ICRA 2015 论文列表

In this paper, we propose an Energy based Fall Prediction (EFP) which observes the real-time balance status of a humanoid robot during standing. The EFP provides an analytic and quantitative measure of the level of balance. Both simulation and experimental studies were conducted and compared with the previously proposed indicators, such as Capture Point (CP) and Foot Rotation Indicator (FRI). The EFP also suggests the balance augmentation by active foot tilting to create larger potential barriers. As a proof of concept, a hybrid balance controller was designed to stabilize the robot including under-actuation phases so the robot can also balance with shoes. Our study reveals that both EFP and CP successfully predict falling about 0.2s in advance for the tested robot, while the FRI fails due to the light weight of the foot and limited resolution of the force/torque measurement.

Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors are used to keep under observation a (possibly larger) set of mobile targets. Robots cooperatively plan their motion in order to maximize the time during which each target lies within the sensing range of at least one robot.

Robotic systems have a high potential for creative learning if they are flexible, accessible and engaging for the user in the experimental process of building and programming robots. In this paper we describe the Fable modular robotic system for creative learning which we develop to enable and motivate anyone to build and program their own robots. The Fable system consists of self-contained modules equipped with sensors and actuators, which users can use to easily assemble a wide range of robots in a matter of seconds. The robots are user-programmable on several levels of abstraction ranging from a simple visual programming language to powerful conventional ones. This paper provides an overview of the design of Fable for different user groups and an evaluation of critical issues when we attempt to integrate the system into an everyday teaching context.

Shape tracking using a fiber Bragg grating sensor is a promising tool due to its thin, flexible, and weightless nature. Conventional investigations attached optical fibers with a metal rod which limited the curvature due to its stiffness and increased distance between the center of the fiber and that of the sensor. We fabricated ultrathin flexible shape sensor with 110 mm length and 560 µm diameter. Its low Young's modulus and small diameter makes it suitable for application in minimally invasive surgical robot such as active cannula. The sensor causes only miniscule resistance when the cannula rotates or moves, and allows extra spaces for other surgical tools thanks to its small diameter. After the calibration, the sensor was integrated to the active cannula and overall position of the sensor was tracked. It showed an average error of 3.17 ± 1.21 % of the lengths of the measured nodes from the fixed starting point. The error was comparable to the outer diameter of the cannula, 2.8mm.

This paper is concerned with large-scale localisation at city scales with monocular cameras. Our primary motivation lies with the development of autonomous road vehicles - an application domain in which low-cost sensing is particularly important. Here we present a method for localising against a textured 3-dimensional prior mesh using a monocular camera. We first present a system for generating and texturing the prior using a LIDAR scanner and camera. We then describe how we can localise against that prior with a single camera, using an information-theoretic measure of image similarity. This process requires dealing with the distortions induced by a wide-angle camera. We present and justify an interesting approach to this issue in which we distort the prior map into the image rather than vice-versa. Finally we explain how the general purpose computation functionality of a modern GPU is particularly apt for our task, allowing us to run the system in real time. We present results showing centimetre-level localisation accuracy through a city over six kilometres.

The integration of several 2D laser rangefinders in a vehicle is a common resource employed for 3D mapping, obstacle detection and navigation. The extrinsic calibration between such sensors (i.e. finding their relative poses) is required to exploit effectively the sensor measurements and to perform data fusion. The approaches found in the literature to obtain such calibration either rely on the approximated parameters from the rig construction or propose ad-hoc solutions for specific LRF rigs. In this paper we present a novel solution for the extrinsic calibration of a set of at least three laser scanners from the information provided by the sensor measurements. This method only requires the lasers to observe a common planar surface from different orientations, thus there is no need of any specific calibration pattern. This calibration technique can be used with almost any geometric sensor configuration (except for sensors scanning parallel planes), and constitutes a versatile solution that is accurate, fast and easy to apply. This approach is validated with both simulated and real data.

Robots are often equipped with 2D laser-rangefinders (LRFs) and cameras since they complement well to each other. In order to correctly combine measurements from both sensors, it is required to know their relative pose, that is, to solve their extrinsic calibration. In this paper we present a new approach to such problem which relies on the observations of orthogonal trihedrons which are profusely found as corners in human-made scenarios. Thus, the method does not require any specific pattern, which turns the calibration process fast and simpler to perform. The estimated relative pose has proven to be also very precise since it uses two different types of constraints, line-to-plane and point-to-plane, as a result of a richer configuration than previous proposals that relies on plane or V-shaped patterns. Our approach is validated with synthetic and real experiments, showing better performance than the state-of-art methods.

For robots to solve hard tasks in real-world manufacturing and service contexts, they need to reason about both symbolic and geometric preconditions, and the effects of complex actions. We use an existing Knowledge of Volumes approach to robot task planning (KVP), which facilitates hybrid planning with symbolic actions and continuous-valued robot and object motion, and make two important additions to this approach: (i) new geometric predicates are added for complex object manipulation planning, and (ii) all geometric queries-such as collision and inclusion of objects and swept volumes-are implemented with a single-sided, bounded approximation, which calculates efficient and safe robot motion paths. Our task planning framework is evaluated in multiple scenarios, using concise and generic scenario definitions.

A humanoid robot can be viewed as a constrained dynamic system with constraints imposed by manipulation tasks, locomotion tasks, and the environment. This paper focuses on dealing with constraints in the upper-body of humanoid robots for manipulation tasks that involve coordinated motion of two arms. Inspired by research on human bimanual actions in the biomechanics area, we have developed the Extended-Cooperative-Task-Space (ECTS) representation that efficiently describes various coordinated motion tasks performed by a humanoid robot. Furthermore, we present a general whole-body control framework as an optimal controller based on Gauss's principle of least constraint. We show that all the constraints imposed on a humanoid system can be handled in a unified manner. The proposed framework is verified by numerical simulations on a Hubo II+ humanoid robot model.

This paper is on simultaneous exploration and topological map building in completely unknown environments. We propose an approach that consists of a continuous loop of local decision-making (where potential search directions are added to the current node) and global decision-making (where the robot moves along one of these directions and adds possibly a new node to the map ) using the previously proposed bubble space representation. As the robot switches between the two in a continual manner, it creates a coarse graph representation of the environment. There is a node for every explored location with a set of associated edges that either indicate potential search directions or connect to the other explored locations. The so-far constructed topological map contains all the required information in regards to deciding where to move with comparatively low memory requirements. Experimental results with an extensive real data set demonstrate that large terrains can be explored and mapped efficiently using this approach.

In this paper we present an approach for creating complete shape representations from a single depth image for robot grasping. We introduce algorithms for completing partial point clouds based on the analysis of symmetry and extrusion patterns in observed shapes. Identified patterns are used to generate a complete mesh of the object, which is, in turn, used for grasp planning. The approach allows robots to predict the shape of objects and include invisible regions into the grasp planning step. We show that the identification of shape patterns, such as extrusions, can be used for fast generation and optimization of grasps. Finally, we present experiments performed with our humanoid robot executing pick-up tasks based on single depth images and discuss the applications and shortcomings of our approach.

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

Autonomous docking for airborne energy transfer is an important unmanned aerial vehicle capability that has yet to be accomplished. The implications of this technology are far reaching because vehicle endurance can be significantly extended without requiring additional onboard energy storage or environmental energy collection. A major barrier to docking with a drogue is reliable and accurate knowledge of the drogue's state, relative to the aircraft that is performing the manoeuvre. We address this by propagating a derived cable-drogue dynamic model using onboard sensor measurements, then correcting the state and aerodynamic coefficients with air-to-air visual observations. The approach is verified through a series of close formation flights with a marker based infra-red vision system. These flights represent significant progress toward an autonomous docking demonstration.

In this study, we experimentally investigated the effect of robot fingertip stiffness on the maximum resistible force. The maximum resistible force is defined as the maximum tangential force at which the fingertip can maintain contact when applying and increasing tangential/shearing force. We include in the definition of this term the effect of fingertip deformation. In contrast to our previous study [11], cylindrical fingertips with flat surfaces were used in this study so that the contact area would remain the same when there was no tangential/shearing force. This made it possible to see the effect of fingertip stiffness more clearly. We also investigated the effect of curvature of the contact surface, which was not investigated in depth in [11]. The main findings are as follows. 1) Harder fingertips produce larger resistible forces, irrespective of the shape of the contact surface (flat or curved). 2) For harder fingertips, the maximum resistible force depends largely on the shape of the contact surface, while for softer fingertips, the shape has little effect. 3) For softer fingertips, the magnitude of the resistible force changes little even when the normal force increases.

Owing to the irregular terrain and microgravity environment of an asteroid, an appropriate locomotion mechanism is required for exploring the asteroid by a robot. In this paper, a ground grip locomotive robot is proposed. One of the most important requirements of the robot is the development of the finger gripping mechanism that can be attached on an uneven surface. To meet this requirement, grip condition and friction characteristics between the finger and the surface need to be understood. Therefore, we conducted an experimental evaluation of gripping characteristics by measuring the coefficients of friction between several types of fingers and simulated grounds. Moreover, the range of possible gripping angles for simulated ground was determined by conditional equations and confirmed by an experiment of gripping the ground using an air-floating system. In this paper, conical shape is shown to be effective for ground grip locomotive robot.

The ability to refuel satellites on-orbit using teleoperated robots could extend the life of satellite missions, potentially saving time and resources for both industry and space agencies. One critical step in the refueling process is to gain access to the satellite's fueling port by cutting through a seam of tape that adheres two sections of multi-layer insulation (MLI) covering the satellite body. The deformable, delicate, specular nature of the metalized tape used to adhere sections of MLI to one another makes cutting the tape seams a difficult task to perform remotely, a difficulty that is compounded by unavoidable multi-second communications delays. Virtual-fixture-assisted teleoperation can help mitigate some of these issues by constraining the motion of the cutting blade based on a priori knowledge of the task. Adding force control may further assist teleoperators by automatically setting a desired normal cutting force. This paper presents the results of a 20-participant user study for a representative satellite tape-cutting task, showing that force control based on a virtual fixture mapped to the MLI blanket plane can decrease both tape bunching and operator workload.

Few comparisons have been performed across torque controllers for exoskeletons, and differences among devices have made interpretation difficult. In this study, we designed, developed and compared the torque-tracking performance of nine control methods, including variations on classical feedback control, model-based control, adaptive control and iterative learning. Each was tested with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyography. Controllers were implemented on a tethered ankle exoskeleton with series elastic actuation. Measurements were taken while one human subject walked on a treadmill at 1.25 m·s−1 for one hundred steady-state steps. The combination of proportional control with damping injection and iterative learning resulted in the lowest errors for all high-level controllers. With time-based desired torque, root-mean-squared errors were 0.6 N·m (1.3% of peak desired torque) step by step, and 0.1 N·m (0.2%) on average. These results indicate that model-free, integration-free feedback control is suited to the uncertain dynamics of the human-robot system, while iterative learning is effective in the cyclic task of walking.

We present an experience-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which provides asymptotically near-optimal coverage of the configuration space, making storing, retrieving, and repairing past experiences very efficient with respect to memory and time. The Thunder framework improves upon past experience-based planners by storing experiences in a graph rather than in individual paths, eliminating redundant information, providing more opportunities for path reuse, and providing a theoretical limit to the size of the experience graph. These properties also lead to improved handling of dynamically changing environments, reasoning about optimal paths, and reducing query resolution time. The approach is demonstrated on a 30 degrees of freedom humanoid robot and compared with the Lightning framework, an experience-based planner that uses individual paths to store past experiences. In environments with variable obstacles and stability constraints, experiments show that Thunder is on average an order of magnitude faster than Lightning and planning from scratch. Thunder also uses 98.8% less memory to store its experiences after 10,000 trials when compared to Lightning. Our framework is implemented and freely available in the Open Motion Planning Library.