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Grasping and manipulating everyday objects in a goal-directed manner is an important ability of a service robot. The robot needs to reason about task requirements and ground these in the sensorimotor information. Grasping and interaction with objects are challenging in real-world scenarios, where sensorimotor uncertainty is prevalent. This paper presents a probabilistic framework for the representation and modeling of robot-grasping tasks. The framework consists of Gaussian mixture models for generic data discretization, and discrete Bayesian networks for encoding the probabilistic relations among various task-relevant variables, including object and action features as well as task constraints. We evaluate the framework using a grasp database generated in a simulated environment including a human and two robot hand models. The generative modeling approach allows the prediction of grasping tasks given uncertain sensory data, as well as object and grasp selection in a task-oriented manner. Furthermore, the graphical model framework provides insights into dependencies between variables and features relevant for object grasping. [ABSTRACT FROM PUBLISHER]

This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown a priori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: 1) maximize the number of events observed and 2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with $O(n)$ time complexity, in which $n$ is the number of stations, with $O(\log n)$ time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any $\epsilon > 0$ a $(1+\epsilon)$ -optimal solution for this more general, NP-hard problem. [ABSTRACT FROM PUBLISHER]

We address the fundamental problem of goal-directed path planning in an uncertain environment represented as a probability (of occupancy) map. Most methods generally use a threshold to reduce the grayscale map to a binary map before applying off-the-shelf techniques to find the best path. This raises the somewhat ill-posed question, what is the right (optimal) value to threshold the map? We instead suggest a persistent homology approach to the problem—a topological approach in which we seek the homology class of trajectories that is most persistent for the given probability map. In other words, we want the class of trajectories that is free of obstacles over the largest range of threshold values. In order to make this problem tractable, we use homology in \mathbb Z_2 coefficients (instead of the standard \mathbb Z coefficients), and describe how graph search-based algorithms can be used to find trajectories in different homology classes. Our simulation results demonstrate the efficiency and practical applicability of the algorithm proposed in this paper. [ABSTRACT FROM PUBLISHER]

We propose local magnetic actuation (LMA) as an approach to robotic actuation for surgical instruments. An LMA actuation unit consists of a pair of diametrically magnetized single-dipole cylindrical magnets, working as magnetic gears across the abdominal wall. In this study, we developed a dynamic model for an LMA actuation unit by extending the theory proposed for coaxial magnetic gears. The dynamic model was used for closed-loop control, and two alternative strategies—using either the angular velocity at the motor or at the load as feedback parameter—were compared. The amount of mechanical power that can be transferred across the abdominal wall at different intermagnetic distances was also investigated. The proposed dynamic model presented a relative error below 7.5% in estimating the load torque from the system parameters. Both the strategies proposed for closed-loop control were effective in regulating the load speed with a relative error below 2% of the desired steady-state value. However, the load-side closed-loop control approach was more precise and allowed the system to transmit larger values of torque, showing, at the same time, less dependence from the angular velocity. In particular, an average value of 1.5 mN$\cdot$m can be transferred at 7 cm, increasing up to 13.5 mN$\cdot$m as the separation distance is reduced down to 2 cm. Given the constraints in diameter and volume for a surgical instrument, the proposed approach allows for transferring a larger amount of mechanical power than what would be possible to achieve by embedding commercial dc motors. [ABSTRACT FROM PUBLISHER]

In robotics, static stiffness maps are used as tools for the performance analysis of robots employed in production tasks, such as pick-and-place or manufacturing. This paper evaluates the relevance of a numerical tool built from a commercial finite element package to generate stiffness maps for any type of robot (serial, parallel, hybrid or compliant). The key points are the spatial resolution, the precision, and the calculation time of a stiffness map. The method for obtaining the 36 static stiffness maps of a 3-D robotic structure in its operational space is presented. The mechanical model is based on a finite element calculation using beam elements for the links and spring elements for the joints. The approach is first applied to a rigid-body mechanism. Numerical results show that a good compromise can be obtained between spatial resolution, precision, and calculation time. Then, the method is applied to a compliant structure requiring processing in a large displacement framework for the relevant estimation of a stiffness map. The numerical tool opens new prospects for the design of robotic structures, in terms of both optimization and use of various material behaviors. [ABSTRACT FROM PUBLISHER]

This paper presents an alternative decomposition of spatial stiffness matrices based on the concept of compliant axes. According to the congruence transformation of spatial stiffness, the coordinate-invariant aspects, which are referred to as the central principal components of the $6\times 6$ symmetric positive semidefinite matrices, can be derived uniquely. The proposed decomposition is free from the eigenvalue problems of the $6\times 6$ stiffness matrices so that both Plücker's ray and axis coordinates can be utilized to characterize the elastic system's force–deflection behavior. Hence, an arbitrary spatial stiffness matrix can be uniquely decomposed into two sets of orthogonal spring wrenches with finite and infinite pitches, respectively. The decomposed wrenches with finite pitches correspond to the stiffness’ wrench-compliant axes, along which linear deformations produce only wrenches parallel to them. As a result, three torsional and three screw springs are required, at the most, to realize a given spatial stiffness. Using the principal axes decomposition, some physical appreciations, such as the center of stiffness, the wrench-compliant axes, and the correspondence of compliance and stiffness, can be derived to reveal the inherent structure of spatial stiffness in an intuitive manner. In order to verify the effectiveness of the proposed method, two numerical examples are intensively studied with comparison to the eigenscrew decomposition. In addition, a potential application of the proposed stiffness decomposition method is also provided for the structural compliance modeling of flexible links in robot manipulators. [ABSTRACT FROM PUBLISHER]

This paper addresses a multirobot scheduling problem in which autonomous unmanned aerial vehicles (UAVs) must be recharged during a long-term mission. The proposal is to introduce a separate team of dedicated charging robots that the UAVs can dock with in order to recharge. The goal is to schedule and plan minimum cost paths for charging robots such that they rendezvous with and replenish the UAVs, as needed, during the mission. The approach is to discretize the 3-D UAV flight trajectories into sets of projected charging points on the ground, thus allowing the problem to be abstracted onto a partitioned graph. Solutions consist of charging robot paths that collectively charge each of the UAVs. The problem is solved by first formulating the rendezvous planning problem to recharge each UAV once using both an integer linear program and a transformation to the Travelling Salesman Problem. The methods are then leveraged to plan recurring rendezvous’ over longer horizons using fixed horizon and receding horizon strategies. Simulation results using realistic vehicle and battery models demonstrate the feasibility and robustness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Intuitive and effective physical assistance is an essential requirement for robots sharing their workspace with humans. Application domains reach from manufacturing and service robotics via rehabilitation and mobility aids to education and training. In this context, assistance based on human behavior anticipation has shown superior performance in terms of human effort minimization. However, when a robot's expectations mismatch a human intentions, undesired interaction forces appear incurring safety risks and discomfort. Human behavior prediction is, therefore, a crucial issue: It enables effective anticipation but potentially produces disagreements when prediction errors occur. In this paper, we present a novel control scheme for anticipatory haptic assistance where robot behavior adapts to prediction uncertainty. Following a data-driven stochastic modeling approach, robot assistance is synthesized solving a risk-sensitive optimal control problem, where the cost function and plant dynamics are affected by model uncertainty. The proposed approach is objectively and subjectively evaluated in an experiment with human users. Results indicate that our method outperforms other assistive control approaches in terms of perceived helpfulness and human effort minimization. [ABSTRACT FROM PUBLISHER]

As sampling-based motion planners become faster, they can be reexecuted more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot's kinematic model. We investigate and analyze high-frequency replanning (HFR) where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and nonholonomic robots including car-like vehicles and steerable medical needles. [ABSTRACT FROM PUBLISHER]

Concentric tube robots are catheter-sized continuum robots that are well suited for minimally invasive surgery inside confined body cavities. These robots are constructed from sets of precurved superelastic tubes and are capable of assuming complex 3-D curves. The family of 3-D curves that the robot can assume depends on the number, curvatures, lengths, and stiffnesses of the tubes in its tube set. The robot design problem involves solving for a tube set that will produce the family of curves necessary to perform a surgical procedure. At a minimum, these curves must enable the robot to smoothly extend into the body and to manipulate tools over the desired surgical workspace while respecting anatomical constraints. This paper introduces an optimization framework that utilizes procedure- or patient-specific image-based anatomical models along with surgical workspace requirements to generate robot tube set designs. The algorithm searches for designs that minimize robot length and curvature and for which all paths required for the procedure consist of stable robot configurations. Two mechanics-based kinematic models are used. Initial designs are sought using a model assuming torsional rigidity. These designs are then refined using a torsionally compliant model. The approach is illustrated with clinically relevant examples from neurosurgery and intracardiac surgery. [ABSTRACT FROM PUBLISHER]

Helical microswimmers that are capable of propulsion at low Reynolds numbers have great potential for numerous applications. Several kinds of artificial magnetic-actuated helical microswimmers have been designed by researchers. However, they are primarily open-loop controlled. This paper aims to investigate methods of closed-loop control of a magnetic-actuated helical swimmer at low Reynolds number by using visual feedback. For many in-vitro applications, helical swimmers should pass through a defined path, for example along channels with no prerequisite on the velocity profile along the path. Therefore, the main objective of this paper is to achieve a velocity-independent planar path following task. Since the planar path following is based on 3-D steering control of the helical swimmer, a 3-D pose estimation of a helical swimmer is introduced based on the real-time visual tracking with a stereo vision system. The contribution of this paper is in two parts: The 3-D steering of a helical swimmer is demonstrated by visual servo control; and the path following of a straight line with visual servo control is achieved, then compared with open-loop control. We further expect that with this visual servo control method, the helical swimmers will be able to follow reference paths at the microscale. [ABSTRACT FROM PUBLISHER]

This paper presents a study of hand impedance measurements comparatively across ten professional and 14 novice manual welders, when they are performing tungsten inert gas (TIG) welding interactively with the KUKA lightweight robot arm (LWR). The results show that hand impedance differs across professional and novice welders. The welding torch is attached to the KUKA LWR, which is admittance controlled via a force sensor to give the feeling of a free floating mass at its end-effector. The subjects perform TIG welding on 1.5-mm-thick stainless steel plates by manipulating the torch. Impedance is measured by introducing external force disturbances and fitting a mass–damper–spring model to human hand reactions. The quality of welding is measured using the variance of the position signals above 0.1 Hz. Professional welders demonstrate less variance and, in general, apply larger hand impedance (larger damping and stiffness) than the novice welders. The variance of position during nominal welding is minimal for both professional and novice welders in the direction perpendicular to the welding line in the plane of the plate, which is the most important direction for the quality of the weld. For both professional and novice welders, the mass and damping values are largest in this direction compared with the other two directions. Professional welders demonstrate larger damping than the novice welders in this direction. [ABSTRACT FROM PUBLISHER]

Sliding probe methods are designed for the in situ characterization of electrical properties of individual 1-D nanostructures. The key to achieving a high resolution is to keep the contact resistance constant by controlling the contact force and area between the specimen and the sliding probe. We have developed several techniques and tools including differential sliding, flexible probes, and specimen-shape-adaptable probes using nanorobotic manipulation. Compared with conventional methods, these sliding probe methods allow in situ characterization with a higher resolution than conventional methods. Furthermore, they are superior for local property characterization, which is of particular interest for heterostructured nanomaterials and defect detection. [ABSTRACT FROM PUBLISHER]

In this paper, we present provably-good distributed task assignment algorithms for a heterogeneous multi-robot system, in which the tasks form disjoint groups and there are constraints on the number of tasks a robot can do (both within the overall mission and within each task group). Each robot obtains a payoff (or incurs a cost) for each task and the overall objective for task allocation is to maximize (minimize) the total payoff (cost) of the robots. In general, existing algorithms for task allocation either assume that tasks are independent or do not provide performance guarantee for the situation, in which task constraints exist. We present a distributed algorithm to provide an almost optimal solution for our problem. The key aspect of our distributed algorithm is that the overall objective is (almost) maximized by each robot maximizing its own objective iteratively (using a modified payoff function based on an auxiliary variable, called price of a task). Our distributed algorithm is polynomial in the number of tasks, as well as the number of robots. [ABSTRACT FROM PUBLISHER]

This paper presents “1STAR,” which is the first robot that is driven by a single actuator but can be directly commanded to move straight or turn clockwise or counterclockwise. The legged robot relies on a novel actuation gait, which exploits the compliance disparity between alternate stance tripods, to generate rotation by continuously accelerating and decelerating the legs. The direction of turning depends on the configuration of the legs—tripod left or right—and the timing of the acceleration and deceleration. Alternating leg acceleration in successive steps allows for continuous rotation in the desired direction. The turning radius can be varied by changing the timing of the leg acceleration and deceleration without changing the cycle frequency and linear speed. A simplified kinematic motion model of a robot is presented, and a dynamic simulation is performed to analyze the behavior and optimize robot parameters. The locomotion gait is verified experimentally using our newly designed “1STAR” robot. [ABSTRACT FROM PUBLISHER]

Widely accepted utility of simple spring–mass models for running behaviors as descriptive tools, as well as literal control targets, motivates accurate analytical approximations to their dynamics. Despite the availability of a number of such analytical predictors in the literature, their validation has mostly been done in simulation, and it is yet unclear how well they perform when applied to physical platforms. In this paper, we extend on one of the most recent approximations in the literature to ensure its accuracy and applicability to a physical monopedal platform. To this end, we present systematic experiments on a well-instrumented planar monopod robot, first to perform careful identification of system parameters and subsequently to assess predictor performance. Our results show that the approximate solutions to the spring-loaded inverted pendulum dynamics are capable of predicting physical robot position and velocity trajectories with average prediction errors of 2% and 7%, respectively. This predictive performance together with the simple analytic nature of the approximations shows their suitability as a basis for both state estimators and locomotion controllers. [ABSTRACT FROM PUBLISHER]

Despite the great success of using gradient-based controllers to stabilize rigid formations of autonomous agents in the past years, surprising yet intriguing undesirable collective motions have been reported recently, when inconsistent measurements are used in the agents’ local controllers. To make the existing gradient control robust against such measurement inconsistency, we exploit local estimators following the well-known internal model principle for robust output regulation control. The new estimator-based gradient control is still distributed in nature, and can be constructed systematically even when the number of agents in a rigid formation grows. We prove rigorously that the proposed control is able to guarantee exponential convergence, and then demonstrate through robotic experiments and computer simulations that the reported inconsistency-induced orbits of collective movements are effectively eliminated. [ABSTRACT FROM PUBLISHER]

This paper presents novel data fusion methods that enable teams of vehicles to perform target search tasks without guaranteed communication. Techniques are introduced for merging estimates of a target's position from vehicles that regain contact after long periods of time, and a fully distributed team-planning algorithm is proposed, which utilizes limited shared information as it becomes available. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open water and harbor environments. The results demonstrate that the proposed distributed coordination techniques provide performance competitive with full communication. [ABSTRACT FROM PUBLISHER]

An author and subject index for the December 2014 issue of “IEEE Transactions on Robotics" is presented.

One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing, and its performance is limited by occlusions and ambiguity in appearance and geometry. This paper proposes an active approach to object detection in which the point of view of a mobile depth camera is controlled. When an initial static detection phase identifies an object of interest, several hypotheses are made about its class and orientation. Then, a sequence of views, which balances the amount of energy used to move the sensor with the chance of identifying the correct hypothesis, is planned. We formulate an active hypothesis testing problem, which includes sensor mobility, and solve it using a point-based approximate partially observable Markov decision process algorithm. The validity of our approach is verified through simulation and real-world experiments with the PR2 robot. The results suggest that the approach outperforms the widely used greedy viewpoint selection and provides a significant improvement over static object detection. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Advertisement. [ABSTRACT FROM AUTHOR]

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

We present a gait generation framework for multilegged robots based on max-plus algebra that is endowed with intrinsically safe gait transitions. The time schedule of each foot liftoff and touchdown is modeled by sets of max-plus linear equations. The resulting discrete-event system is translated to continuous time via piecewise constant leg phase velocities; thus, it is compatible with traditional central pattern generator approaches. Different gaits and gait parameters are interleaved by utilizing different max-plus system matrices. We present various gait transition schemes and show that optimal transitions, in the sense of minimizing the stance time variation, allow for constant acceleration and deceleration on legged platforms. The framework presented in this paper relies on a compact representation of the gait space, provides guarantees regarding the transient and steady-state behavior, and results in simple implementations on legged robotic platforms. [ABSTRACT FROM PUBLISHER]

Typical animal locomotion is achieved by the rhythmical undulation of its body segments while interacting with its environment. It inspires the mechanical design of multilink locomotors. With different postures, a multilink system may present different locomotion gaits. Recently, a so-called natural oscillation gait was studied for multilink systems, and a class of biologically inspired controllers was designed for the achievement of the gait. In this paper, the theoretical design is experimentally applied on a mechanical multilink testbed of two posture configurations in rayfish-like flapping-wing motion and snake-like serpentine motion. The effectiveness of the design is cross examined by theoretical analysis, numerical simulation, and experiments. [ABSTRACT FROM PUBLISHER]

In this paper, we set out a framework for the analysis of coupled stability in dual-user linear teleoperation systems. An extension of the Zeheb–Walach (ZW) criteria for absolute stability of an n-port network will be stated and proven. While the original theorem states conditions for asymptotic stability of a network terminated by passive impedances, the extended version allows for poles on the imaginary axis, which makes it applicable to a larger class of systems, such as robotic applications with position feedback. The extended theorem includes conditions on the Laurent expansion of the elements and the principal minors of the network immittance matrix. A novel dual-user shared control paradigm, realizing a three-way gearbox mechanism, is presented. A numerical analysis of absolute stability of the three-port network, representing the shared control architecture, demonstrates the effectiveness of the extended Zeheb–Walach method. [ABSTRACT FROM PUBLISHER]

A highly maneuverable, spheroid-shaped, underwater robot using appendage-free, multi-degree of freedom (DOF) propulsion technologies is presented. The vehicle is hydrodynamically unstable due to the Munk moment. The vehicle is stabilized by feedback control, rather than passive fins, which facilitates rapid turns and agile motions. The new design was motivated by nuclear reactor inspection and other applications where external appendages must be avoided. Two technical challenges are addressed in this paper. One is the development of a compact, multi-DOF propulsion system that generates multiaxis water jets and switches them rapidly. The other is the design of a jet configuration and control system that augments stability and achieves high maneuverability. A nonlinear hydrodynamic model is formulated, and its linearized dynamics are analyzed to attain insights into how jet direction influences controllability and stability. A prototype vehicle is built and used to verify these concepts. The integrated design method is implemented and shown to achieve stable motions, high maneuverability, and multidirectional capability. [ABSTRACT FROM PUBLISHER]

Vehicle localization is one of the most common tasks in robotics. Using vision as a sensor, most methods perform localization from cameras mounted on board the vehicle. In contrast, we propose a method based on an off-board camera. The system uses a similarity measure between a camera image and a synthetic image generated using a 3-D vehicle model, ideally converging to the true pose of the vehicle. The similarity measure is based on a model of shading appearance, which depends on the surface curvature of the 3-D model. Considering that the area observed by the camera is fairly planar, a rough initial estimation for the position of the object can be obtained using 2-D blob tracking. The 3-D model of the vehicle can be rendered near the vehicle in the real image, and using the proposed similarity measure, it converges to the correct pose. A classification function to discriminate between different vehicles is also proposed, making it possible for the system to identify and track multiple vehicles of interest. A number of experiments are performed with different vehicles outdoors, in a real industrial environment, considering different illumination conditions. The low average error rate compared with a laser-based ground truth illustrates the applicability of the method. [ABSTRACT FROM PUBLISHER]

To date, untethered magnetic devices actuated with a single rotating permanent magnet, such as active capsule endoscopes and magnetic microrobots, have been constrained to operate in positions where the rotating dipole field behavior is simple and easy to visualize. In this paper, we show how to generate a rotating magnetic field with any desired rotation axis, for magnetic device actuation, at any device position in space using a single rotating-magnet actuator. The methods presented can control untethered rotating magnetic devices, while the rotating actuator magnet follows trajectories independent of the untethered devices themselves. We demonstrate our methods by actuating rotating magnetic devices in a lumen. Applications include minimally invasive medical tasks requiring an untethered magnetic device to operate in natural lumen pathways of the body (e.g., the gastrointestinal system, the subarachnoid space of the nervous system, or vasculature). [ABSTRACT FROM PUBLISHER]

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar, or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally, for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations. [ABSTRACT FROM PUBLISHER]

This paper analyzes tendon-driven mechanisms (TDMs) with active and passive tendons and proposes a method for designing TDMs. First, we group TDMs into six classes according to their controllability and the number of driving degrees of freedom. In this classification system, the conventional underactuated mechanisms are grouped into three classes, two of which have often previously been grouped together although they have different manipulation abilities. Next, we analyze bias forces to separate and decouple a given TDM into several smaller TDMs. Finally, we propose a design method for combining smaller TDMs into an appropriate TDM. Using this method, we can easily determine an appropriate tendon transmission that meets the requirements for the number of tendons, the hardware of the tendon routing, and arbitrary joint constraint that has useful applications in prosthetic and biomimetic hands. Numerical examples show that the proposed method guarantees a nonsingular series actuation transmission and that the designed underactuated TDMs could achieve arbitrary stiffness independent of the actuation effort compared with the conventional underactuated TDM with torsion springs. [ABSTRACT FROM PUBLISHER]

In robotic single-port surgery, it is desirable for a manipulator to exhibit the property of variable stiffness. Small-port incisions may require both high flexibility of the manipulator for safety purposes, as well as high structural stiffness for operational precision and high payload capability. This paper presents a new hyperredundant tubular manipulator with a variable neutral-line mechanisms and adjustable stiffness. A unique asymmetric arrangement of the tendons and the links realizes both articulation of the manipulator and continuous stiffness modulation. This asymmetric motion of the manipulator is compensated by a novel actuation mechanism without affecting its structural stiffness. The paper describes the basic mechanics of the variable neutral-line manipulator, and its stiffness characteristics. Simulation and experimental results verify the performance of the proposed mechanism. [ABSTRACT FROM PUBLISHER]

Active constraints, also known as virtual fixtures, are high-level control algorithms which can be used to assist a human in man–machine collaborative manipulation tasks. The active constraint controller monitors the robotic manipulator with respect to the environment and task, and anisotropically regulates the motion to provide assistance. The type of assistance offered by active constraints can vary, but they are typically used to either guide the user along a task-specific pathway or limit the user to within a “safe” region. There are several diverse methods described within the literature for applying active constraints, and these are surveyed within this paper. The active constraint research is described and compared using a simple generalized framework, which consists of three primary processes: 1) constraint definition, 2) constraint evaluation, and 3) constraint enforcement. All relevant research approaches for each of these processes, found using search terms associated to “virtual fixture,” “active constraint” and “motion constraint,” are presented. [ABSTRACT FROM PUBLISHER]

This paper presents a decentralized algorithm for the coordinated assembly of 3-D objects that consist of multiple types of parts, using a networked team of robots. We describe the algorithm and analyze its convergence and adaptation properties. We partition construction in two tasks: tool delivery and assembly. Each task is performed by a networked team of specialized robots. We analyze the performance of the algorithms using the balls into bins problem and show their adaptation to failure of robots, dynamic constraints, multiple types of elements, and reconfiguration. We instantiate the algorithm to building truss-like objects using rods and connectors. We implement the algorithm in simulation and show results to construct 2-D and 3-D parts. Finally, we describe hardware implementation of the algorithms, where mobile manipulators assemble smart parts with IR beacons. [ABSTRACT FROM PUBLISHER]

The break-away friction ratio (BF-ratio), which is the ratio between friction force and the normal force at slip occurrence, is important for the prediction of incipient slip and the determination of optimal grasping forces. Conventionally, this ratio is assumed constant and approximated as the static friction coefficient. However, this ratio varies with acceleration rates and force rates applied to the grasped object and the object material, which lead to difficulties in determining optimal grasping forces that avoid slip. In this paper, we propose a novel approach based on the interactive forces to allow a robotic hand to predict object slip before its occurrence. The approach only requires the robotic hand to have a short haptic surface exploration over the object surface before manipulating it. Then, the frictional properties of the finger–object contact can be efficiently identified, and the BF-ratio can be real-time predicted to predict slip occurrence under dynamic grasping conditions. Using the predicted BF-ratio as a slip, threshold is demonstrated to be more accurate than using the static/Coulomb friction coefficient. The presented approach has been experimentally evaluated on different object surfaces, showing good performance in terms of prediction accuracy, robustness, and computational efficiency. [ABSTRACT FROM PUBLISHER]

A subject index for the December 1, 2013 issue of "IEEE Transactions on Robotics" is presented.

We address the data association problem of features that are observed by a robotic network. Every robot in the network has limited communication capabilities and can only exchange local matches with its neighbors. We propose a distributed algorithm that takes these local matches and, by their propagation in the network, computes global correspondences. When the algorithm finishes, each robot knows the correspondences between its features and the features of all the other robots, even if they cannot directly communicate. The presence of spurious local correspondences may produce inconsistent global correspondences, which are association paths between features observed by the same robot. The contributions of this study are the propagation of the local matches and the detection and resolution of these inconsistencies. We formally prove that after executing the algorithm, all the robots finish with a data association that is free of inconsistencies. We provide a fully decentralized solution to the problem that is valid for any fixed communication topology and with bounded communications between the robots. Simulations and experimental results with real images show the performance of the method considering different features, matching functions, and robotic applications. [ABSTRACT FROM PUBLISHER]

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator with simplified constraints, a convex reformulation of this optimal control problem has been derived previously. However, many applications in robotics feature constraints such as velocity-dependent torque constraints or torque rate constraints that destroy the convexity. The present paper proposes an efficient sequential convex programming (SCP) approach to solve the corresponding nonconvex optimal control problems by writing the nonconvex constraints as a difference of convex (DC) functions, resulting in convex–concave constraints. We consider seven practical applications that fit into the proposed framework even when mutually combined, illustrating the flexibility and practicality of the proposed framework. Furthermore, numerical simulations for some typical applications illustrate the fast convergence of the proposed method in only a few SCP iterations, confirming the efficiency of the proposed framework. [ABSTRACT FROM PUBLISHER]

To adapt to many different objects and tasks, hands are very complex systems with many degrees of freedom (DoFs), sensors, and actuators. In robotics, such complexity comes at the cost of size and weight of the hardware of devices, but it strongly affects also the ease of their programming. A possible approach to simplification consists in coupling some of the DOFs, thus affording a reduction of the number of effective inputs, and eventually leading to more efficient, simpler, and reliable designs. Such coupling can be at the software level, to achieve faster, more intuitive programmability or at the hardware level, through either rigid or compliant physical couplings between joints. Physical coupling between actuators and simplification of control through the reduction of independent inputs is also an often-reported interpretation of human hand movement data, where studies have demonstrated that few “postural synergies” explain most of the variance in hand configurations used to grasp different objects. Together with beneficial simplifications, the reduction of the number of independent inputs to a few coupled motions or “synergies” has also an impact on the ability of the hand to dexterously control grasp forces and in-hand manipulation. This paper aims to develop tools that establish how many synergies should be involved in a grasp to guarantee stability and efficiency, depending on the task and on the hand embodiment. Through the analysis of a quasi-static model, grasp structural properties related to contact force and object motion controllability are defined. Different compliant sources are considered, for a generalization of the discussion. In particular, a compliant model for synergies assumed, referred to as “soft synergies,” is discussed. The controllable internal forces and motions of the grasped object are related to the actuated inputs. This paper investigates to what extent a hand with many joints can exploit postural synergies to control force and motion of the grasped object. [ABSTRACT FROM PUBLISHER]

In the rendezvous search problem, two robots at unknown locations must successfully meet somewhere in the environment. We study the symmetric version of the problem in which they must use the same strategy. We provide a new algorithm for the symmetric rendezvous problem on the line. Our symmetric strategy has a competitive ratio of \17.686 for total distance traveled and a competitive ratio of \24.843 for total time. Both are improvements over the previously best-known algorithm, which has (time and distance) a competitive ratio of \26.650. Our algorithm can be adapted for bounded linear environments and simple closed curves with the same performance guarantees. It is also robust with respect to errors in motion and differences in robots’ starting times. We confirm our theoretical results through simulations and show that our algorithms are practical by reporting the results of real robot deployments in indoor environments. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of capturing an omnidirectional evader using a differential drive robot in an obstacle-free environment. At the beginning of this game, the evader is at a distance L > l (the capture distance) from the pursuer. The goal of the evader is to keep the pursuer farther than this capture distance for as long as possible. The goal of the pursuer is to capture the evader as soon as possible. In this paper, we make the following contributions. We present closed-form representations of the motion primitives and time-optimal strategies for each player; these strategies are in Nash equilibrium, meaning that any unilateral deviation of each player from these strategies does not provide to such player benefit toward the goal of winning the game. We propose a partition of the playing space into mutually disjoint regions where the strategies of the players are well established. This partition is represented as a graph, which exhibits properties that guarantee global optimality. We also analyze the decision problem of the game and we present the conditions defining the winner. [ABSTRACT FROM PUBLISHER]

Over the past three decades, the spring–mass model has developed into the basic behavior model to study running in animals and robots. In the planar version, this model has helped to reveal and understand the passive stabilization of running in the horizontal and sagittal planes, and to derive from this knowledge control strategies for running robots. However, only few attempts have been made to transfer the knowledge to 3-D locomotion. Here, we show that the 3-D spring–mass model reveals a deadbeat control that does not require feedback about the actual ground level to produce highly robust running and steering in uncertain environments. The control naturally extends the time-based control derived for the planar version of this model and allows it to navigate rough terrain, while stabilizing running and steering. Using this control strategy, we demonstrate in simulation that a human-like system running at 5 ms^-1 tolerates frequent ground disturbances up to 30% of the leg length. Moreover, we find that the control outperforms a classical leg-placement strategy in terms of turning rate and disturbance rejection if the relative errors in system energy and the other model parameters stay small (<10%). Our results suggest that the time-based control can be a powerful alternative for leg-placement strategies in highly maneuverable running robots. [ABSTRACT FROM PUBLISHER]

This paper presents a study on electroencephalogram (EEG)-based control of an electric wheelchair. The objective is to control the direction of an electric wheelchair using only EEG signals. In other words, this is an attempt to use brain signals to control mechanical devices such as wheelchairs. To achieve this goal, we have developed a recursive training algorithm to generate recognition patterns from EEG signals. Our experimental results demonstrate the utility of the proposed recursive training algorithm and the viability of accomplishing direction control of an electric wheelchair by only EEG signals. Index Terms--Direction control, electric wheelchair, electroencephalogram (EEG)-based control, recursive training algorithm.

A noisy behavior of the time-domain passivity controller during the period of low velocity is analyzed. Main reasons of the noisy behavior are investigated through a simulation with a one-DOF haptic interface model. It is shown that the PO/PC is ineffective in dissipating the produced energy when the sign of the velocity, which is numerically calculated from the measured position, is suddenly changed, and when this velocity is zero. These cases happen during the period of low velocity due to the limited resolution of the position sensor. New methods, ignoring the produced energy from the velocity sign change, and holding the control force while the velocity is zero, are proposed for removing the noisy behavior. The feasibility of the developed methods is proved with both a simulation and a real experiment. Index Terms--Haptic interface, noisy behavior, passivity controller, passivity observer, time-domain passivity.

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments. Index Terms--Adaptation, genetic programming, snake-like robot, robustness.

In this paper, we present a novel way to approach the interconnection of a continuous and a discrete time physical system first presented in [1]-[3]. This is done in a way which preserves passivity of the coupled system independently of the sampling time T. This strategy can be used both in the field of telemanipulation, for the implementation of a passive master/slave system on a digital transmission line with varying time delays and possible loss of packets (e.g., the Internet), and in the field of haptics, where the virtual environment should 'feel' like a physical equivalent system. Index Terms--Haptics, Sampled passivity, sampled port-Hamiltonian systems, telemanipulation.