ICRA 2015 论文列表

The scales on the skin of a snake are an integral part of the snake's locomotive capabilities. It stands to reason that the integration of scales into the design of robotic snakes would open new properties to exploit. In this work, we present a robotic snake design that incorporates rigid scales in the casing of each module. To validate the impact of the scales, locomotion is tested under three conditions: with scales, covered in cloth with scales, and covered in cloth without scales. The performance of the snake robot, in each of the aforementioned scenarios, is evaluated based on its forward displacement while executing each of two pre-programmed gaits: inchworm and lateral undulation. The lateral undulation gait is tested under two additional conditions: pitched and un-pitched. Tracks of the experimental runs are presented followed by a statistical analysis demonstrating an increase in locomotive performance when incorporating scales in the chassis design.

This work proposes a Human-Comfortable Path Planner (HCoPP) system for autonomous passenger vehicles. The aim is to create a path planner that improves the feeling of comfort of the passenger, this topic is different from collision free planning and it has not received much attention. For this purpose, in addition to the shortest distance constraint conventionally used in path planning, constraints related to relevant environmental features are introduced. For straight segments, the constraint is based on the lane-circulation pattern preferred by humans. In curved segments and intersections, the constraint takes into account the visibility. A multi-layered cost map is proposed to integrate these additional constraints. To compute the human-comfortable path, a graph search algorithm was implemented. The evaluation of the proposed approach was conducted by having 30 participants riding an autonomous robotic wheelchair. The paths computed by the proposed path planner were compared towards a state of the art shortest-distance path planner implemented in the navigation stack of ROS. Experimental results show that the paths computed by the proposed approach are perceived as more comfortable.

This paper proposes techniques to calibrate six-axis force-torque sensors that can be performed in situ, i.e., without removing the sensor from the hosting system. We assume that the force-torque sensor is attached to a rigid body equipped with an accelerometer. Then, the proposed calibration technique uses the measurements of the accelerometer, but requires neither the knowledge of the inertial parameters nor the orientation of the rigid body. The proposed method exploits the geometry induced by the model between the raw measurements of the sensor and the corresponding force-torque. The validation of the approach is performed by calibrating two six-axis force-torque sensors of the iCub humanoid robot.

The goal of this paper is to develop a regrasp planning algorithm general enough to perform statistical analysis with thousands of experiments and arbitrary mesh models. We focus on pick-and-place regrasp which reorients an object from one placement to another by using a sequence of pick-ups and place-downs. We improve the pick-and-place regrasp approach developed in 1990s and analyze its performance in robotic assembly with different work surfaces in the workcell. Our algorithm will automatically compute the stable placements of an object, find several force-closure grasps, generate a graph of regrasp actions, and search for regrasp sequences. We demonstrate the advantages of our algorithm with various mesh models and use the algorithm to evaluate the completeness, the cost and the length of regrasp sequences with different mesh models and different assembly tasks in the presence of different work surfaces. Our results show that spare work surfaces are beneficial to assembly. Tilted work surfaces are only sometimes beneficial, depending on the objects.

In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. This allows us to predict driving behavior over long time horizons with extremely high accuracy. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual and usable in semi-autonomous frameworks.

Inspired by achievements in rehabilitation, motor learning, and neuroscience, therapeutic robots are aiming to provoke neuromotor plasticity and improve recovery after stroke and mobility impairments. Human sensorimotor system is specialized with position and velocity sensory fibers and exhibits variant muscle impedance in accordance with the ongoing task. The virtually interfaced robotic ankle and balance trainer (vi-RABT) was introduced as a cost-effective platform-based rehabilitation robot to improve overall ankle / balance strength, mobility and control. This study is the first step toward assistive / resistive ankle rehabilitation using vi-RABT. We have implemented a task-dependent anisotropic impedance controller into the 2-DOF ankle rehabilitation robot. An objective virtual Maze game is developed. The controller is specifically designed for the Maze workspace; and exhibits elastic or pure viscos properties in compliant with the subject's direction of movement. Early results on two human subjects are presented.

The impedance shaping control with robot base dynamics compensation is presented in this paper. The method has been conceived to avoid force overshoots in applications where the coupled dynamics of the global system (compliant robot base - controlled robot - interacting compliant environment) affects the force tracking task. Force tracking performance are obtained tuning on-line both the position set-point and the stiffness and damping parameters, based on the force error, the estimated stiffness of the interacting environment (an Extended Kalman Filter is used) and the estimated robot base position (a Kalman Filter is used). The stability of the presented strategy has been studied through Lyapunov. To validate the performance of the control an assembly task is taken into account, considering the geometrical and mechanical properties of the (partially) unknown environment. Results are compared with constant stiffness and damping impedance controllers, which show force overshoots and instabilities.

Humans can easily memorize images of places and labels (road names, addresses, etc.) associated with them, as well as trajectories defined by sequences of images and corresponding positions. Later, they are able to remember places' labels and relative positions when seeing the same images again. In this work, we present an image-based mapping, global localization and position tracking system based on Virtual Generalizing Random Access Memory (VG-RAM) weightless neural networks, dubbed VIBML. VIBML mimics humans ability of learning about a place and of recognizing the same place in a later moment, as well as of tracking self-movement through the environment using images. We evaluated the performance of VIBML on the precise localization of an autonomous car using real-world datasets. Our experimental results showed that VIBML is able to localize car-like robots on large maps of real world environments with accuracy equivalent to that of state-of-the-art methods - VIBML is able to localize an autonomous car with average positioning error of 1.12m and with 75% of the poses with error below 1.5m in a 3.75km path around the main campus of the Federal University of Espírito Santo.

This paper presents a geometric approach to the problem of designing visual feedback control laws to steer a nonholonomic vehicle, equipped with a fixed monocular camera, through a gate. The originality of our approach is to introduce and exploit the natural geometry induced by the presence of a gate in the environment, e.g. bundle of hyperbolae, ellipses and circles, providing stabilizing feedback control laws that steer the vehicle through the middle of the gate. Moreover, using visual servoing we prove that this geometry can be measured directly in the camera image plane. As a consequence, we provide an image-based control scheme, avoiding the use of a state observer. Simulations in a realistic scenario and experiments are provided to show the effectiveness of our feedback control laws.

This paper presents a monocular visual SLAM system that does not require a globally consistent 3D model. Instead of generating a globally consistent 3D model and localising the camera from the 3D model, the system merely optimises relative pose parameters for pairs of keyframes that overlap on the scene, providing accurate local information at the expense of global consistency. During run-time, the camera is localised using only 2D measurements from nearby keyframes instead of using correspondences between 2D measurements and 3D features of a 3D model. Extensive experiments using both synthetic and real data sets were performed to evaluate the system's performance. Results show that our system is accurate and runs in real time at an average of 25 frames per second on a standard computer. Finally, we also show how useful applications can be easily developed on top of a framework without global consistency.

Retinal surgery is one of the most technically challenging surgical disciplines. Many robotic systems have been developed to enhance the surgical capabilities. However, very few of them provide the surgeon the dexterity within the patient's eye to enable more flexible, more advanced surgical procedures. This paper presents a sub-millimeter intraocular dexterous robot, the Integrated Robotic Intraocular Snake (IRIS). The variable neutral-line mechanism is used to provide very high dexterity with a very small form factor. The IRIS distal dexterous unit is 0.9 mm in diameter and about 3 mm in length. It enables two rotational degrees of freedom at the distal end of the ophthalmic instruments. The analysis on contact mechanics provides a reference for the adjustment of the wire pretension. Redundant actuation is implemented by using one motor for each wire. A motion scaling transmission is developed to overcome the suboptimal resolution of the motors. A scale-up model of the IRIS is built for initial experimental evaluation. Preliminary results show that the scale-up IRIS can provide large range of motion. For given bending angle, the kinematic model can estimate the desired wire translation when the friction is not significant. The first prototype of the actual-scale IRIS is assembled and tested.

Reconfigurable robots have the potential for use on a much wider range of tasks than a typical, fixed-configuration robot because they may be modified to accommodate the specific task at hand. Such reconfiguration, however, necessitates the generation of a new kinematics model to allow performance of a task in Cartesian space. Whereas previous work has typically relied on either a known kit of parts from which the robot is constructed or a numerical search technique that is not guaranteed to converge, this paper presents an analytically-based kinematic identification scheme for an all-revolute, serial-link manipulator. Experimental results are presented investigating the practical utility of the technique.

In robotics applications such as SLAM (Simultaneous Localization and Mapping), loop closure detection is an integral component required to build a consistent topological or metric map. This paper presents an appearance based loop closure detection mechanism titled `IBuILD' (Incremental bag of BInary words for Appearance based Loop closure Detection). The presented approach focuses on an online, incremental formulation of binary vocabulary generation for loop closure detection. The proposed approach does not require a prior vocabulary learning phase and relies purely on the appearance of the scene for loop closure detection without the need of odometry or GPS estimates. The vocabulary generation process is based on feature tracking between consecutive images to incorporate pose invariance. In addition, this process is coupled with a simple likelihood function to generate the most suitable loop closure candidate and a temporal consistency constraint to filter out inconsistent loop closures. Evaluation on different publicly available outdoor urban and indoor datasets shows that the presented approach is capable of generating higher recall at 100% precision in comparison to the state of the art.

Hybrid zero dynamics (HZD) has emerged as a popular framework for the stable control of bipedal robotic gaits, but typically designing a gait's virtual constraints is a slow and undependable optimization process. To expedite and boost the reliability of HZD gait generation, we borrow methods from trajectory optimization to formulate a smoother and more linear optimization problem. We present a multiple-shooting formulation for the optimization of virtual constraints, combining the stability-friendly properties of HZD with an optimization-conducive problem formulation. To showcase the implications of this recipe for improving gait generation, we use the same process to generate periodic planar walking gaits on two different robot models, and in one case, demonstrate stable walking on the hardware prototype, DURUS-R.

Direct photometric visual servoing uses only the pure image information as a visual feature, instead of using classic geometric features such as points or lines. It was demonstrated efficiently in 6 degrees of freedom (DoF) positioning. However, in micro-scale, using only image intensity as a visual feature performs unsatisfactorily in cases where the photometric variation is low, such as motions along vision sensor's focal axis under a high magnification. In order to improve the performance and accuracy in those cases, an approach using hybrid visual features is proposed in this paper. Image gradient is employed as a visual feature on z axis while image intensity is used on the other 5 DoFs to control the motion. A 6-DoF micro-positioning task is accomplished by this hybrid visual servoing scheme. The experimental results obtained on a parallel positioning micro-stage under a digital microscope show the robustness and efficiency of the proposed method.

Mobile, interactive robots that operate in human-centric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closed-loop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot co-navigation.

In this paper we propose a human-in-the-loop approach for teaching robots how to solve part assembly tasks. In the proposed setup the human tutor controls the robot through a haptic interface and a hand-held impedance control interface. The impedance control interface is based on a linear spring-return potentiometer that maps the button position to the robot arm stiffness. This setup allows the tutor to modulate the robot compliance based on the given task requirements. The demonstrated motion and stiffness trajectories are encoded using Dynamical Movement Primitives and learnt using Locally Weight Regression. To validate the proposed approach we performed experiments using Kuka Light Weight Robot and HapticMaster robot. The task of the experiment was to teach the robot how to perform an assembly task involving sliding a bolt fitting inside a groove in order to mount two parts together. Different stiffness was required in different stages of the task execution to accommodate the interaction of the robot with the environment and possible human-robot cooperation.

Following a person is a fundamental requirement for human-robot interaction. In this paper we propose a novel tracking approach for robust human feet tracking which integrates human locomotion into tracking algorithms. The vertical displacement between the two feet is analyzed and we observe that this displacement during the walking cycle is close to a modulated cosine waveform. Based on this, we propose an adaptive model for the human walking pattern. We divide the motion of the human feet into local motion and global motion. The local motion is modeled by a modified cosine wave that updates along time. Global motion is estimated by the continuity between successive frames. This model is combined with particle filtering to guide the searching of the feet. A 2D Gaussian mask is generated according to the predicted position estimated by the motion model and used to modify the weight of the particles. Experiments are implemented in several human walking videos and the algorithm is evaluated against the generic particle filtering method. Results show that the feet can be tracked successfully with significant improvements compared to the generic particle filtering method.

Human activity recognition has become an increasingly important field of research with many practical applications related to health care and leisure activities. The accessibility of inexpensive portable sensors, such as accelerometers, allows for a widespread use of this technology for both commercial and personal activity recognition. This paper develops a novel feature extraction approach to human activity recognition through the development of the Lempel-Ziv-Welch Coded Probabilistic Finite State Automata (LZW-Coded PFSA) to classify activities such as walking, jumping, running, waist rotations, and shoulder rotations. The PFSA reveal the underlying architecture of a given activity and classify it without making any a priori assumptions by inferring patterns from the sensor measurements. LZW-Coded PFSA select the optimal variable length state from the time-series data and compress it into class-separable state transition matrices π. This algorithm is robust to subject biases and is shown to be effective with a correct classification rate of 95.63%.