IROS 2015 论文列表

Depending on a humanoid finger sensor with multimodal perception capability, physical properties such as pressure, temperature, texture features, surface roughness, micro vibration, etc., could be easily extracted when the sensor contacts with different objects. In this paper, an innovative calibration device is created, as well as the motor controllers, centre control system, hardware and software system. The Least Square method is proposed for data fitting and parameter predicting, which is used to implement precise calibration for pressure and its distribution. The Frequency Spectrum Analysis method is presented by using Fast Fourier Transform to determine different physical properties of objects under certain frequency range and energy distribution. With the analysis of calibration data and results, the contact force and temperature range are obtained in good linearity, meanwhile the method for how to distinguish objects with similar texture features is put forward by using the Frequency Spectrum Analysis. This study provides a set of systematic calibration methods for the humanoid robot fingers, which can be served as an efficient reference to perceive multimodal objects.

If the control authority of a running system is insufficient to reach a target state in a single step, i.e. if deadbeat control is not possible, then a stabilizing controller is faced with the decision on how to plan intermediate steps. In this work, we compare the performance of a simple greedy control policy (that computes deadbeat inputs and simply caps them) with the optimal performance found by an exhaustive search through decision space. The performance criterion used in this study is the basin of attraction: the set of all states from where the target state will be reached in a finite number of steps. Using the planar spring-loaded inverted pendulum (SLIP) as a model for a running robot, we compare the two control schemes and a fully passive behavior. To this end, we extended the passive slip model to include a controllable, yet limited variation of the touchdown angle and of the damping in the leg spring. We quantified the number of steps that it would take for the model to fall or converge from arbitrary initial states. The paper highlights how the passive stabilization, that is inherent to the SLIP model, greatly influences the dynamics of the controlled system. Furthermore, it reveals some new insights into the structure of basins of attractions of SLIP-like running models.

The Tri-Wheel is a novel wheel-leg locomotion concept inspired by work with first responders. Through its two modes of operation—Driving Mode and Tumbling Mode— this mechanism is able to both drive quickly on smooth surfaces at roughly 1.7 times desired speed and climb objects as tall as 67% of the diameter of the mechanism. The unique gearing configuration that facilitates these dual capabilities is described, and testing quantifies that this nonprecision gearing system is roughly 81% efficient in a worst-case scenario of loading. This work introduces the Tri-Wheel concept and provides preliminary testing to validate its predicted operating characteristics.

When designing a lower-limb assisting robot with body-weight support (BWS), it is important to achieve high force fidelity to support body weight at standing phase. Low impedance operation at swing phase is also required not to disturb leg motions for users. The SoftGait is designed to achieve both performances with a simple mechanical structure based on a pneumatic actuator and low cost sensor development. The SoftGait generates large force for body-weight-supporting in squatting mode corresponding to user's body weight. In fact, in walking mode, it enables a user to experience more comfortable walking with support from intrinsic compliance.

HTN planners have generally relied on specialised languages for domain and problem representations. To facilitate adoption by other communities such as robotics, however, and integration with real world applications written in standard programming languages, we need HTN planners that are based on more familiar concepts from structured programming, and that come ready with features supporting integration. In this paper, we demonstrate how the HATP (Hierarchical Agent-based Task Planner) HTN planner offers such “syntactic sugar” and some of these features. Moreover, since it has a conceptually distinct syntax compared to traditional HTN planners, we also develop a formalism to unambiguously capture HATP's syntax and an important subset of its semantics, which we then use to compare against the formalism of a well understood family of HTN planners and to show that the former is sound. Finally, we demonstrate that despite quite possibly using “heavier” data structures to naturally capture HATP's syntax/semantics, and thereby facilitate extensions to HATP and integration with other applications, the implementation still performs acceptably.

This paper introduces and characterizes the Baxter Easyhand, a new 3D printed hand derived from the Yale T42 hand [5], [10], but designed specifically to be mounted on the Baxter robot from Rethink robotics. Because this hand is designed specifically for Baxter, we are able to make some important simplifications in the design relative to other 3D printed hands. In particular, the Easyhand is smaller than most other 3D printed hands and it is powered by the native Baxter gripper actuator. As a result, our hand is cheaper, lighter, and easier to interface with than other robot hands available for Baxter. This paper details the design of the hand and its mechanical characteristics and reports results from experiments that characterize its grasping performance.

We consider the problem of learning from complex sequential demonstrations. We propose to analyze demonstrations in terms of the concurrent interaction phases which arise between pairs of involved bodies (hand-object and object-object). These interaction phases are the key to decompose a full demonstration into its atomic manipulation actions and to extract their respective consequences. In particular, one may assume that the goal of each interaction phase is to achieve specific geometric constraints between objects. This generalizes previous Learning from Demonstration approaches by considering not just the motion of the end-effector but also the relational properties of the objects' motion. We present a linear-chain Conditional Random Field model to detect the pair-wise interaction phases and extract the geometric constraints that are established in the environment, which represent a high-level task oriented description of the demonstrated manipulation. We test our system on single- and multi-agent demonstrations of assembly tasks, respectively of a wooden toolbox and a plastic chair.

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

A fundamental problem with dual-arm robotic control is to find the coordinated motion resolution under high kinematic redundancy and intrinsic constraints of each robot. To solve this problem, this paper presents a multi-tasking, co-operative control framework, in which potential task conflicts and robot joint constraints are properly handled. Based on the relative Jacobian formulation, singularity-robust inverse kinematics and the scheme of null space distributing exceeded joint velocity, this work contributes by introducing a framework to handle multi-tasking conflicts both in task and joint space for dual-arm robots. Detailed validation of the proposed framework is first conducted by using a simulated dual-arm robot, followed by a demonstration on two 7-dof Kuka lightweight manipulators in a bimanual stent graft manufacturing task.

When a mobile manipulator functions as an assistive device, the robot's initial configuration and the configuration of the environment can impact the robot's ability to provide effective assistance. Selecting initial configurations for assistive tasks can be challenging due to the high number of degrees of freedom of the robot, the environment, and the person, as well as the complexity of the task. In addition, rapid selection of initial conditions can be important, so that the system will be responsive to the user and will not require the user to wait a long time while the robot makes a decision. To address these challenges, we present Task-centric initial Configuration Selection (TCS), which unlike previous work uses a measure of task-centric manipulability to accommodate state estimation error, considers various environmental degrees of freedom, and can find a set of configurations from which a robot can perform a task. TCS performs substantial offline computation, so that it can rapidly provide solutions at run time. At run time, the system performs an optimization over candidate initial configurations using a utility function that can include factors such as movement costs for the robot's mobile base. To evaluate TCS, we created models of 11 activities of daily living (ADLs) and evaluated TCS's performance with these 11 assistive tasks in a computer simulation of a PR2, a robotic bed, and a model of a human body. TCS performed as well or better than a baseline algorithm in all of our tests against state estimation error.

To facilitate manipulation tasks, grasp should be selected intelligently to fulfill different stability properties and manipulative requirements in the tasks. In this paper, two task-dependent grasp quality measures are introduced: task wrench coverage measure and the manipulator efficiency measure. The first one measures the ability of a grasp to provide required interactive wrench during a task, while the second measures the effort that the manipulator takes for the whole manipulation process in facilitating the required instrument motion, which is determined by the grasp when the motion of the instrument is defined. The proposed measures are then used in selecting grasps for three typical manipulation tasks in simulations and using a real robotic system and produced successful grasp synthesis outcomes that satisfy manipulative requirements.

We address the operation of robotic legs with intrinsic elasticity in hopping cycles determined by the mechanical resonant properties of the system. This ensures energy efficiency and high jumping velocity and distance. Recently, we have shown in simulation that a simple, biologically inspired bang-bang controller operating in the local coordinate of the first resonant mode leads to limit cycles which are robust with respect to leg model uncertainties and ground properties. In this paper we address the velocity control of the hopping and the planning of the bang-bang control parameters for the case that the systems should not move at steady state velocity, but should have different step lengths and heights. We exploit the discrete structure and the small number of parameters of the controller to develop a fast optimization procedure for generating an arbitrary sequence of steps. This approach can provide high motion performance, robustness and substantial computational time saving compared to continuous trajectory and controller gain planning. The stationary and the aperiodic hopping is validated by experiments on a new planar elastic leg.

Image feature descriptors composed of a series of binary intensity comparisons yield substantial memory and runtime improvements over conventional descriptors, but are sensitive to viewpoint changes in ways that vary per feature. We propose a method to improve the matching performance of such descriptors by specifically reasoning about the reliability of test results on a feature-by-feature basis. We demonstrate an intuitive method to learn improved descriptor structures for individual features. Further, these learned results can be efficiently applied during matching with little increase in runtime. We provide an evaluation using a standard, ground-truthed, multi-image dataset.

This paper explores the design space of simple legged robots capable of leaping culminating in new behaviors for the Penn Jerboa, an underactuated, dynamically dexterous robot. Using a combination of formal reasoning and physical intuition, we analyze and test successively more capable leaping behaviors through successively more complicated body mechanics. The final version of this machine studied here bounds up a ledge 1.5 times its hip height and crosses a gap 2 times its body length, exceeding in this last regard the mark set by the far more mature RHex hexapod. Theoretical contributions include a non-existence proof of a useful class of leaps for a stripped-down initial version of the new machine, setting in motion the sequence of improvements leading to the final resulting performance. Conceptual contributions include a growing understanding of the Ground Reaction Complex as an effective abstraction for classifying and generating transitional contact behaviors in robotics.

This paper presents a tactile sensing system capable of measuring three force components, three torque components and the position of the centroid of the forces applied to a surface linked to an array of force-torque sensors. The paper deals with the design, the calibration, and the testing of a specific configuration of this system, where the surface is connected to an array of piezoresistive MEMs silicon 3-axis force sensors. In this configuration, only the contact position and the force components are estimated. The tests have proven that this sensor satisfies most of the characteristics needed to achieve robotic dexterous manipulation.

Adhesion quality sensing is critical to the performance of any robot that utilizes gecko-inspired dry adhesives for climbing, perching, or grasping. We present a 3-axis tactile sensor designed for this application that demonstrates performance on par with a large commercial load cell while being compact enough to integrate into a robot foot. The sensor can measure spatially distributed force loads and demonstrates high sensitivity in both shear and normal components. Results showcase the sensor's ability to detect a variety of unreliable contact and loading conditions before the onset of adhesion failure.

By performing synthesis from formal high-level mission specifications, we can obtain robot controllers that are guaranteed to operate correctly under the specified environment conditions. Such conditions must be stated in the specification whenever there is no way in which the robot's task can be fulfilled without them holding, and they relate the possible behaviors of the environment with the behavior of the robot. Contemporary synthesis algorithms however frequently construct implementations that try to trivially satisfy their specifications by actively working towards the violation of the assumptions, which is undesirable behavior.

The goal of this work is to study the application of switchable magnets (SM) for climbing robots. A switchable magnet is a device which uses moving permanent magnets to change the magnetic flux path and switch on or off the magnetic attraction force. In our work we used Comsol Multiphysics, a physics simulation software in order to simulate the flow of the magnetic flux on switchable magnets on its different states and study the effect of different design and material parameters on the attraction force of the unit. Bearing in mind the lessons learned from this study, we developed a novel device in a smaller scale with the best holding force/mass ratio, for using in climbing robot applications. As a case study, three of these optimized SM units are then equipped with an actuator that can rotate the moving magnets, turning the device on and off. This new device is employed in a novel adaptive adhesion unit for the OmniClimber robot, replacing its previous system which was relying on electromagnets magnets. We demonstrate this novel device application on a climbing robot and the advantages relative to electromagnet or permanent magnet based devices.

This study presents the survival microinjection into Caenorhabditis elegans (C. elegans) with in vivo observation based on micromanipulation. The microinjections were achieved with micro-gel beads which are enable to encapsulate chemicals for injection. In this study, a fluorescent material was used to evaluate the injection positional precision inside the C. elegans. The fluorescent microbead was picked up at the tip of a micropipette injection tool and injected by a piezo actuated microinjector. The distance between the injected micro-gel bead and closest nerve axon was measured as 20.3 μm and 16.5 μm by in vivo observation of a confocal microscopy. The two types of pipette tools were used to evaluate the success and survival rates of microinjection, and the smaller pipette (pipette A, 0.8 μm in diameter) showed higher rates as 50 % and 67 % respectively.