IROS 2016 Papers

Ribbon folding is a new approach to structure formation that forms higher dimensional structures using a lower dimensional primitive, namely a ribbon. In this paper, we present a novel algorithm to address path planning for ribbon folding of multi-link planar structures. We first represent the desired structure with a graph-based representation of edges and nodes. We then use graph theory to claim that for any object which is represented by a connected graph, there exists a continuous path which visits all of its edges. Finally, we develop a path planning algorithm that takes into account the physical constraints of the folding machine. The input is the desired planar structure, and the output is the optimal sequence of ribbon folds for creating that structure using the minimum number of folds. The results of this algorithm are successfully used to fold various planar structures.

Admittance control is a well-established and popular control strategy in modern robotics. However, the standard admittance controller has several limitations. First, the passivity can be guaranteed by finding a set of control parameters that makes the admittance function positive real. However, this approach cannot be applied to multi degrees-of-freedom robot because it requires transfer function analysis. Second, standard admittance controller is not suitable when the system is exposed to unexpected environmental interaction (of which interaction force is not measured) due to the wall sticking effect. To overcome these limitations, this paper proposes an admittance controller of which structure can be constructed by feedback interconnection of passive subsystems. The proposed approach was verified using experiments and simulations.

We present a new palm design that features a dense array of micro-spines for the JPL Robosimian human-scale climbing robot. A linearly-constrained spine mechanism is introduced and analyzed using adhesion and stiffness models. This mechanism achieves a spine density of 19/cm2 and a mean adhesion of 67N (207kPa) on coarse concrete surfaces. The models are validated on two different surfaces with two sets of experiments. A 120×100mm palm consisting of 12 spine tiles and a pulley differential system for load sharing are designed and tested on 9 different surfaces. The mean shear adhesion goes up to 710N (183kPa) on concrete blocks. Design considerations include scaling efficiency and maximal single-spine force. Desirable properties for load sharing in the palm design are discussed.

Control of the exerted force on objects as well as the position of the actuator during a cell characterization operation has an essential role in achieving an efficient performance. In such operations, any undesired applied force could degrade the efficiency or make damages to the object. A challenging problem associated with application to cell characterization using a piezoelectric actuator is to design a suitable force-position switching control law and also to deal with nonlinear hysteretic behavior of such actuators. The main concern in this operation is to provide a smooth switching transition between position and force control modes. A modified Prandtl-Ishlinskii (PI) operator and its inverse are utilized for both identification and real time compensation of the hysteresis nonlinear behavior. By proposing an appropriate force-position switching control approach, the smooth transition between these two modes is guaranteed. The stability of each controller as well as the switching system is demonstrated analytically. Experimental results depict that the proposed approach achieves precise force-position control within each modes and also the switching remains smooth.

Despite successful demonstrations of outdoor walking by a few biped robots, performance measure for such robots has not been formally defined yet. The performance measure should be adequately defined by which one can evaluate how well the robot keeps from falling in the presence of disturbance. If such performance measure is suitably determined, designing a sort of optimal controller for stable outside walking would be possible. This paper firstly suggests to adopt the l∞-induced norm defined on the linearized Poincarè map as a novel performance measure for biped robots, in the presence of bounded persistent disturbance. In order to validate the measure, a nonlinear dynamic simulation is conducted by extending an existing simple model to walk on rough terrain, of which height variance is bounded by some maximum value. The measure exploited for the model is compared with the numerical result obtained from nonlinear dynamic simulation. Finally, an example of application of this measure is provided, which successfully predicts whether the system could overcome upcoming terrain roughness.

This work extends the drag-force enhanced quadrotor model by denoting the free stream air velocity as the difference between the ground speed and the wind speed. It is demonstrated that a relatively simple nonlinear observer is capable of estimating the local wind components, provided accelerometer and GPS-velocity measurements are available. We perform a wind tunnel experiment at various wind speeds using a quadrotor vehicle with a low-cost Inertial Measurement Unit (IMU) and a motion tracking system to provide accurate ground speed measurements. It is shown that the onboard Extended Kalman Filter (EKF) accurately estimates the wind components.

Wireless Capsule Endoscopy (WCE) allows physicians to examine the entire digestive system without any surgical operation. Although it provides a noninvasive imaging approach to access the gastrointestinal (GI) tract, the biggest drawback of this technology is the large numbers of images need to be diagnosed. In this paper, a global and local saliency coding (GLSAC) method is proposed to detect polyps from WCE images. We first extract the Scale Invariant Feature Transform (SIFT) features from image patches and apply K-means method on these features to obtain visual words. Since saliency is a fundamental characteristic of feature coding, a novel coding strategy: the global and local saliency coding (GLSAC) is proposed. The calculation of the global saliency coding and local saliency coding is obtained by the exponential function of feature differences to the nearest visual code and all visual words. Specifically, the local saliency strategy considers coding bases based on the distribution of the distances between the features and visual words adaptively. Furthermore, we present a concentric circle-based spatial-rotation-invariant pooling strategy to obtain final image features from the patch features. Experiment results achieve promising 91.13% accuracy and 92.51% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our strategy outperforms the state-of-the-art methods on the polyp recognition task.

How to achieve the required contouring tracking accuracy especially during high-speed and large-curvature contouring tasks, has always been an important problem in manufacturing applications. In this paper, a contouring error estimation method based on natural local approximation is used, and then the position-loop cross-coupled controller is proposed to reduce the estimated contouring error. The effectiveness and superiority of the natural local approximation method using on the position-loop cross-coupled control scheme are demonstrated through experiments on a biaxial linear motor drive servo system.

We relax parametric inference to a nonparametric representation towards more general solutions on factor graphs. We use the Bayes tree factorization to maximally exploit structure in the joint posterior thereby minimizing computation. We use kernel density estimation to represent a wider class of constraint beliefs, which naturally encapsulates multi-hypothesis and non-Gaussian inference. A variety of new uncertainty models can now be directly applied in the factor graph, and have the solver recover a potentially multi-modal posterior. For example, data association for loop closure proposals can be incorporated at inference time without further modifications to the factor graph. Our implementation of the presented algorithm is written entirely in the Julia language, exploiting high performance parallel computing. We show a larger scale use case with the well known Victoria park mapping and localization data set inferring over uncertain loop closures.

This paper presents a control design method with a new nonlinear disturbance observer using delayed estimates for motion control of an underwater vehicle-manipulator system (UVMS). In recent years, application of an underwater vehicle has been expected in various fields. Since modeling unknown hydrodynamic forces is difficult, nonlinear disturbance observers have been proposed in the aim of decreasing disturbances due to hydrodynamic forces. In this paper, we propose a new disturbance observer by combining a conventional disturbance observer and time delay control, and attempt to apply it to motion control of a UVMS. Further, an analysis of ultimate boundedness and numerical simulations are conducted to evaluate the proposed control system. The respective analysis and simulation results show that the proposed disturbance observer exhibits faster convergence and less estimation errors than the conventional one and hence the control system based on it can perform successful motion control.

In highly dynamic tasks that involve moving targets, planning is necessary to figure out when, where and how to intercept the target. In robotic table tennis in particular, motion planning can be very challenging due to time constraints, dimension of the search space and modelling uncertainties. To simplify the problem, conventional planning algorithms often rely on a fixed virtual hitting plane to construct robot striking trajectories. These algorithms however generate restrictive strokes and can result in unnatural strategies when compared with human playing. In this paper, we introduce a new trajectory generation framework for robotic table tennis. We use a free-time optimal control approach to construct a novel planning algorithm that does not involve a fixed hitting plane. Furthermore, we estimate the parameters of our prediction models using human demonstrations. The resulting trajectories have lower accelerations while the joint constraints are enforced at all times. Our algorithm returns the balls with a higher probability to the opponent's court in our realistic simulation environment when compared with a virtual hitting plane based method.

Robotic-assisted catheter insertion is becoming increasingly popular due to its potential applications including cardiac catheterization. Typically, catheters are tracked during insertion procedures to verify the location of the tip relative to anatomy or features of interest. To this end, many catheter tracking systems have been proposed in the literature. Current approaches such as visual servoing are computationally intensive and sometimes require harmful ionizing radiation (X-rays) for tip localization. Conversely, other approaches use 3D ultrasound probes which can be prohibitively expensive. In contrast, we propose an ultrasound-enabled robotic catheter tracking system that uses a 2D ultrasound probe and an active piezoelectric element to track the tip of a catheter. This approach has the potential to guide catheters from initial insertion, in a vein of the groin, to final placement at a target area inside of the heart. During the tracking process, no information from the ultrasound image is necessary; however, this information can be used to help clinicians guide the catheter or to perform diagnostic procedures. In this paper, we outline this procedure by first discussing the individual components of the system and then by describing our methodology for tracking the catheter tip. Next, we simulate the system in ROS to test its effectiveness, and finally we experimentally verify that a robotic arm equipped with a 2D ultrasound probe can track a catheter in a multi-vein phantom. Furthermore, the data collected during tracking can be used to virtually reconstruct the 3D structure of veins while tracking.

This paper presents a methodology for the development of a multi-axis force/torque sensor based on optoelectronic technology. The advantages of using this sensing principle are the low manufacturing costs, the simple fabrication, and the immunity to electrical noise. The force/ torque sensor makes use of six optical sensors: each sensor measures the displacement of a reflective surface that moves integrally with a simply-supported beam. The proposed mechanical structure allows for a variety of shapes on the mechanical structure to be easily adaptable to many robot applications. In this paper, we present a five-axis force/torque sensor based on this optoelectronic principle. To measure force/torque components, two identical three-DoF force/torque sensor structures (comprised of three beams) are mounted on top of each other. Photo sensors and mirrors are fixed inside the structure to measure the six beam deflections. In this paper, we describe the sensor structure, design, fabrication, calibration, and verify our sensor development methodology.

We propose a novel variable stiffness joint (VSJ) mechanism that achieves the desired stiffness by using elastic deformation of silicone rubber cushions (SRCs). This mechanism can absorb the forces from multiple direction by the flexibility of SRCs and guarantee high safety. To react adequately against unpredictable collision or to manipulate an unstable object skillfully, a joint should be designed to have a stiffness adjustment capability in a short time. The proposed mechanism adjusts stiffness by controlling the position of an elliptical cam that pushes or pulls the support wall of the SRCs to surround them. The elastic deformation of the SRCs can be restricted according to the degree of the surrounded region of the SRCs. As a result, we can control the joint stiffness easily. Furthermore, the rotation of the cam from 0 deg. to 90 deg. accomodates the entire range of stiffness;, as a result, we can expect a fast adjustment of the stiffness. In this paper, we describe in detail the mechanism and the theoretical model for the stiffness adjustment. Moreover fundamental performances on the stiffness range, adjustment speed, and movability of the joint are shown through several experiments, and the problems in actual use are discussed.

In this paper, an object detection and localization method for bin picking of plastic wrapped objects is described. Since such objects are deformable and have non-Lambertian surfaces, it is difficult to apply conventional feature point approaches or edge based template matching. To solve this problem, we propose a new method which is called “KCS (kernel convolution score)”. It measures the total score of convolution between local image and multiple kernels which are generated according to the characteristics of the target object. The local image having maximum score is considered as the most promising object for picking. In the last part of this paper, the limitation of existing edge and template matching in the given problem is discussed with the experimental results. Performance evaluation shows that the proposed method could localize the object which is appropriate for picking up with 91.67% success rate.

Tactile sensing has been used in robotics for object identification, grasping, and material recognition. Most material recognition approaches use vibration signals from a tactile exploration, typically above one second long, to identify the material. This work proposes a tactile multi-modal (vibration and thermal) material identification approach based on recursive Bayesian estimation. Through the frequency response of the vibration induced by the material and thermal features, like an estimate of the thermal power loss of the finger, we show that it is possible to identify materials in less than half a second. Moreover, a comparison between vibration only and multi-modal identification shows that both recognition time and classification errors are reduced by adding thermal information.

Current methods of grounding natural language instructions do not include reactive or temporal components, making these methods unsuitable for instructions describing tasks as sets of conditional instructions. We introduce the Verifiable Distributed Correspondence Graph (V-DCG) model, which enables the validation of natural language instructions by using Linear Temporal Logic (LTL) specifications together with physical world groundings. We demonstrate the V-DCG model on a physical robot and provide examples of the output our system produces for natural language instructions.

Obesity is defined as an unhealthy excess of body fat, which increases the risks of medical illness and premature mortality. There are multiple health risks linked to obesity such as heart disease and stroke, high blood pressure, diabetes, cancers, gallbladder disease and gallstones, osteoarthritis, gout, and breathing problems like sleep apnea, and asthma. Intragastric balloons (IGBs) have become an efficient and less invasive method for obesity treatment. However, the use of traditional IGBs requires complex insertion tools and flexible endoscopes to place and remove the balloon inside patient's stomach. This causes abdominal discomfort, nausea, vomiting, and gastric mucous damage. To overcome these limitations, we designed a novel magnetic soft capsule robot for obesity treatment with magnetically actuated inflatable IGB. The balloon is made from a thin, flexible, biocompatible material, and is inflated to a desired volume using biocompatible effervescent chemicals. Instead of using complex deflation mechanism, a biodegradable material is developed to automatically deflate the balloon after a predetermined period of treatment. In addition, multiple capsules can be simultaneously swallowed. As the source of actuation is provided via external magnetic fields, the magnetic soft capsule size can be significantly reduced with no limitations on the power consumption. A prototype of magnetic soft capsule is developed. Experiments are carried out to demonstrate the effectiveness of the proposed approach.

This paper presents the architecture and implementation of a tele-presence wheelchair system based on tele-presence robot, intelligent wheelchair, and touch screen technologies. The tele-presence wheelchair system consists of a commercial electric wheelchair, an add-on tele-presence interaction module, and a touchable live video image based user interface (called TIUI). The tele-presence interaction module is used to provide video-chatting for an elderly or disabled person with the family members or caregivers, and also captures the live video of an environment for tele-operation and semi-autonomous navigation. The user interface developed in our lab allows an operator to access the system anywhere and directly touch the live video image of the wheelchair to push it as if he/she did it in the presence. This paper also discusses the evaluation of the user experience.