ICRA 2015 论文列表

In order to realize many of the potential benefits associated with robotically assisted minimally invasive surgery, the robot must be more than a remote controlled device. Currently using a surgical robot can be challenging, fatiguing, and time consuming. Teaching the robot to actively assist surgical tasks, such as suturing, has the potential to vastly improve both patient outlook and the surgeon's efficiency. One obstacle to completing surgical sutures autonomously is the difficulty in tracking surgical suture threads. This paper proposes an algorithm which uses a Non-Uniform Rational B-Spline (NURBS) curve to model a suture thread. The NURBS model is initialized from a single selected point located on the thread. The NURBS curve is optimized by minimizing the image match energy between the projected stereo NURBS image and the segmented thread image. The algorithm is able to accurately track a suture thread as it translates, deforms, and changes length in real-time.

Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral viewpoint- and condition-invariant, vision-based place recognition. Our successive frame patch-tracking technique infers average scene depth along traverses and automatically rescales views of the same place at different depths to increase their similarity. We combine our system with the condition-invariant SMART algorithm and demonstrate place recognition between day and night, across entire 4-lane-plus-median-strip roads, where current algorithms fail.

Networks of laser range finders are a popular tool for monitoring large cluttered areas and to track people. Whenever multiple scanners are used for this purpose, one major problem is how to determine the relative positions of all the scanners. In this paper, we present a novel approach to calibrate a network of multiple planar laser range finders scanning horizontally. To robustly deal with the potentially restricted overlap between the fields of view, our approach only requires a dynamic object, e.g., a person, moving through the observed area. We employ a RANSAC-like algorithm to find the correspondences between the measurements of the different laser range finders. Based on these correspondences we formulate a graph-based optimization problem to determine the maximum likelihood extrinsic parameters of the sensor network. Furthermore, we present a method to evaluate the consistency of the resulting calibration based on visibility constraints. Experiments on real and simulated data show that the proposed approach yields better results than techniques that only perform pairwise calibration.

One of the challenges in precision agriculture is the detection of diseased crops in agricultural environments. This paper presents a methodology to detect the Ceratocystis wilt disease in Eucalyptus crops. An unmanned aerial vehicle is used to obtain high-resolution RGB images of a predefined area. The methodology enables the extraction of visual features from image regions and uses several supervised machine learning (ML) techniques to classify regions into three classes: ground, healthy and diseased plants. Several learning techniques were compared using data obtained from a commercial Eucalyptus plantation. Experimental results show that the GP learning model is more reliable than the other learning methods for accurately identifying diseased trees.

We address the problem of deciding what information a robot should transmit to the outside world, by exploring a setting where some information (e.g., current status of the task) must be shared in order for the robot to be useful, but where, simultaneously, we wish to impose limits which ensure certain information is never divulged. These sorts of conditions arise in several circumstances of increasing relevance: robots that can provide some guarantee of privacy to their users, controllers which safely use untrusted “cloud” services or smart-space infrastructure, or robots that act as inspection devices in information-sensitive contexts (e.g., factories, nuclear plants, etc.) We introduce an algorithm which takes as input an arbitrary combinatorial filter, expressed as a transition graph, and a set of constraints, constituting both upper and lower bounds, that specify the desired informational properties. The algorithm produces a coarser version of the input filter which possesses the desired informational properties, if and only if such a filter exists. We show that determining whether it is possible to satisfy both the distinguishablity and indistinguishablity constraints is NP-hard. The hardness result helps justify the worst-case running time of the algorithm. We describe an implementation of the algorithm along with empirical results showing that, beyond some minimum problem complexity, the algorithm is faster than naïve filter enumeration, albeit with greater memory requirements.

Wireless capsule endoscopy (WCE) is a revolutionary imaging technique that enables direct inspection of the gastrointestinal tract in a non-invasive way. However, viewing the large amounts of images is a very time-consuming and labor intensive task for clinicians. In this paper, we propose an automatic bleeding detection method in the WCE images. We propose a two-stage saliency map extraction method to highlight bleeding regions where the first-stage saliency map is created by means of different color channels mixer and the second-stage saliency map is obtained from the visual contrast in the RGB color space. Followed by an appropriate fusion strategy and threshold, we localize the bleeding areas in the WCE images. Then we extract statistic color features in the corresponding saliency region and non-saliency region respectively and fuse them together to represent the whole WCE images. Finally Support Vector Machine (SVM) is applied to carry out the experiment on 800 sample WCE images. Experiment result achieves an accuracy of 95.89%, sensitivity of 98.77% and specificity of 93.45%. This inspiring result demonstrates that the proposed method is very effective in detecting bleeding patterns in the WCE images. Our comparison studies with several state-of-the-art bleeding detection methods confirm that the proposed method achieves much better results than those of the alternative techniques.

This paper reports the first robotic system for vitrification of mammalian embryos. Vitrification is a technique for preserving oocytes and embryos in clinical IVF (in vitro fertilization). The procedure involves multiple steps of stringently timed pick-and-place operation for processing an oocyte/embryo in vitrification media. In IVF clinics, vitrification is conducted manually by highly skilled embryologists. Processing one oocyte/embryo occupies the embryologist 15–20 minutes, depending on protocols chosen to implement. Due to poor reproducibility and inconsistency across operators, success rates and survival rates also vary significantly. Through collaboration with IVF clinics, we are in process to realize robotic vitrification and aim ultimately to standardize clinical vitrification from manual operation to fully automated robotic operation. Our robotic system is embedded with two contact detection methods to determine the relative Z positions of the vitrification micropipette, embryo, and vitrification straw. A 3D tracking algorithm is developed for visually servoed embryo transfer and real-time monitoring of embryo volume changes during vitrification. Excess medium is automatically removed from around the vitrified embryo on the vitrification straw to achieve a high cooling rate. Tests on mouse embryos demonstrate that the system is capable of performing vitrification with a throughput at least three times that of manual operation and achieved a high survival rate (88.9%) and development rate (93.8%).

Micro- and nanosized objects aligned in specific spatial order are of great interest for applications in photonics and nanoelectronics. In particular, piezo-actuated robotic setups are promising tools to arrange and manipulate these objects individually. However, automated robotic processing on the sub-micron scale remains challenging due to the force scaling laws and the limited possibilities in terms of control. This paper presents the current progress on fully-automated pick-and-place routines of individual colloidal particles using a dedicated dual-probe setup inside a scanning electron microscope. Applying tailored probes in combination with image processing of the visual feedback provided by the microscope allow for complex automation sequences. The limits of the current technique are highlighted and the challenges for automated processing of progressively smaller particles are discussed.

Mechanical force is an integral part of tissue morphogenesis and patterning. We have developed an automated micro-aspiration system to investigate how mouse limb bud tissue responds to extrinsic forces in order to understand whether tissue-generated forces can be a part of the mechanism causing oriented cell behaviors observed in mouse limb bud morphogenesis. The system is capable of performing automated micropipette tracking, tissue-tip contact detection, pressure control, and prolonged application of constant pressure. A three-dimensional tissue tracking algorithm is developed based on the processing of time-lapsed confocal Z-stack images. 3D visual feedback from confocal microscopy imaging, for the first time, is used to realize 3D visual servoing to control the micropipette position to compensate for tissue movement. This enables stable force application in a biologically relevant time scale (e.g., 60 minutes) during which cell remodeling occurs. Experimental results demonstrate that micro-aspiration on mouse limb bud is capable of creating tension anisotropy which causes force-responsive cells to dynamically remodel through polarized cell division and rosette resolution.

We address the problem of predicting the physical state of a an agent performing a known activity. In particular we are interested in predicting human movement during complex composite activities. Our proposed framework combines a graphical model that extends the Sequential Interval Network (SIN) [1] for modeling global temporal structure of activities with a low level dynamic system for modeling the dynamics of the physical state. Specifically, two sets of new hidden state variables are added: one with respect to the temporal structure and one with respect to time. A mapping factor is defined to ensure these variables values remain consistent and hence allows fusing the two sources of information. We then derive an inference algorithm for computing the posterior densities of the hidden variables. The system can run in an on-line predictive mode to recognize on-going activity and make predictions arbitrarily far in the future during execution of the activity. Experiments illustrate that the long term prediction performance benefits from the knowledge about the temporal structure of the activity while short term prediction performance is improved by incorporating the dynamics of physical state.

This paper addresses a method for stabilizing the attitude of a quadrotor-like Unmanned Aerial Vehicle (UAV). Towards this end, a new fuzzy control technique based on Linear Matrix Inequalities (LMIs) is proposed, relying on a simple Takagi-Sugeno (TS) fuzzy model when compared to others found in the Literature. This strategy guarantees prescribed performance criteria, such as exponential decay rate, allowing stability under aggressive disturbances. The designed local gains are combined through membership functions of the model and are used to regulate the attitude of the vehicle on the hovering operating point, an important flight mode for this kind of robot. Numerical analysis reveal how the transient response can be tuned and tests with disturbances show the stability of the vehicle starting from different initial conditions, illustrating the merits of the proposed approach.

This paper presents a method for stabilizing the attitude of a Hybrid Unmanned Aerial Underwater Vehicle. Firstly, we present aerodynamic and hydrodynamic models for the angular motion of our robot, discussing effects like buoyancy force and added inertia. Next, we apply robust control techniques for both environment, aerial and underwater, based on linear uncertain models with only four vertices and well-defined stability criteria, such as D-stability and ℋ2 performance. Gain matrices Kair and Kwat are computed and the attitude of the vehicle at the hovering operation point for each environment is controlled, respectively. Finally, a procedure is proposed to check the global stability for the switching control case, when the robot changes from air to water (or vice-versa). Numerical simulations with disturbances and switching control are presented to show the stability at different initial conditions.

The autonomous capabilities of light-weight Flapping Wing Micro Air Vehicles (FWMAVs) have much to gain from onboard state estimation and attitude control. In this article, we present the first FWMAV with robust onboard state estimation and attitude control. The tailed FWMAV DelFly II was used, with the main goal to achieve active stabilization in the (passively unstable) hover condition. The attitude is estimated using an Inertial Measurement Unit with a gyroscope, accelerometer and magnetometer and the altitude is estimated using a barometer. A major challenge lies in the disturbance of the accelerometer measurements by the flapping motion of the wings. We propose a mechanical damping mechanism and flap-cycle based filtering to resolve this issue. The pitch estimates have a mean error of 1.5° with respect to the ground-truth measurement from a motion capture system. Using the onboard pitch estimate we can control the attitude of the FWMAV in the forward flight regime with a 30% lower standard deviation than in a trimmed flight. With a different set of gains, the FWMAV is able to perform a hovering flight - showing that a tailed FWMAV has enough control authority for this task. In a fully autonomous hover experiment, the DelFly II stays within a sphere of 0.75 m radius.

Indoor localization is a fundamental capability for service robots and indoor applications on mobile devices. To realize that, the cost and performance are of great concern. In this paper, we introduce a lightweight signal encoding and decomposition method for a low-cost and low-power Visible Light Communication (VLC)-based indoor localization system. Firstly, a Gold-sequence-based tiny-length code selection method is introduced for light encoding. Then a correlation-based asynchronous blind light-signal decomposition method is developed for the decomposition of the lights mixed with modulated light sources. It is able to decompose the mixed light-signal package in real-time. The average decomposition time-cost for each frame is 20 ms. By using the decomposition results, the localization system achieves accuracy at 0.56 m. These features outperform other existing low-cost indoor localization approaches, such as WiFiSLAM.

Many path-finding algorithms on graphs such as A* are sped up by using a heuristic function that gives lower bounds on the cost to reach the goal. Aiming to apply similar techniques to speed up sampling-based motion-planning algorithms, we use effective lower bounds on the cost between configurations to tightly estimate the cost-to-go. We then use these estimates in an anytime asymptotically-optimal algorithm which we call Motion Planning using Lower Bounds (MPLB). MPLB is based on the Fast Marching Trees (FMT*) algorithm [1] recently presented by Janson and Pavone. An advantage of our approach is that in many cases (especially as the number of samples grows) the weight of collision detection in the computation is almost negligible compared to the weight of nearest-neighbor queries. We prove that MPLB performs no more collision-detection calls than an anytime version of FMT*. Additionally, we demonstrate in simulations that for certain scenarios, the algorithmic tools presented here enable efficiently producing low-cost paths while spending only a small fraction of the running time on collision detection.

In this paper, a robotics-assisted articulating ultrasonic surgical scalpel for minimally invasive soft tissue cutting and coagulation is designed and developed. For this purpose, the optimal design of a Langevin transducer with stepped horn profile is presented for internal-body applications. The modeling, optimization and design of the ultrasonic scalpel are performed through equivalent circuit theory and verified by finite element analysis. Moreover, a novel two degrees-of-freedom (DOFs) surgical end effector (1-DOF pitch, 1-DOF grip) with decoupled motions is developed that is compatible with the da Vinci® surgical system. The developed instrument is then driven using the dVRK (da Vinci® research kit) and the Classic da Vinci® surgical system.

The best design among many can be selected through their accurate performance evaluation. When such evaluation is based on discrete event simulations, the design selection is extremely time-consuming. Ordinal optimization greatly speeds up this process. Optimal Computing-Budget Allocation (OCBA) has further accelerated it. Other kinds of OCBA have been introduced for reaching different goals, for example, to select the optimal subset of designs. However, facing the issue of subset ranking, which is a generalized form from problems selecting the best design or optimal subset, all the existing ones are insufficient. This work develops a new OCBA-based approach to address this subset ranking issue. Through mathematical deduction, its theoretical foundation is laid. Our numerical simulation results reveal that it indeed outperforms all the other existing methods in terms of probability of correct subset ranking and computational efficiency.

The task of Simultaneous Localization and Mapping (SLAM) is regularly performed in network spaces consisting of a set of corridors connecting locations in the space. Empirical research has demonstrated that such spaces generally exhibit common structural properties relating to aspects such as corridor length. Consequently there exists potential to improve performance through the placement of priors over these properties. In this work we propose an appearance-based SLAM method which explicitly models the space as a network and in turn uses this model as a platform to place priors over its structure. Relative to existing works, which implicitly assume a network space and place priors over its structure, this approach allows a more formal placement of priors. In order to achieve robustness, the proposed method is implemented within a multi-hypothesis tracking framework. Results achieved on two publicly available datasets demonstrate the proposed method outperforms a current state-of-the-art appearance-based SLAM method.

Robots are ever more present in human environments and effective physical human-robot interactions are essential to many applications. But to a person, these interactions rarely feel biological or equivalent to a human-human interactions. It is our goal to make robots feel more human-like, in the hopes of allowing more natural human-robot interactions. In this paper, we examine a novel biologically-inspired control method, emulating antagonistic muscle pairs based on a nonlinear Hill model. The controller captures the muscle properties and dynamics and is driven solely by muscle activation levels. A human-robot experiment compares this approach to PD and PID controllers with equivalent impedances as well as to direct human-human interactions. The results show the promise of driving motors like muscles and allowing users to experience robots much like humans.