IROS 2016 论文列表

Comprised of multiple telescoptic precurved tubes that can independently rotate and translate, concentric tube robots (CTRs) are favorable in minimally invasive surgeries thanks to their small size and considerable dexterity along with curvilinear accessibility. However, there is a lack of investigation on improvement of the surgeons' perception which in turn can be used to guide the telemanipulation. In this work, we proposed an eye-in-hand configuration for the concentric tube robot by adding an endoscope to the tip of the inner tube, which provides direct and intuitive visual sensing ability for the operator. Based on this visual feedback, we further developed two frameworks for the hybrid control of CTR, namely Teleoperation Before Visual Servoing (TBVS) and Teleoperation During Visual Servoing (TDVS). The structures of these two frameworks were elaborated with key algorithms derived. The effectiveness of the proposed methods were demonstrated through a series of experiments both in free space and in a confined environment (inside a skull model). The results manifested that the visual guidance had the potential of assisting the operator to control the CTR more efficiently.

In the last decade, multi-rotor Micro Aerial Vehicles (MAVs) have attracted great attention from robotics researchers. Offering affordable agility and maneuverability, multi-rotor aircrafts have become the most commonly used platforms for robotics applications. Amongst the most promising applications are inspection of power-lines, cell-towers, large and constrained infrastructures and precision agriculture. While GPS offers an easy solution for outdoor autonomy, using on-board sensors is the only solution for autonomy in constrained indoor environments. In this paper, we present our results on autonomous inspection of completely dark, featureless, symmetric dam penstocks using cameras and range sensors. We use a hex-rotor platform equipped with an IMU, four cameras and two lidars. One of the cameras tracks features on the walls using the on-board illumination to estimate the position along the tunnel axis unobservable to range sensors while all of the cameras are used for panoramic image construction. The two lidars estimate the remaining degrees of freedom (DOF). Outputs of the two estimators are fused using an Unscented Kalman Filter (UKF). A moderately trained operator defines waypoints using the Remote Control (RC). We demonstrate our results from Carters Dam, GA and Glen Canyon Dam, AZ which include panoramic images for cracks and rusty spot detection and 6-DOF estimation results with ground truth comparisons. To our knowledge ours is the only study that can autonomously inspect environments with no geometric cues and poor to no external illumination using MAVs.

Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a particle filter, coupled with a distance filter and a scan matching method, which achieves a more robust localization against dynamic obstacles. For the mapping problem we use an extended sensor model which results in an effective and precise map update effect. We compare our approach against other localization methods and evaluate the impact the map update effect has on the localization in dynamic environments.

The manipulation of objects is an inherent capability of dexterous human hands. However, state-of-the-art prostheses are limited to only forming grasps and gestures due to the low information transfer rate in the human-machine interface. Additionally, in the prosthetic setting the hand is not privy to such global knowledge including the object's shape, weight, friction properties, etc. Existing techniques from robotics that can manipulate objects in this prosthetic setting require compensation terms which are counterproductive to the manipulation task. The incorporation of tactile sensing leads to a simpler control formulation and enables relaxation of several restrictive assumptions inherent in the robotics applications. In this work a novel control system is proposed that incorporates tactile sensing for object manipulation and optimal distribution of contact forces for prosthetic hands. Simulation results demonstrate the ability of the proposed control system to manipulate an object, while minimizing the use of frictional forces during manipulation.

This paper considers the problem of numerically efficient planning for legged robot locomotion, aiming towards reactive multi-contact planning as a reliability feature. We propose to decompose the problem into two parts: an extremely low dimensional kinematic search, which only adjusts a geometric path through space; and a dynamic optimization, which we focus on in this paper. This dynamic optimization also includes the selection of foot steps and hand-holds—in the special case of instantaneous foot re-location. This case is interesting because 1) it is a limiting behavior for algorithms with a foot switching cost, 2) it may have merit as a heuristic to guide search, and 3) it could act as a building block towards algorithms which do consider foot transition cost. The algorithm bears similarity both to phase space locomotion planning techniques for bipedal walking and the minimum time trajectory scaling problem for robot arms. A fundamental aspect of the algorithm's efficiency is its use of linear programming with reuse of the active set of inequality constraints. Simulation results in a simplified setting are used to demonstrate the planning of agile locomotion behaviors.

Micromanipulation during live microscopic imaging relies heavily on good manual controls, dexterity, and hand-eye coordination. However, unassisted manual operations in these procedures greatly limit the speed, repeatability, and ease of operation. This is especially challenging in the case of microinjection where the insertion path needs to be in precise alignment with the imaging plane to avoid damage to cells. In this paper, we proposed an assistive robotic system that facilitates micromanipulation under microscopy. This comes in the form of intelligent robotic vision and guided manipulation. Using user-selected patch similarity, the system registers target templates and provides online coordinated depth compensation that ensures in-plane microinjection without the need for any prior calibration. This vision-based auto-registration approach readily integrates to any existing microscope system uncalibrated. It can also work as a standalone imaging solution with any general digital microscope camera. Experiments show that the similarity-score based depth compensation performed better than the uncompensated method. The method was shown to self-recover from an unfocused position. By robotizing conventional microscopy and micromanipulation procedures, we hope to address traditional latent needs and open up new possibilities in the ways experimental biology is performed.

Even though research on autonomous robots and human-robot interaction accomplished great progress in recent years, and reusable soft- and hardware components are available, many of the reported findings are only hardly reproducible by fellow scientists. Usually, reproducibility is impeded because required information, such as the specification of software versions and their configuration, required data sets, and experiment protocols are not mentioned or referenced in most publications. In order to address these issues, we recently introduced an integrated tool chain and its underlying development process to facilitate reproducibility in robotics. In this contribution we instantiate the complete tool chain in a unique user study in order to assess its applicability and usability. To this end, we chose three different robotic systems from independent institutions and modeled them in our tool chain, including three exemplary experiments. Subsequently, we asked twelve researchers to reproduce one of the formerly unknown systems and the associated experiment. We show that all twelve scientists were able to replicate a formerly unknown robotics experiment using our tool chain.

In this paper we address the 3D reconstruction of points, on a non-central catadioptric system, composed by a mirror, a projector, and a perspective camera. The goal of the paper is to propose a framework to build an omnidirectional depth camera, towards an omnidirectional RGB-D camera system. The main contributions are: an efficient technique to project 3D points from the world to an image of a general non-central catadioptric camera; the definition of the template pattern (for both the projector and camera's images); and the matching between the projection of these features to the world and its respective images. The 3D depth is directly recovered using the template matching approach. In conclusion, we apply some filtering techniques to improve the results. To evaluate the proposed framework, we test the method using synthetic data, under different levels and types of noises, proving that the framework is robust to noise and, thus, can be put into practice.

We propose a formalism for the hierarchical representation of affordances. Starting with a perceived model of the environment consisting of geometric primitives like planes or cylinders, we define a hierarchical system for affordance extraction whose foundation are elementary power grasp affordances. Higher-level affordances, e.g. bimanual affordances, result from combining lower-level affordances with additional properties concerning the underlying geometric primitives of the scene. We model affordances as continuous certainty functions taking into account properties of the environmental elements and the perceiving robot's embodiment. The developed formalism is regarded as the basis for the description of whole-body affordances, i.e. affordances associated with whole-body actions. The proposed formalism was implemented and experimentally evaluated in multiple scenarios based on RGB-D camera data. The feasibility of the approach is demonstrated on a real robotic platform.

The use of robotics for laparoscopic surgery has been an established field for over a decade. However, with the influx of advanced tools and algorithms for general purpose robotics, there is a need to incorporate these advancements into medical robotics technology. The daVinci Research Kit and its software framework provides a step towards these advancements. This paper presents the development of new tools and utilization of previously developed tools used for general purpose robotics, and their tailored use in medical robotics. Additionally, a method for computing haptic forces for tele-operated surgical robots is presented. The technique utilizes elastic, Spherical Proxy Regions (SPR) to readily compute directional interaction forces and manipulate them to create a dynamic behavior at the surgeon/user's manipulator.

The large number of potential applications for robotic odor source localization has motivated the development of a variety of plume tracking algorithms, the majority of which work in restricted two-dimensional scenarios. In this paper, we introduce a distributed algorithm for 3-D plume tracking using a system of ground and aerial robots in formation. We propose an algorithm that takes advantage of spatially distributed measurements to track the plume in 3-D and lead the robots to the source by integrating three behaviors - upwind movement, plume centering, and Laplacian feedback formation control. We evaluate this strategy in simulation and with real robots in a wind tunnel. For a source close to the ground, results show that a team of robots running our algorithm reaches the source with low lateral error while also tracing the horizontal and vertical plume shape.

In this paper a quasi-static framework for optimally controlling the contact force distribution is experimentally verified with the full-size compliant humanoid robot Walk-Man. The proposed approach is general enough to cope with multi-contact scenarios, i.e. robot-environment interactions occurring on feet and hands, up to the more general case of whole-body loco-manipulation, in which the robot is in contact with the environment also with the internal limbs, with a consequent loss of contact force controllability. Experimental tests were conducted with the Walk-Man robot (i) standing on flat terrain, (ii) standing on uneven terrain and (iii) interacting with the environment with both feet and a hand touching a vertical wall. Moreover, the influence of unmodeled weight on the robot, and the combination with a higher priority Cartesian tasks are shown. Results are presented also in the attached video.

The increasingly pervasive usage of robotic automation not only calls for a high performance of physical robot manipulators, but also implies challenging requirements on 3D robot simulators. A skillful reaction of simulated robots to unpredicted contact forces has become an especially stringent necessity to support the development of emerging applications for which a compliant robotic manipulation is indispensable. This paper focuses on the simulation of physics-based virtual testbeds in which simulated robots are made capable of accommodating external forces exerted by the environment on their manipulators. For this, we integrate methods from multi-body dynamics modeling, advanced robotics, and virtual reality to develop a robotics simulation basis that facilitates the employment of compliant robotic manipulations in simulated applications. The validation of this basis in joint space indicates that a physics-based simulation of robots endowed with active physical interaction control can capture and reflect significant effects which characterize the interactive behavior of physical compliant robots.We demonstrate the practical utility of our approach to unlock competitive advantages in fully simulated applications prone to position uncertainty. Furthermore, we show the potential of using compliant digital twins of physical robots to introduce compliant robotic manipulations into real applications.

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments.

Unmanned aerial vehicles (UAVs) are now very popular for many applications such as surveillance and transport and they are typically constructed starting from the design process. However, it is very time consuming and require all new airworthiness process. In this paper, we aim to provide a novel framework to automate an existing aircraft for unmanned operations using a humanoid robot, which is capable of manipulating the yoke, levers, switches and pedals to fly the airplane while operating various components such as lights and landing gears just as a human pilot would do. In this manner, the airplane can be converted for automated mode in a very short time without losing airworthiness. For testing, due to the complication of operating the robot in a real airplane, it is validated using a flight simulator, which provides the flight data over the network. The simulator is installed on a motion platform, which makes the robot move around due to the airplane's motion for realism. The proposed method is successfully validated in a series of flight simulations with various scenarios to show the feasibility of humanoid robot operation of an aircraft under various conditions.

Touch perception is an important sense to model in humanoid robots to interact physically and socially with humans. We present a neural controller that can adapt the compliance of the robot arm in four directions using as input the tactile information from an artificial skin and as output the estimated torque for admittance control-loop reference. This adaption is done in a self-organized fashion with a neural system that learns first the topology of the tactile map when we touch it and associates a torque vector to move the arm in the corresponding direction. The artificial skin is based on a large area piezoresistive tactile device (ungridded) that changes its electrical properties in the presence of the contact. Our results show the self-calibration of a robotic arm (2 degrees of freedom) controlled in the four directions and derived combination vectors, by the soft touch on all the tactile surface, even when the torque is not detectable (force applied near the joint). The neural system associates each tactile receptive field with one direction and the correct force. We show that the tactile-motor learning gives better interactive experiments than the admittance control of the robotic arm only. Our method can be used in the future for humanoid adaptive interaction with a human partner.

In recent years, much research has been done to develop various direct teaching schemes, in which an operator directly teaches a robot by hand guiding instead of using a teach pendant. However, conventional direct teaching methods are usually sensor based, thus leading to an expensive solution. To deal with this problem, this study proposes a sensorless hand guiding method based on torque control. The dynamic model of a robot along with the motor current and friction model is used to determine the user's intention to move the end-effector of a robot instead of directly sensing the external force by the user. A friction torque observer is employed for the friction model identification. The proposed method was experimentally verified using a 6 DOF robot. The experimental results show that the proposed sensorless hand guiding scheme is quite useful for practical direct teaching although the sensitivity is slightly lower than the sensor-based approach.

In this paper, we address the problem of time-optimal coordination of mobile robots under kinodynamic constraints along specified paths. We propose a novel approach based on time discretization that leads to a mixed-integer linear programming (MILP) formulation. This problem can be solved using general-purpose MILP solvers in a reasonable time, resulting in a resolution-optimal solution. Moreover, unlike previous work found in the literature, our formulation allows an exact linear modeling (up to the discretization resolution) of second-order dynamic constraints. Extensive simulations are performed to demonstrate the effectiveness of our approach.

Visual tracking and estimation of the 3D posture of a micro/nano-object is a key issue in the development of automated manipulation tasks using the visual feedback. The 3D posture of the micro-object is estimated based on a template matching algorithm. Nevertheless, a key challenge for visual tracking in a scanning electron microscope (SEM) is the difficulty to observe the motion along the depth direction. In this paper, we propose a template-based hybrid visual tracking scheme that uses luminance information to estimate the object displacement on x−y plane and uses defocus information to estimate object depth. This approach is experimentally validated on 4-DoF motion of a sample in a SEM.