ICRA 2017 论文列表

A rocket-propelled miniature robot is capable to explore sites that planetary rovers cannot reach with efficiency in locomotion distance per mass. The technical difficulty is the significant variance in its flight distance because of two factors: sensitivity of error in the center of gravity with respect to the thrust axis, and solid rocket engines deviance in thrusting force. To overcome the issue, our flight distance control strategy includes flight trajectory forming, and the flight trajectory prediction including the opposing shot. With our method, we experimentally showed the flight distance to the forward direction improved by a factor of four in variance reduction.

Fast and high-fidelity dynamic model is very useful for planning, control, and estimation. Here, we present a fixed-time-step, discrete-time dynamic model of differential-drive vehicle with friction for reliable velocity prediction, which is fast, stable, and easy to calibrate. Unlike existing methods which are predominantly formulated in the continuous-time domain (very often ignoring dry friction) that require numerical solver for digital implementation, our model is formulated directly in a fixed-time-step discrete-time setting, which greatly simplifies the implementation and minimizes computational cost. We also explicitly take into account friction, using the stable formulation developed by Kikuuwe [1]. Friction model, while non-trivial to implement, is necessary for predicting wheel locks and velocity steady-states which occur in real physical systems. In this paper, we present our dynamic model and evaluate it on a physical platform, a commercially-available electric powered wheelchair. We show that our model, which can run over 105 times faster than real-time on a typical laptop, can accurately predict linear and angular velocities without drift. The calibration of our model requires only a time-series of wheel speed measurements (via encoders) and command inputs, making it readily deployable to physical mobile robots.

Recent advancements in the performance and affordability of cameras and inertial measurement units (IMUs) have caused demand for efficient, accurate visual-inertial navigation solutions. In this paper, we present a system for the fusion of preintegrated inertial measurements with highly informative direct alignment of images. In particular, our preintegration theory is based on closed-form solutions of the continuous-time IMU kinematic model, instead of discrete time. This allows for more accurate computation of preintegrated measurements and their uncertainty as well as bias Jacobians. These measurements are fused via graph-based methods with relative pose constraints obtained from direct image alignment from a stereo platform. The proposed system is validated on publicly-available real-world datasets.

Most visual odometry algorithm for a monocular camera focuses on points, either by feature matching, or direct alignment of pixel intensity, while ignoring a common but important geometry entity: edges. In this paper, we propose an odometry algorithm that combines points and edges to benefit from the advantages of both direct and feature based methods. It works better in texture-less environments and is also more robust to lighting changes and fast motion by increasing the convergence basin. We maintain a depth map for the keyframe then in the tracking part, the camera pose is recovered by minimizing both the photometric error and geometric error to the matched edge in a probabilistic framework. In the mapping part, edge is used to speed up and increase stereo matching accuracy. On various public datasets, our algorithm achieves better or comparable performance than state-of-the-art monocular odometry methods. In some challenging texture-less environments, our algorithm reduces the state estimation error over 50%.

Differential dynamic programming (DDP) is a widely used trajectory optimization technique that addresses nonlinear optimal control problems, and can readily handle nonlinear cost functions. However, it does not handle either state or control constraints. This paper presents a novel formulation of DDP that is able to accommodate arbitrary nonlinear inequality constraints on both state and control. The main insight in standard DDP is that a quadratic approximation of the value function can be derived using a recursive backward pass, however the recursive formulae are only valid for unconstrained problems. The main technical contribution of the presented method is a derivation of the recursive quadratic approximation formula in the presence of nonlinear constraints, after a set of active constraints has been identified at each point in time. This formula is used in a new Constrained-DDP (CDDP) algorithm that iteratively determines these active set and is guaranteed to converge toward a local minimum. CDDP is demonstrated on several underactuated optimal control problems up to 12D with obstacle avoidance and control constraints and is shown to outperform other methods for accommodating constraints.

In this paper, we aim to address two challenges in the implementation of mobile gaze tracking systems, i.e., the parallax error and the inflexible calibration procedure. Our proposed method mainly involves two steps and all the calibration process can be completed without needs to receive user's commands. At first, instead of fixating at calibration points successively, users are required to fixate at one calibration point while smoothly and persistently varying the head position. In this case, the eye movements will compensate for head movements due to the activation of the smooth pursuit system. Then the PCA analysis is applied for distinguishing the smooth pursuits from other kinds of eye movements to get reliable training data. Meanwhile, a online sparse Gaussian Process using the FITC approximation is proposed to model the relationship between image pupil centers and gaze points. The next step aims to compensate the parallax error by recovering the epipolar geometry of gaze tracking systems and eyeballs. Users are asked to fixate at the points with different distances and detected parallax errors can be applied to estimate the epipolar geometry of mobile gaze trackers by solving a nonlinear optimization problem. Thus the real image gaze point with different depths can be straightforwardly estimated as the intersection point of two epipolar lines derived from binocular data. The simulation and experimental results demonstrate the effectiveness of our proposed method.

In spite of the remarkable benefits that minimally invasive surgery provides for patients, the absence of force feedback is still a significant disadvantage. Several studies have been performed to address this issue; however, an accurate sterilizable force sensing technology for measuring axial and grasping forces is still missing. In this work, an innovative partial grasper has been designed and developed for a laparoscopic needle driver that can measure axial and grasping force information at the grasper tip. Fiber Bragg Grating sensors are used in this work because of their sterilizability and high sensitivity. Accuracies of 0.19 N and 0.26 N were achieved for the grasping and axial sensors respectively.

During outbreaks of contagious diseases, healthcare workers are at high risk for infection due to routine interaction with patients, handling of contaminated materials, and challenges associated with safely removing protective gear. This poses an opportunity for the use of remote-controlled robots that could perform common nursing duties inside hazardous clinical areas, thereby minimizing the exposure of healthcare workers to contagions and other biohazards. This paper describes the development of the prototype system Tele-Robotic Intelligent Nursing Assistant (TRINA), which consists of a mobile manipulator robot, a human operator's console, and operator assistance algorithms which automate or partially-automate tedious and error-prone tasks. Using off-the-shelf robotic and sensing components, total hardware costs are kept under $75,000. The system's capabilities for performing standard nursing tasks are evaluated in the simulation laboratory of a nursing school.

This paper presents a system that consists of three robots to imitate the motion of top volleyball blockers. In a volleyball match, in order to score by spiking, it is essential to improve the spike decision rate of each spiker. To increase the spike decision rates, iterative spiking training with actual blockers is required. Therefore, in this study, a block machine system was developed that can be continuously used in an actual practice field to improve attack practice. In order to achieve the required operating speed and mechanical strength each robot has five degrees of freedom. This robot performs high speed movement on 9 m rails that are arranged in parallel with the volleyball net. In addition, an application with a graphical user interface to enable a coach to manipulate these robots was developed. It enables the coach to control block motions and change the parameters such as the robots' positions and operation timing. Through practical use in the practice field, the effectiveness of this system was confirmed.

The primary contribution of this work is the development of a generalized modeling methodology for complex tendon systems toward a long-term goal of modeling the mechanical structure of the human finger. The key feature of this model is its ability to predict how muscle forces will transmit through an interconnected tendon network based on tendon kinematics and the current joint posture, so that the transformation from input muscle forces to output joint torques and fingertip forces is accurately represented. The feasibility of this model is evaluated by using a tendon-driven robotic finger testbed. Moreover, we utilize the validated model to explore unique features of the extensor mechanism.

We propose a novel pipeline for detecting, localizing, and recognizing trees with a quadcoptor equipped with monocular camera. The quadcoptor flies in an area of semidense plantation filled with many trees of more than 5 meter in height. Trees are detected on a per frame basis using state of the art Convolutional Neural Networks inspired by recent rapid advancements showcased in Deep Learning literature. Once detected, the trees are tagged with a GPS coordinate through our global localizing and positioning framework. Further the localized trees are segmented, characterized by feature descriptors, and stored in a database by their GPS coordinates. In a subsequent run in the same area, the trees that get detected are queried to the database and get associated with the trees in the database. The association problem is posed as a dynamic programming problem and the optimal association is inferred. The algorithm has been verified in various zones in our campus infested with trees with varying density on the Bebop 2 drone equipped with omnidirectional vision. High percentage of successful recognition and association of the trees between two or more runs is the cornerstone of this effort. The proposed method is also able to identify if trees are missing from their expected GPS tagged locations thereby making it possible to immediately alert concerned authorities about possible unlawful felling of trees. We also propose a novel way of obtaining dense disparity map for quadcopter with monocular camera.

Robotic image-guided micromanipulation contributes towards the ease of operation, speed, accuracy, and repeatability in cell manipulation. However, such technology is not fully exploited because of the challenges in the integration of robotic modules with existing microscope systems, and the difficulty in incorporating robot assistance seamlessly into the workflow. In this paper, we propose a vision-based workflow algorithm termed Detect-Focus-Track-Servo (DFTS). It facilitates easy integration of robotic modules. It also supports user interactions while minimizing the need for manual intervention and disruption to workflow through automatic detection, focusing, tracking and servoing. Experimental results suggest satisfactory detection accuracy of 99.0 % at 70 μm tolerance. The robustness test suggests no difference in the accuracy under blurred and cluttered images. The self-focus algorithm is also demonstrated to bring the tip into focus consistently. The track-servo algorithm achieves low sub-pixel uncertainty. By proposing the DFTS workflow algorithm, we hope that the level of autonomy and ease of deployment in robot and vision modules for micromanipulation can be improved so as to open up new possibilities in the development of robotic image-guided cell manipulation.

At the cross section of the fields of Uneven Terrain Navigation and Multi Agent Systems (MAS), in this work, a Detachable Compliant Modular Robot (DCMR) which can perform concurrent scene exploration by detaching into numerous parts, while preserving its ability to climb stairs is proposed and built. A spring is designed and used in the modular robot taking the worst-case-scenario of stairs encountered in an urban setting. In addition to the actuators at the wheels, an additional set of actuators per module are introduced to enable the detachment and re-attachment. The design additions and their trade-offs are discussed. Potential applications are presented with special focus on improving coverage of a map with obstacles/slabs large enough to merit exploration by climbing them. The problem of turning in crammed spaces is solved using the ability to detach of DCMR. The detaching & re-attaching capability, and stair climbing of the composite modular robot are demonstrated through experimentation using the prototype.

Holonomic systems are often complex and expensive with poor terrain handling capabilities. This paper introduces HAMR, a platform that is simple and low-cost with good terrain handling capabilities which was designed using a new method for characterizing mobility. A mobility ellipsoid (analogous to a manipulability ellipsoid) is introduced as a design tool. A metric called holonomicity measures the extent to which a vehicle can move equally in all directions. The holonomicity of HAMR is measured experimentally. It is shown that non-holonomic vehicles can have relatively high holonomicity as well.

Presented are techniques for designing, modeling, and control of reliable pneumatic artificial muscle actuators with integrated low profile sensors for position feedback. The sensor is fabricated through a three-dimensional manufacturing process based on a modified lathe approach for controlling viscous and viscoelastic materials as well as on direct writing of liquid metal. Next, a new precision pneumatic muscle design and its integration with the sensor is illustrated. A theoretical model and experimental characterization of the muscle-sensor package are presented with high correlation and repeatability. Finally, a position feedback sliding mode controller is implemented with a position error of <0.9% of maximum muscle contraction.

Traditional colonoscopy requires highly trained personnel to be performed. Additionally, current devices may cause discomfort and carry the risk of perforating the bowel wall. In this paper, a soft three modular section robot is designed, modeled, controlled and tested. Each of the robotic sections has three degrees of freedom, one translation and two rotations. The robot uses a peristaltic motion to translate, inspired by the motion generated by the bowel. The robot uses nine independently controlled Shape Memory Alloy (SMA) springs as its actuators and a novel silicone rubber skin provides the passive recovery force to expand the springs to their original state. It also incorporates three air tubes, one for each section, to provide forced convection reducing the cooling time of the SMA springs. A parametric study on the skin curvature and thickness using Finite Element Analysis (FEA) is performed to maximize traction while providing enough recovery force. A multi-input multi-output (MIMO) controller based on fuzzy control is designed and implemented for each of the sections allowing the robot to achieve any orientation between −90° and +90° in both pitch and roll in less than 4 seconds with near zero steady state error. Both the peristaltic motion and the orientability of the robot are tested. The robot is able to perform a peristaltic motion with maximum speed of 4 mm/s (24 cm/min) and an average speed of 2.2 cm/min. Each section is also able to follow, with less than 2% overshoot and near zero steady-state error, periodic multi-input squared signals of 25° of amplitude.

Despite the recent advances in, and the adoption of robotic technologies in the construction industry, the architectural processes which demand a high degree of geometric freedom still remain largely labour intensive and manual. This is due to the inherent difficulties in robotizing the current implementation of such processes coupled with the lack of alternate robotic technologies. A specific example, which is also the focus of this paper, is that of building a steel reinforced concrete structure, with varying curvature or cross-section. This process still remains rather manual and requires extensive support of customized form-work. In this paper, first we describe an alternate novel robotic fabrication process for building steel wire meshes which act as both reinforcement and formwork. The robotization of such a process is discussed with the use of a previously developed mobile robotic system. Based on the specifications derived from the process, design of a novel custom designed robotic end-effector, enabling this process, is detailed. Automation of the full robotic system comprising the mobile robotic system and the robotic end-effector is discussed from simulation to control. Through experimental evaluation of the robotic system, we demonstrate the ability to fully automate the construction of non-standard steel reinforced steel meshes of varying curvature and cell sizes.

Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization by excelling both stability and maneuverability at insect/hummingbird scale. Flapping Wing Micro Air Vehicle (FWMAV) holds great promise in bridging the performance gap between engineering system and their natural counterparts. Designing and constructing such a system is a challenging problem under stringent size, weight and power (SWaP) constraints. In this work, we presented a systematic approach for design optimization and integration for a hummingbird inspired FWMAV. Our formulation covers aspects of actuation, dynamics, flight stability and control, which was validated by experimental data for both rigid and flexible wings, ranging from low to high wing loading. The optimization yields prototypes with onboard sensors, electronics, and computation units. The prototype flaps at 30Hz to 40Hz, with 7.5 to 12 grams of system weight and 12 to 20 grams of maximum lift. Liftoff was demonstrated with added payloads. Flapping wing platforms with different requirements and scales can now be designed and optimized with minor modifications of proposed formulation.

This paper presents the design of the Interacting-BoomCopter (I-BoomCopter) unmanned aerial vehicle (UAV), designed specifically for environmental interactions. The novel design consists of a horizontally mounted four-blade reversible propeller on a front boom that is attached to a standard tri-rotor UAV configuration. A custom end-effector and force sensor is placed at the end of the front boom for autonomous pushing and pulling interaction tasks. The modeling of the new platform is presented and two prototype versions built, flight tested, and characterized. Finally, the efficacy of the new platform for environmental interactions is evaluated with an autonomous door opening task and a teleoperated door opening and door closing task.