Showing papers in "IEEE Transactions on Robotics in 2008"
TL;DR: The history and state of the art of lower limb exoskeletons and active orthoses are reviewed and a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature are provided.
Abstract: In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.
1,250 citations
TL;DR: iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering and provides efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix.
Abstract: In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.
1,091 citations
TL;DR: It is shown how novel manufacturing paradigms enable the creation of the mechanical and aeromechanical subsystems of a microrobotic device that is capable of Diptera-like wing trajectories, and the results are a uniquemicrorobot: a 60 mg robotic insect that can produce sufficient thrust to accelerate vertically.
Abstract: Biology is a useful tool when applied to engineering challenges that have been solved in nature. Here, the emulous goal of creating an insect-sized, truly micro air vehicle is addressed by first exploring biological principles. These principles give insights on how to generate sufficient thrust to sustain flight for centimeter-scale vehicles. Here, it is shown how novel manufacturing paradigms enable the creation of the mechanical and aeromechanical subsystems of a microrobotic device that is capable of Diptera-like wing trajectories. The results are a unique microrobot: a 60 mg robotic insect that can produce sufficient thrust to accelerate vertically. Although still externally powered, this micromechanical device represents significant progress toward the creation of autonomous insect-sized micro air vehicles.
878 citations
TL;DR: A new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).
Abstract: We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features relative to the camera locations from which they were first viewed, which produces measurement equations with a high degree of linearity. Importantly, our parametrization can cope with features over a huge range of depths, even those that are so far from the camera that they present little parallax during motion---maintaining sufficient representative uncertainty that these points retain the opportunity to "come in'' smoothly from infinity if the camera makes larger movements. Feature initialization is undelayed in the sense that even distant features are immediately used to improve camera motion estimates, acting initially as bearing references but not permanently labeled as such. The inverse depth parametrization remains well behaved for features at all stages of SLAM processing, but has the drawback in computational terms that each point is represented by a 6-D state vector as opposed to the standard three of a Euclidean XYZ representation. We show that once the depth estimate of a feature is sufficiently accurate, its representation can safely be converted to the Euclidean XYZ form, and propose a linearity index that allows automatic detection and conversion to maintain maximum efficiency---only low parallax features need be maintained in inverse depth form for long periods. We present a real-time implementation at 30 Hz, where the parametrization is validated in a fully automatic 3-D SLAM system featuring a handheld single camera with no additional sensing. Experiments show robust operation in challenging indoor and outdoor environments with a very large ranges of scene depth, varied motion, and also real time 360deg loop closing.
815 citations
TL;DR: The skeleton of this framework is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration.
Abstract: Many successful indoor mapping techniques employ frame-to-frame matching of laser scans to produce detailed local maps as well as the closing of large loops. In this paper, we propose a framework for applying the same techniques to visual imagery. We match visual frames with large numbers of point features, using classic bundle adjustment techniques from computational vision, but we keep only relative frame pose information (a skeleton). The skeleton is a reduced nonlinear system that is a faithful approximation of the larger system and can be used to solve large loop closures quickly, as well as forming a backbone for data association and local registration. We illustrate the workings of the system with large outdoor datasets (10 km), showing large-scale loop closure and precise localization in real time.
624 citations
TL;DR: The design and fabrication methods used to create underactuated, multimaterial structures that conform to surfaces over a range of length scales from centimeters to micrometers are described.
Abstract: Stickybot is a bioinspired robot that climbs smooth vertical surfaces such as glass, plastic, and ceramic tile at 4 cm/s. The robot employs several design principles adapted from the gecko including a hierarchy of compliant structures, directional adhesion, and control of tangential contact forces to achieve control of adhesion. We describe the design and fabrication methods used to create underactuated, multimaterial structures that conform to surfaces over a range of length scales from centimeters to micrometers. At the finest scale, the undersides of Stickybot's toes are covered with arrays of small, angled polymer stalks. Like the directional adhesive structures used by geckos, they readily adhere when pulled tangentially from the tips of the toes toward the ankles; when pulled in the opposite direction, they release. Working in combination with the compliant structures and directional adhesion is a force control strategy that balances forces among the feet and promotes smooth attachment and detachment of the toes.
579 citations
TL;DR: A mechanics-based model for transforming desired beam configuration to tendon displacements and vice versa is presented and insight as well as performance gains are provided for this increasingly ubiquitous class of manipulators.
Abstract: Continuum robotic manipulators articulate due to their inherent compliance. Tendon actuation leads to compression of the manipulator, extension of the actuators, and is limited by the practical constraint that tendons cannot support compression. In light of these observations, we present a new linear model for transforming desired beam configuration to tendon displacements and vice versa. We begin from first principles in solid mechanics by analyzing the effects of geometrically nonlinear tendon loads. These loads act both distally at the termination point and proximally along the conduit contact interface. The resulting model simplifies to a linear system including only the bending and axial modes of the manipulator as well as the actuator compliance. The model is then manipulated to form a concise mapping from beam configuration-space parameters to n redundant tendon displacements via the internal loads and strains experienced by the system. We demonstrate the utility of this model by implementing an optimal feasible controller. The controller regulates axial strain to a constant value while guaranteeing positive tendon forces and minimizing their magnitudes over a range of articulations. The mechanics-based model from this study provides insight as well as performance gains for this increasingly ubiquitous class of manipulators.
576 citations
TL;DR: This work presents an online method that makes it possible to detect when an image comes from an already perceived scene using local shape and color information, and extends the bag-of-words method used in image classification to incremental conditions and relies on Bayesian filtering to estimate loop-closure probability.
Abstract: In robotic applications of visual simultaneous localization and mapping techniques, loop-closure detection and global localization are two issues that require the capacity to recognize a previously visited place from current camera measurements. We present an online method that makes it possible to detect when an image comes from an already perceived scene using local shape and color information. Our approach extends the bag-of-words method used in image classification to incremental conditions and relies on Bayesian filtering to estimate loop-closure probability. We demonstrate the efficiency of our solution by real-time loop-closure detection under strong perceptual aliasing conditions in both indoor and outdoor image sequences taken with a handheld camera.
521 citations
TL;DR: Experiments showed that users were able to successfully operate the device in the three control strategies, and that the grasp success increased with more interactive control, and whether a vibrotactile feedback system is subjectively or objectively useful and how it changes users' performance.
Abstract: An anthropomorphic underactuated prosthetic hand, endowed with position and force sensors and controlled by means of myoelectric commands, is used to perform experiments of hierarchical shared control. Three different hierarchical control strategies combined with a vibrotactile feedback system have been developed and tested by able-bodied subjects through grasping tasks used in activities of daily living (ADLs). The first goal is to find a good tradeoff between good grasping capabilities and low attention required by the user to complete grasping tasks, without addressing advanced algorithm for electromyographic processing. The second goal is to understand whether a vibrotactile feedback system is subjectively or objectively useful and how it changes users' performance. Experiments showed that users were able to successfully operate the device in the three control strategies, and that the grasp success increased with more interactive control. Practice has proven that when too much effort is required, subjects do not do their best, preferring, instead, a less-interactive control strategy. Moreover, the experiments showed that when grasping tasks are performed under visual control, the enhanced proprioception offered by a vibrotactile system is practically not exploited. Nevertheless, in subjective opinion, feedback seems to be quite important.
431 citations
TL;DR: This work presents a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots and demonstrates successful learning in the real world by having an humanoid robot interacting with objects.
Abstract: Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.
385 citations
TL;DR: An extended Kalman filter is presented for precisely determining the unknown transformation between a camera and an IMU and it is proved that the nonlinear system describing the IMU-camera calibration process is observable.
Abstract: Vision-aided inertial navigation systems (V-INSs) can provide precise state estimates for the 3-D motion of a vehicle when no external references (e.g., GPS) are available. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera under the assumption that the rigid transformation between the two sensors is known. Errors in the IMU-camera extrinsic calibration process cause biases that reduce the estimation accuracy and can even lead to divergence of any estimator processing the measurements from both sensors. In this paper, we present an extended Kalman filter for precisely determining the unknown transformation between a camera and an IMU. Contrary to previous approaches, we explicitly account for the time correlation of the IMU measurements and provide a figure of merit (covariance) for the estimated transformation. The proposed method does not require any special hardware (such as spin table or 3-D laser scanner) except a calibration target. Furthermore, we employ the observability rank criterion based on Lie derivatives and prove that the nonlinear system describing the IMU-camera calibration process is observable. Simulation and experimental results are presented that validate the proposed method and quantify its accuracy.
TL;DR: Experimental results on an experimental UAV known as an X4-flyer made by the French Atomic Energy Commission (CEA) demonstrate the robustness and performances of the proposed control strategy.
Abstract: An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data, along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller, that ensures exponential convergence of the system considered, is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV, developed by the French Atomic Energy Commission, demonstrate the robustness and performance of the proposed control strategy.
TL;DR: A biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms based on computational models of the rodent hippocampus is described, coupled with a lightweight vision system that provides odometry and appearance information.
Abstract: This paper describes a biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms. The core SLAM system, dubbed RatSLAM, is based on computational models of the rodent hippocampus, and is coupled with a lightweight vision system that provides odometry and appearance information. RatSLAM builds a map in an online manner, driving loop closure and relocalization through sequences of familiar visual scenes. Visual ambiguity is managed by maintaining multiple competing vehicle pose estimates, while cumulative errors in odometry are corrected after loop closure by a map correction algorithm. We demonstrate the mapping performance of the system on a 66 km car journey through a complex suburban road network. Using only a web camera operating at 10 Hz, RatSLAM generates a coherent map of the entire environment at real-time speed, correctly closing more than 51 loops of up to 5 km in length.
TL;DR: A human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation and a calibration algorithm is presented that relies exclusively on sensors mounted on the exoskeleton.
Abstract: This paper presents a human--machine interface to control exoskeletons that utilizes electrical signals from the muscles of the operator as the main means of information transportation. These signals are recorded with electrodes attached to the skin on top of selected muscles and reflect the activation of the observed muscle. They are evaluated by a sophisticated but simplified biomechanical model of the human body to derive the desired action of the operator. A support action is computed in accordance to the desired action and is executed by the exoskeleton. The biomechanical model fuses results from different biomechanical and biomedical research groups and performs a sensible simplification considering the intended application. Some of the model parameters reflect properties of the individual human operator and his or her current body state. A calibration algorithm for these parameters is presented that relies exclusively on sensors mounted on the exoskeleton. An exoskeleton for knee joint support was designed and constructed to verify the model and to investigate the interaction between operator and machine in experiments with force support during everyday movements.
TL;DR: A novel type of impedance controllers for flexible joint robots with physical interpretation as a scaling of the motor inertia and the physical interpretation of torque feedback allows a proof of the asymptotic stability of the closed-loop system based on the passivity properties of the system.
Abstract: In this paper, a novel type of impedance controllers for flexible joint robots is proposed. As a target impedance, a desired stiffness and damping are considered without inertia shaping. For this problem, two controllers of different complexity are proposed. Both have a cascaded structure with an inner torque feedback loop and an outer impedance controller. For the torque feedback, a physical interpretation as a scaling of the motor inertia is given, which allows to incorporate the torque feedback into a passivity-based analysis. The outer impedance control law is then designed differently for the two controllers. In the first approach, the stiffness and damping terms and the gravity compensation term are designed separately. This outer control loop uses only the motor position and velocity, but no noncollocated feedback of the joint torques or link side positions. In combination with the physical interpretation of torque feedback, this allows us to give a proof of the asymptotic stability of the closed-loop system based on the passivity properties of the system. The second control law is a refinement of this approach, in which the gravity compensation and the stiffness implementation are designed in a combined way. Thereby, a desired static stiffness relationship is obtained exactly. Additionally, some extensions of the controller to viscoelastic joints and to Cartesian impedance control are given. Finally, some experiments with the German Aerospace Center (DLR) lightweight robots verify the developed controllers and show the efficiency of the proposed control approach.
TL;DR: A new approach for modeling soft robotic manipulators that incorporates the effect of material nonlinearities and distributed weight and payload is presented, based on the geometrically exact Cosserat rod theory and a fiber reinforced model of the air muscle actuators.
Abstract: Unlike traditional rigid linked robots, soft robotic manipulators can bend into a wide variety of complex shapes due to control inputs and gravitational loading This paper presents a new approach for modeling soft robotic manipulators that incorporates the effect of material nonlinearities and distributed weight and payload The model is geometrically exact for the large curvature, shear, torsion, and extension that often occur in these manipulators The model is based on the geometrically exact Cosserat rod theory and a fiber reinforced model of the air muscle actuators The model is validated experimentally on the OctArm V manipulator, showing less than 5% average error for a wide range of actuation pressures and base orientations as compared to almost 50% average error for the constant-curvature model previously used by researchers Workspace plots generated from the model show the significant effects of self-weight on OctArm V
TL;DR: The presented study allows force sensing in challenging environments where placing force sensors at the distal end of a robot is not possible due to limitations such as size and MRI compatibility.
Abstract: This paper presents the theoretical analysis and the experimental validation of the force sensing capabilities of continuum robots. These robots employ superelastic NiTi backbones and actuation redundancy. The paper uses screw theory to analyze the limitations and provide geometric interpretation to the sensible wrenches. The analysis is based on the singular value decomposition of the Jacobian mapping between the configuration space and the twist space of the end effector. The results show that the sensible wrenches belong to a 2-D screw system and the insensible wrenches belong to a 4-D screw system. The theory presented in this paper is validated through simulations and experiments. It is shown that the force sensing errors have an average of 0.34 g with a standard deviation of 0.83 g. Another experiment of generating the stiffness map of a silicone strip suggests possible medical application of palpation for tumor detection. The presented study allows force sensing in challenging environments where placing force sensors at the distal end of a robot is not possible due to limitations such as size and MRI compatibility.
TL;DR: This work addresses the challenge of distributed motion algorithms that guarantee connectivity of the overall network using a key control decomposition of graphs as combinatorial objects and shows that the resulting motion always ensures connectivity ofThe network, while it reconfigures toward certain secondary objectives.
Abstract: Control of mobile networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multiagent systems, a great new challenge is the development of distributed motion algorithms that guarantee connectivity of the overall network. Motivated by the inherently discrete nature of graphs as combinatorial objects, we address this challenge using a key control decomposition. First, connectivity control of the network structure is performed in the discrete space of graphs and relies on local estimates of the network topology used, along with algebraic graph theory, to verify link deletions with respect to connectivity. Tie breaking, when multiple such link deletions can violate connectivity, is achieved by means of gossip algorithms and distributed market-based control. Second, motion control is performed in the continuous configuration space, where nearest-neighbor potential fields are used to maintain existing links in the network. Integration of the earlier controllers results in a distributed, multiagent, hybrid system, for which we show that the resulting motion always ensures connectivity of the network, while it reconfigures toward certain secondary objectives. Our approach can also account for communication time delays as well as collision avoidance and is illustrated in nontrivial computer simulations.
TL;DR: A real-time algorithm for computing the ego-motion of a vehicle relative to the road using only those images provided by a single omnidirectional camera mounted on the roof of the vehicle and using image mosaicing to obtain a textured 2-D reconstruction of the estimated path.
Abstract: In this paper, we describe a real-time algorithm for computing the ego-motion of a vehicle relative to the road. The algorithm uses as input only those images provided by a single omnidirectional camera mounted on the roof of the vehicle. The front ends of the system are two different trackers. The first one is a homography-based tracker that detects and matches robust scale-invariant features that most likely belong to the ground plane. The second one uses an appearance-based approach and gives high-resolution estimates of the rotation of the vehicle. This planar pose estimation method has been successfully applied to videos from an automotive platform. We give an example of camera trajectory estimated purely from omnidirectional images over a distance of 400 m. For performance evaluation, the estimated path is superimposed onto a satellite image. In the end, we use image mosaicing to obtain a textured 2-D reconstruction of the estimated path.
TL;DR: This article uses a locomotion controller based on the biological concept of central pattern generators (CPGs) together with a gradient-free optimization method, Powell's method, to identify fast swimming and crawling gaits for a variety of environments.
Abstract: An important problem in the control of locomotion of robots with multiple degrees of freedom (e.g., biomimetic robots) is to adapt the locomotor patterns to the properties of the environment. This article addresses this problem for the locomotion of an amphibious snake robot, and aims at identifying fast swimming and crawling gaits for a variety of environments. Our approach uses a locomotion controller based on the biological concept of central pattern generators (CPGs) together with a gradient-free optimization method, Powell's method. A key aspect of our approach is that the gaits are optimized online, i.e., while moving, rather than as an off-line optimization process. We present various experiments with the real robot and in simulation: swimming, crawling on horizontal ground, and crawling on slopes. For each of these different situations, the optimized gaits are compared with the results of systematic explorations of the parameter space. The main outcomes of the experiments are: 1) optimal gaits are significantly different from one medium to the other; 2) the optimums are usually peaked, i.e., speed rapidly becomes suboptimal when the parameters are moved away from the optimal values; 3) our approach finds optimal gaits in much fewer iterations than the systematic search; and 4) the CPG has no problem dealing with the abrupt parameter changes during the optimization process. The relevance for robotic locomotion control is discussed.
TL;DR: This paper adopts an optimal filtering approach to fusing local sensor data into a global model of the environment based on the use of proportional-integral average consensus estimators, whereby information from each mobile sensor diffuses through the communication network.
Abstract: Cooperating mobile sensors can be used to model environmental functions such as the temperature or salinity of a region of ocean. In this paper, we adopt an optimal filtering approach to fusing local sensor data into a global model of the environment. Our approach is based on the use of proportional-integral (PI) average consensus estimators, whereby information from each mobile sensor diffuses through the communication network. As a result, this approach is scalable and fully decentralized, and allows changing network topologies and anonymous agents to be added and subtracted at any time. We also derive control laws for mobile sensors to move to maximize their sensory information relative to current uncertainties in the model. The approach is demonstrated by simulations including modeling ocean temperature.
TL;DR: Experimental results indicated that there is a relationship between negative attitudes and emotions, and communication avoidance behavior, which have important implications for robotics design.
Abstract: When people interact with communication robots in daily life, their attitudes and emotions toward the robots affect their behavior. From the perspective of robotics design, we need to investigate the influences of these attitudes and emotions on human-robot interaction. This paper reports our empirical study on the relationships between people's attitudes and emotions, and their behavior toward a robot. In particular, we focused on negative attitudes, anxiety, and communication avoidance behavior, which have important implications for robotics design. For this purpose, we used two psychological scales that we had developed: negative attitudes toward robots scale (NARS) and robot anxiety scale (RAS). In the experiment, subjects and a humanoid robot are engaged in simple interactions including scenes of meeting, greeting, self-disclosure, and physical contact. Experimental results indicated that there is a relationship between negative attitudes and emotions, and communication avoidance behavior. A gender effect was also suggested.
TL;DR: A system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor, which accommodates both monocular and stereo.
Abstract: In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3-D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3-D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other vision-only SLAM systems, stereo does not suffer from ldquoscale driftrdquo because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, we show experimental results in indoor and outdoor urban environments of 210 m and 140 m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4--5 km/h.
TL;DR: It is proved that it is indeed possible to achieve stable behavior with simple PD-like schemes-even without the delayed derivative action-under the classical assumption of passivity of the terminal operators.
Abstract: In a recent scheme, with delayed derivative action [Lee and Spong, IEEE Trans. Robot., vol. 22, no. 2, pp. 269--281, Apr. 2006], it is claimed that a simple proportional derivative (PD) scheme yields a stable operation. Unfortunately, the stability proof hinges upon unverifiable assumptions on the human and contact environment operators, namely, that they define Linfin-stable maps from velocity to force. In this short paper, we prove that it is indeed possible to achieve stable behavior with simple PD-like schemes-even without the delayed derivative action-under the classical assumption of passivity of the terminal operators.
TL;DR: A robust new algorithm based on the scaled unscented transformation called unscenting FastSLAM (UFastSLAM) is provided, which overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations.
Abstract: The Rao-Blackwellized particle filter (RBPF) and FastSLAM have two important limitations, which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. These can make the filter inconsistent. Another challenge is to reduce the number of particles while maintaining the estimation accuracy. This paper provides a robust new algorithm based on the scaled unscented transformation called unscented FastSLAM (UFastSLAM). It overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations. This approach improves the filter consistency and state estimation accuracy, and requires smaller number of particles than the FastSLAM approach. Simulation results in large-scale environments and experimental results with a benchmark dataset are presented, demonstrating the superiority of the UFastSLAM algorithm.
TL;DR: An innovative tactile display device based on the soft actuator technology that can provide stimulation on the human skin without any additional electromechanical transmission is presented.
Abstract: As a major human sensory function, the implementation of the tactile sensation for the human-machine interface has been one of the core research interests for long time. In this paper, an innovative tactile display device based on the soft actuator technology is presented. Using electroactive polymer for the construction of the tactile display device, it can provide stimulation on the human skin without any additional electromechanical transmission. Softness and flexibility of the device structure, ease of fabrication, possibility for miniaturization, and low cost for mass production are the representative benefits of the presented device. Especially, the device application is open to many different purposes since the flexible structure offers the excellent adaptability to any contour of the human body. To prove its feasibility, a wearable device that can fit to the distal part of the human finger is presented and its performance is evaluated, experimentally.
TL;DR: How to obtain all feasible inverse kinematic solutions in the global configuration space where joint movable ranges are limited and analytical methods to avoid joint limits are developed in the position domain are focused on.
Abstract: This paper proposes an analytical methodology of inverse kinematic computation for 7 DOF redundant manipulators with joint limits. Specifically, the paper focuses on how to obtain all feasible inverse kinematic solutions in the global configuration space where joint movable ranges are limited. First, a closed-form inverse kinematic solution is derived based on a parameterization method. Second, how the joint limits affect the feasibility of the inverse solution is investigated to develop an analytical method for computing feasible solutions under the joint limits. Third, how to apply the method to the redundancy resolution problem is discussed and analytical methods to avoid joint limits are developed in the position domain. Lastly, the validity of the methods is verified by kinematic simulations.
TL;DR: This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator.
Abstract: We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment. It combines a dynamical system control approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator. The system is validated on two experiments involving a humanoid robot: putting an object into a box and reaching for and grasping an object.
TL;DR: This paper poses the cooperative perimeter-surveillance problem and offers a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members by identifying and sharing the critical coordination information and by exploiting the known communication topology.
Abstract: This paper poses the cooperative perimeter-surveillance problem and offers a decentralized solution that accounts for perimeter growth (expanding or contracting) and insertion/deletion of team members. By identifying and sharing the critical coordination information and by exploiting the known communication topology, only a small communication range is required for accurate performance. Simulation and hardware results are presented that demonstrate the applicability of the solution.
TL;DR: The successful development of the tactile sensor system of the human-interactive robot named RI-MAN, which can lift up a dummy human, is reported.
Abstract: Human-interactive robots, such as those used for nursing, which share humans' environments and interact with them, should be covered with soft areal tactile sensors for safety and dexterous manipulation We report the successful development of the tactile sensor system of our human-interactive robot named RI-MAN, which can lift up a dummy human