvalue; hence, our approach allows computing (-
r verifying) the optimal solution.]]>3235455651679<![CDATA[Planning the Initial Motion of a Free Sliding/Rolling Ball]]>3235665821457<![CDATA[Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees]]>soft constraint, whose partial satisfaction is allowed, while the safety component is viewed as a hard constraint, whose violation is forbidden. To partially satisfy the cosafety component, inspirations are taken from indoor-robotic scenarios, and three types of (unexpressed) restrictions on the ordering of subtasks in the specification are considered. For each type, a partial satisfaction method is introduced, which guarantees the generation of trajectories that do not violate the safety constraints while attending to partially satisfying the cosafety requirements with respect to the chosen restriction type. The efficacy of the framework is illustrated through case studies on a hybrid car-like robot in an office environment.]]>3235835991798<![CDATA[Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments]]>323600613990<![CDATA[High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots]]> faster than real time on an ordinary PC. Experimental results on multiple platforms and terrain types show that, once calibrated, our models predict motion accurately. To facilitate their use, we have released open-source MATLAB and C^{++} libraries implementing our modeling/simulation methods.]]>3236146252234<![CDATA[Stable Grip Control on Soft Objects With Time-Varying Stiffness]]>3236266371815<![CDATA[Multi-body Motion Estimation from Monocular Vehicle-Mounted Cameras]]>ego motion and motions of multiple moving objects in the scene–called eoru motions–through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g., visual odometry and simultaneous localization and mapping) can only estimate the ego motion of the vehicle. The capability of a robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego motion. First, the framework for general unconstrained motion is introduced and then it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego motion is segmented and then several hypotheses are generated for the eoru motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with a constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (the KITTI dataset), including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with a general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as multibody structure from motion in the computer vision community.]]>3236386511064<![CDATA[Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions]]>3236526711424<![CDATA[Model and Analysis of the Interaction Dynamics in Cooperative Manipulation Tasks]]>3236726831136<![CDATA[Distributed Rotational and Translational Maneuvering of Rigid Formations and Their Applications]]>3236846971398<![CDATA[Minimal Actuation for a Flat Actuated Flexible Manifold]]> or . The AFM shape can theoretically be manipulated into any continuous smooth function. The mechanism possesses dozens of degrees of freedom (DOF). However, an applicable AFM would require thousands or even an infinite number of DOF (for the continuous case). This paper addresses the need to aggressively reduce the number of inputs. To exemplify our optimization, we introduce an algorithm for the forward kinematics and the inverse kinematics for such mechanisms. We then present a periodic actuation method, which enables input reduction to its peripheral inputs alone. We show how a -DOF AFM can make the most of its capabilities, while the number of inputs substantially drops. To exemplify our results, we have fabricated an AFM as a grid using shape memory alloy artificial muscle wire. Our input method may simplify the fabrication process, reduce the mechanism's overall weight, and improve the actuation performance.]]>323698706577<![CDATA[Progressive Planning of Continuum Grasping in Cluttered Space]]> -section continuum manipulator to probe an object, while gradually forming a whole-arm grasp in a cluttered environment. This approach is effective and efficient as evident from simulation and real experiments.]]>3237077161186<![CDATA[Learning and Generalization of Compensative Zero-Moment Point Trajectory for Biped Walking]]> -nearest neighbor regression based on the Mahalonobis distance. Compared with state-of-the-art model-based methods, the proposed learning approach is model free and allows online adaptation to constant unknown disturbances. Enhanced walking robustness can be observed from reduced average ZMP error and more robust reaction against external disturbances on the DLR humanoid robot TORO.]]>3237177252924<![CDATA[Humanoid and Human Inertia Parameter Identification Using Hierarchical Optimization]]>323726735938<![CDATA[Toward Self-Healing Actuators: A Preliminary Concept]]>3237367431321<![CDATA[Cooperative Swinging of Complex Pendulum-Like Objects: Experimental Evaluation]]>323744753883<![CDATA[Continuum Differential Mechanisms and Their Applications in Gripper Designs]]>3237547621408<![CDATA[The GR2 Gripper: An Underactuated Hand for Open-Loop In-Hand Planar Manipulation]]>a priori workspace exploration is still an open problem in robot manipulation and a necessity for many robotics applications. In this paper we present a two-fingered gripper topology that enables an enhanced predefined in-hand manipulation primitive controlled without knowing the size, shape, or other particulars of the grasped object. The in-hand manipulation behavior, namely, the planar manipulation of the grasped body, is predefined thanks to a simple hybrid low-level control scheme and has an increased range of motion due to the introduction of an elastic pivot joint between the two fingers. Experimental results with a prototype clearly show the advantages and benefits of the proposed concept. Given the generality of the topology and in-hand manipulation principle, researchers and designers working on multiple areas of robotics can benefit from the findings.]]>323763770669<![CDATA[Introducing IEEE Collabratec]]>3237717711927<![CDATA[Member Get-A-Member (MGM) Program]]>3237727723354<![CDATA[IEEE Robotics and Automation Society]]>323C3C358<![CDATA[Blank page]]>323C4C45