IROS 2015 论文列表

In this paper, a controller based on constrained optimization for tracking problems in mobile manipulation is presented. A Model Predictive Control problem is set and solved online, allowing to deal with dynamic scenarios and unforeseen events. Besides acceleration, velocity and position constraints, collision avoidance constraints for the mobile base and the arm and Field-of-View constraints have been enforced and extended over the prediction horizon. Navigation performance has been improved by including an additional goal, derived from the classical vortex field approach, to the MPC problem. An experimental validation on a KUKA youBot mobile manipulator has been carried out, showing the online applicability of the presented approach.

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

When teleoperating a multi-robot system it is useful to control the kind of behavior of the fleet depending on the environment it is moving in. In this paper, a novel bilateral control architecture for teleoperating a group of mobile robots is proposed. The user can command the robots both as a flexible and amorphous group and as a set of agents executing different trajectories for achieving a desired task, mimicking a conductor-orchestra paradigm. Exploiting passivity based control, we ensure a stable and safe behavior for the user. The proposed teleoperation strategy is validated by means of experiments.

In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.

Software design patterns are reusable solutions to commonly occurring problems in software development. Growing complexity of robotics software increases the importance of applying proper software engineering principles and methods such as design patterns to robotics. Concurrency design patterns are particularly interesting to robotics because robots often have many components that can operate in parallel. However, there has not yet been any established set of reusable concurrency design patterns for robotics. For this purpose, we choose six known concurrency patterns - Future, Periodic timer, Invoke later, Active object, Cooperative cancellation, and Guarded suspension. We demonstrate how these patterns could be used for solving common robotic coordination tasks. We also discuss advantages and disadvantages of the patterns and how existing robotics programming frameworks can support them.

We propose a method for a robot to form various concepts. The robot uses its embodiment to obtain visual, auditory, and haptic information by grasping, shaking, and observing objects. At the same time, a user teaches the robot object features through speech. From these kinds of information, the robot can form object concepts. The information obtained by the robot is converted into Bag-of-Words representations, which are classified into categories. The proposed method is based on a stochastic model, and objects can be classified by estimating their parameters. We introduce the Chinese restaurant process into the multimodal hierarchical Dirichlet process. This model is an infinite mixture of models and enables the robot to form various concepts such as object type, color, and so on. Therefore, the robot can form not only object concepts but also various concepts that are not represented by object concepts (e.g., color). Also, because the proposed method is based on a stochastic model, it makes it possible for the robot to estimate category and unobserved information of unseen objects. We implement the proposed method on a robot and show that it can form various concepts and perform various estimations for unseen objects.

This paper presents an impulse-based model for n-body collisions with or without friction. In the frictionless case, impulses at all the contacts, initialized via solution of a non-linear system, accumulate over different phases (compression and restitution) tracked through numerical integration. Experiment over Newton's cradle, using vision-based velocity estimation, achieves a close agreement with simulation. The presence of friction requires a contact mode analysis via solution of a linear system to check for contact mode consistencies with Coulomb's friction law. Simulation of nine-ball break shots, drawing upon a recent trajectory study [6], yields realistic scenarios. The modeling approach is part of our efforts toward planning for robotic manipulation strategies using impulsive forces.

Performance of path tracking controllers for mobile robots is usually constrained to the tuning of a set of gains within previously known boundaries. In industrial applications, controller tuning is a time consuming task, which is especially critical when the gain values have a considerable impact on the use of the robot's energy resources. In this paper, we propose a set of approaches to automatically improve the performance of well-known path tracking controllers used for agricultural applications of automated agricultural machinery. The effect of the proposed strategy can be better appreciated during the transient response of the robot when it has to maneuver along a non-kinematically compatible path. The results presented in this work show that the robot's path tracking performance is improved up to 15% using the proposed approaches.

In this work, we examine several wing designs for a motor-driven, flapping-wing micro air vehicle capable of liftoff. The full system consists of two wings independently driven by geared pager motors that include a spring in parallel with the output shaft. The linear transmission allows for resonant operation, while control is achieved by direct drive of the wing angle. Wings used in previous work were chosen to be fully rigid for simplicity of modeling and fabrication. However, biological wings are highly flexible and other micro air vehicles have successfully utilized flexible wing structures for specialized tasks. The goal of our study is to determine if wing flexibility can be generally used to increase wing performance. Two approaches to lift improvement using flexible wings are explored, resonance of the wing cantilever structure and dynamic wing twisting. We design and test several wings that are compared using different figures of merit. A twisted design improved lift per power by 73.6% and maximum lift production by 53.2% compared to the original rigid design. Wing twist is then modeled in order to propose optimal wing twist profiles that can maximize either wing efficiency or lift production.

Wall-climbing robot research is increasingly popular for cleaning, painting, and repairing high-rise buildings. In this study, a compliant wall-climbing robotic platform for various curvatures is presented. In order to adapt to flat and curved surfaces with a single configuration, the robotic platform uses a compliance mechanism with eight legs. A parallel four-bar linkage connects the inner frame to the outer frame, and walking locomotion is achieved by an another four-bar linkage with a single DC motor. The robot's footpads use a dry adhesive system, and the robot is small and lightweight. Experimental results reveal that the robotic platform can climb on various curved surfaces with a radius of curvature between 50 mm and infinity in the vertical direction. The platform could be used for cleaning high-rise buildings and solar panels with different curvature.

This paper proposes a modeling method for generic serial-chain compliant robotic manipulators. It is based on graph theory and port-Hamiltonian systems, which allows a modular approach to the interconnection of rigid bodies with compliant actuators by means of kinematic pairs. This modularity allows a very simple and straight-forward change in a manipulator's actuator morphology. An example of a two degree of freedom planar manipulator shows that this modeling method is more suitable for modeling changes in actuator placement than traditional Euler-Lagrange models.

This paper presents a reconfiguration algorithm for a robot composed of sliding cube modules with a limited motion primitive. We assume that a cube module is not allowed to make convex motions around other modules but is allowed to slide across other modules' surfaces. Such a motion primitive limitation complicates the development of the reconfiguration algorithm; however, it simplifies the design of module hardware and reduces the size and manufacturing costs of modules. The algorithm uses a three-dimensional 2 × 2 × 2 meta-module to guarantee the existence of mobile modules and maintain the connectivity of the robot structure during the reconfiguration process. The transition of the meta-module is executed by the tunneling of cube modules. The tunneling movement is efficiently controlled by focusing on the motion of voids that are invoked through the transition of meta-modules. We show that the algorithm is complete for a connected robot structure with more than a certain number of meta-modules and that the reconfiguration in an environment with obstacles is executed in quadratic operating time cost.

In this paper, a lightweight master interface for a surgical robot system is proposed. The position of the human hand is measured by a non-contact motion tracker, but the force feedback is realized by a handheld haptic device. A gripping force is displayed by a pneumatic bellows, which is smaller and more lightweight than electric motors. As for the slave manipulator, the gripper of a surgical forceps is actuated using a pneumatic cylinder. Force estimation is possible due to the back-drivability of the pneumatic actuator. Experimental results show the control performance of the haptic device and a constructed master-slave system.

This paper presents a study on intention communication in a navigational context using a robotic wheelchair. The robotic wheelchair uses light projection to communicate its motion intentions. The novelty of the work is threefold: the communication of robot intentions to the passenger, the consideration of passenger and robot as a group (“in-group”) [1] who share motion intentions and the communication of the in-group intentions to other pedestrians (the “out-group”). A comparison in an autonomous navigation task where the robotic wheelchair autonomously navigates the environment with and without intention communication was performed showing that passengers and walking people found intention communication intuitive and helpful for passing by actions. Evaluation results significantly show human participant preference for having navigational intention communication for the wheelchair passenger and the person passing by it. Quantitative results show the motion of the person passing by the wheelchair with intention communication was significantly smoother compared to without intention communication.

We consider the problem of enabling a robot to efficiently obtain a dense haptic map of its visible surroundings using the complementary properties of vision and tactile sensing. Our approach assumes that visible surfaces that look similar to one another are likely to have similar haptic properties. We present an iterative algorithm that enables a robot to infer dense haptic labels across visible surfaces when given a color-plus-depth (RGB-D) image along with a sequence of sparse haptic labels representative of what could be obtained via tactile sensing. Our method uses a color-based similarity measure and connected components on color and depth data. We evaluated our method using several publicly available RGBD image datasets with indoor cluttered scenes pertinent to robot manipulation. We analyzed the effects of algorithm parameters and environment variation, specifically the level of clutter and the type of setting, like a shelf, table top, or sink area. In these trials, the visible surface for each object consisted of an average of 8602 pixels, and we provided the algorithm with a sequence of haptically-labeled pixels up to a maximum of 40 times the number of objects in the image. On average, our algorithm correctly assigned haptic labels to 76.02% of all of the object pixels in the image given this full sequence of labels. We also performed experiments with the humanoid robot DARCI reaching in a cluttered foliage environment while using our algorithm to create a haptic map. Doing so enabled the robot to reach goal locations using a single plan after a single greedy reach, while our previous tactile-only mapping method required 5 or more plans to reach each goal.

We are combining symbolic and geometric planning to synthesize human-aware plans in order to deal with the complex and highly intricate planning problems induced by Human-Robot collaborative object manipulation. In this paper, we summarize our previous contributions - refining symbolic actions at geometric level, during the symbolic planning, in order to assess their feasibility and computing the geometric side effects-, then we present the current contributions meant to tighten the cooperation between the symbolic planning and the geometric planning: the symbolic planner helps the geometric one by providing it with constraints and domain-expert knowledge making the geometric planner more efficient, and the geometric planner helps the symbolic one to find the best plan based on social costs computed at geometric level. We also propose different examples, highlighting the interest of such cooperation between the planners in simulation and on our PR2 robot.

A new Robotic Assembly Skill (RAS) modeling framework is proposed. An assembly skill is a primitive that encapsulates the capabilities to coordinate, control and supervise an elementary robot task. To gain reusability of a primitive in alike robot tasks, the primitives are represented as generic templates that are parametrized for each situation with data from an assembly specification. A skill is represented in two ways, namely as a trajectory describing compliant motions in pose-wrench space and as a finite state machine. This approach comes with the potential to simplify robot programming and to improve robustness in robotic assembly due to inherent quality checking. The approach is implemented on an ABB YuMi robot performing the assembly of a programmable logic controller (PLC) I/O module.

This paper proposes two methods for collision detection between cylindrical components when mutual blocking occurs in the view of cameras. In reconstruction approach, 3D models are built according to key features captured by cameras and constraint optimization is employed to rapidly find possible intersections. To satisfy the computational efficiency required by real time operation, we present another way, projection method, to convert two planar views to contours on a projection plane and to detect high dimension collisions by studying the projection's relationships in low dimension. Nine cases are totally categorized and eleven parameters are constructed for detection on the basis of relative postures and positions. Simulations and experiments are carried out to demonstrate their validity.

This paper presents modular self-reconfigurable robots for grasping an unknown object based on a grasp quality metric. The presented self-reconfiguration approach makes use of basic motions performed by individual robots to change the structure. The method to solve for forward kinematics as well as configuration representation is proposed based on relationship between robotic modules. This method can be used to update the estimated contact points in self-reconfiguration. Incorporating a grasp quality metric of form closure into self-reconfiguration, the structure for grasping can be evaluated until a desired metric is obtained. Grasping and self-reconfiguration simulation experiments are presented, showing the availability and effectiveness of the proposed approach.