TechTalks from event: Technical session talks from ICRA 2012

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Marine Robotics I

  • Towards Improving Mission Execution for Autonomous Gliders with an Ocean Model and Kalman Filter Authors: Smith, Ryan N.; Kelly, Jonathan; Sukhatme, Gaurav
    Effective execution of a planned path by an underwater vehicle is important for proper analysis of the gathered science data, as well as to ensure the safety of the vehicle during the mission. Here, we propose the use of an unscented Kalman filter to aid in determining how the planned mission is executed. Given a set of waypoints that define a planned path and a dicretization of the ocean currents from a regional ocean model, we present an approach to determine the time interval at which the glider should surface to maintain a prescribed tracking error, while also limiting its time on the ocean surface. We assume practical mission parameters provided from previous field trials for the problem set up, and provide the simulated results of the Kalman filter mission planning approach. The results are initially compared to data from prior field experiments in which an autonomous glider executed the same path without pre-planning. Then, the results are validated through field trials with multiple autonomous gliders implementing different surfacing intervals simultaneously while following the same path.
  • Position and Velocity Filters for Intervention AUVs Based on Single Range and Depth Measurements Authors: Viegas, Daniel; Batista, Pedro; Oliveira, Paulo; Silvestre, Carlos
    This paper proposes novel cooperative navigation solutions for an Intervention Autonomous Underwater Vehicle (I-AUV) working in tandem with an Autonomous Surface Craft (ASC). The I-AUV is assumed to be moving in the presence of constant unknown ocean currents, and aims to estimate its position relying on measurements of its range to the ASC and of its depth relatively to the sea level. Two different scenarios are considered: in one, the ASC transmits its position and velocity to the I-AUV, while in the other the ASC transmits only its position, and the I-AUV has access to measurements of its velocity relative to the ASC. A sufficient condition for observability and a method for designing state observers with Globally Asymptotically Stable (GAS) error dynamics are presented for both problems. Finally, simulation results are included and discussed to assess the performance of the proposed solutions in the presence of measurement noise.
  • Uncertainty-Driven View Planning for Underwater Inspection Authors: Hollinger, Geoffrey; Englot, Brendan; Hover, Franz; Mitra, Urbashi; Sukhatme, Gaurav
    We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). In such scenarios, the goal is to construct an accurate 3D model of the structure and to detect any anomalies (e.g., foreign objects or deformations). We propose a method for constructing 3D meshes from sonar-derived point clouds that provides watertight surfaces, and we introduce uncertainty modeling through non-parametric Bayesian regression. Uncertainty modeling provides novel cost functions for planning the path of the AUV to minimize a metric of inspection performance. We draw connections between the resulting cost functions and submodular optimization, which provides insight into the formal properties of active perception problems. In addition, we present experimental trials that utilize profiling sonar data from ship hull inspection.
  • Formation Control of Underactuated Autonomous Surface Vessels Using Redundant Manipulator Analogs Authors: Bishop, Bradley
    In this paper, we present a method utilizing redundant manipulator analogs for formation control of underactuated autonomous surface vessels (ASVs) with realistic turning constraints and dynamics. The method used relies on casting the swarm as a single entity and utilizing redundant manipulator techniques to guarantee task-level formation control as well as obstacle avoidance and secondary tasks such as mean position control. The method presented differs from other approaches in that the units herein represent a larger class of ASVs with realistic limitations on vessel motions and that the exact position of each of the units on the formation profile is not specified.
  • Delayed State Information Filter for USBL-Aided AUV Navigation Authors: Ribas, David; Ridao, Pere; Mallios, Angelos; Palomeras, Narcis
    This paper presents a navigation system for an Autonomous Underwater Vehicle (AUV) which merges standard dead reckoning navigation data with absolute position fixes from an Ultra-Short Base Line (USBL) system. Traditionally, the USBL transceiver is located on the surface, which makes necessary to feed the position fixes back to the AUV by means of an acoustic modem. An Information filter, which maintains a bounded circular buffer of past vehicle poses, is in charge of the sensor data fusion while dealing with de delays induced by the acoustic communication. The method is validated using a data set gathered for a dam inspection task.
  • Miniature Underwater Glider: Design, Modeling, and Experimental Results Authors: Zhang, Feitian; Thon, John; Thon, Cody; Tan, Xiaobo
    The concept of gliding robotic fish combines gliding and fin-actuation mechanisms to realize energy-efficient locomotion and high maneuverability, and holds strong promise for mobile sensing in versatile aquatic environments. In this paper we present the modeling and design of a miniature fish-like glider, a key enabling component for gliding robotic fish. The full dynamics of the glider is first derived and then reduced to the sagittal plane, where the lift, drag, and pitch moment coefficients are obtained as linear or quadratic functions of the attack angle based on computational fluid dynamics (CFD) analysis. The model is used to design the glider by accommodating stringent constraints on dimensions yet meeting the desired specification on speed. A fully untethered prototype of underwater glider is developed, with a weight of 4 kg and length of 40 cm. With a net buoyancy of 20 g, it realizes a steady gliding speed of 20 cm/s. The volume and net buoyancy of this glider are less than 10% and 5%, respectively, of those of reported gliders in the literature, and its speed per unit net buoyancy is over 9 times of those other vehicles. Experimental results have shown that the model is able to capture well both the steady glide behavior under different control inputs, and the dynamics during transients.

Planning and Navigation of Biped Walking

  • Real-Time Footstep Planning for Humanoid Robots among 3D Obstacles Using a Hybrid Bounding Box Authors: Perrin, Nicolas Yves; Stasse, Olivier; Lamiraux, Florent; Kim, Young J.; Manocha, Dinesh
    In this paper we introduce a new bounding box method for footstep planning for humanoid robots. Similar to the classic bounding box method (which uses a single rectangular box to encompass the robot) it is computationally efficient, easy to implement and can be combined with any rigid body motion planning library. However, unlike the classic bounding box method, our method takes into account the stepping over capabilities of the robot, and generates precise leg trajectories to avoid obstacles on the ground. We demonstrate that this method is well suited for footstep planning in cluttered environments.
  • Foot Placement for Planar Bipeds with Point Feet Authors: van Zutven, Pieter; Kostic, Dragan; Nijmeijer, Hendrik
    When humanoid robots are going to be used in society, they should be capable to maintain the balance. Knowing where to step appears to be crucially important to remain balanced. This paper contributes the foot placement indicator (FPI), an extension to the foot placement estimator (FPE) for planar bipeds with point feet and an arbitrary number of non-massless links. The method uses conservation of energy to determine where the planar biped needs to step to remain in balance. Simulations of the FPI show improved foot placement for balance with respect to the FPE.
  • A Framework for Extreme Locomotion Planning Authors: Dellin, Christopher; Srinivasa, Siddhartha
    A person practicing parkour is an incredible display of intelligent planning; he must reason carefully about his velocity and contact placement far into the future in order to locomote quickly through an environment. We seek to develop planners that will enable robotic systems to replicate this performance. An ideal planner can learn from examples and formulate feasible full-body plans to traverse a new environment. The proposed approach uses momentum equivalence to reduce the full-body system into a simplified one. Low-dimensional trajectory primitives are then composed by a sampling planner called Sampled Composition A* to produce candidate solutions that are adjusted by a trajectory optimizer and mapped to a full-body robot. Using primitives collected from a variety of sources, this technique is able to produce solutions to an assortment of simulated locomotion problems.
  • Adaptive Level-of-Detail Planning for Efficient Humanoid Navigation Authors: Hornung, Armin; Bennewitz, Maren
    In this paper, we consider the problem of efficient path planning for humanoid robots by combining grid-based 2D planning with footstep planning. In this way, we exploit the advantages of both frameworks, namely fast planning on grids and the ability to find solutions in situations where grid-based planning fails. Our method computes a global solution by adaptively switching between fast grid-based planning in open spaces and footstep planning in the vicinity of obstacles. To decide which planning framework to use, our approach classifies the environment into regions of different complexity with respect to the traversability. Experiments carried out in a simulated office environment and with a Nao humanoid show that (i) our approach significantly reduces the planning time compared to pure footstep planning and (ii) the resulting plans are almost as good as globally computed optimal footstep paths.
  • Dominant Sources of Variability in Passive Walking Authors: Nanayakkara, Thrishantha; Byl, Katie; Liu, Hongbin; Song, Xiaojing; Villabona, Tim
    This paper investigates possible sources of variability in the dynamics of legged locomotion, even in its most idealized form. The rimless wheel model is a seemingly deterministic legged dynamic system, popular within the legged locomotion community for understanding basic collision dynamics and energetics during passive phases of walking. Despite the simplicity of this legged model, however, experimental motion capture data recording the passive step-to-step dynamics of a rimless wheel down a constant-slope terrain actually demonstrates significant variability, providing strong evidence that stochasticity is an intrinsic-and thus unavoidable-property of legged locomotion that should be modeled with care when designing reliable walking machines. We present numerical comparisons of several hypotheses as to the dominant source(s) of this variability: 1) the initial distribution of the angular velocity, 2) the uneven profile of the leg lengths and 3) the distribution of the coefficients of friction and restitution across collisions. Our analysis shows that the 3rd hypothesis most accurately predicts the noise characteristics observed in our experimental data while the 1st hypothesis is also valid for certain contexts of terrain friction. These findings suggest that variability due to ground contact dynamics, and not simply due to geometric variations more typically modeled in terrain, is important in determining the stochasticity and resulting stability of walking robots. Althou
  • First Steps Toward Underactuated Human-Inspired Bipedal Robotic Walking Authors: Ames, Aaron
    This paper presents the first steps toward going from human data to formal controller design to experimental realization in the context of underactuated bipedal robots. Specifically, by studying experimental human walking data, we find that specific outputs of the human, i.e., functions of the kinematics, appear to be canonical to walking and are all characterized by a single function of time, termed a human walking function. Using the human outputs and walking function, we design a human-inspired controller that drives the output of the robot to the output of the human as represented by the walking function. The main result of the paper is an optimization problem that determines the parameters of this controller so as to guarantee stable underactuated walking that is as "close" as possible to human walking. This result is demonstrated through the simulation of a physical underactuated 2D bipedal robot, AMBER. Experimentally implementing this control on AMBER through "feed-forward" control, i.e., trajectory tracking, repeatedly results in 5-10 steps.

Sensing for manipulation

  • Using Depth and Appearance Features for Informed Robot Grasping of Highly Wrinkled Clothes Authors: Ramisa, Arnau; Alenyà, Guillem; Moreno-Noguer, Francesc; Torras, Carme
    Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple re-grasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a "grasp goodness" criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.
  • Integrating surface-based hypotheses and manipulation for autonomous segmentation and learning of object representations Authors: Ude, Ales; Schiebener, David; Morimoto, Jun
    Learning about new objects that a robot sees for the first time is a difficult problem because it is not clear how to define the concept of object in general terms. In this paper we consider as objects those physical entities that are comprised of features which move consistently when the robot acts upon them. Among the possible actions that a robot could apply to a hypothetical object, pushing seems to be the most suitable one due to its relative simplicity and general applicability. We propose a methodology to generate and apply pushing actions to hypothetical objects. A probing push causes visual features to move, which enables the robot to either confirm or reject the initial hypothesis about existence of the object. Furthermore, the robot can discriminate the object from the background and accumulate visual features that are useful for training of state of the art statistical classifiers such as bag of features.
  • From Object Categories to Grasp Transfer Using Probabilistic Reasoning Authors: Madry, Marianna; Song, Dan; Kragic, Danica
    In this paper we address the problem of grasp generation and grasp transfer between objects using categorical knowledge. The system is built upon an i)~active scene segmentation module, able of generating object hypotheses and segmenting them from the background in real time, ii)~object categorization system using integration of 2D and 3D cues, and iii)~probabilistic grasp reasoning system. Individual object hypotheses are first generated, categorized and then used as the input to a grasp generation and transfer system that encodes task, object and action properties. The experimental evaluation compares individual 2D and 3D categorization approaches with the integrated system, and it demonstrates the usefulness of the categorization in task-based grasping and grasp transfer.
  • Voting-Based Pose Estimation for Robotic Assembly Using a 3D Sensor Authors: Choi, Changhyun; Taguchi, Yuichi; Tuzel, Oncel; Liu, Ming-Yu; Ramalingam, Srikumar
    We propose a voting-based pose estimation algorithm applicable to 3D sensors, which are fast replacing their 2D counterparts in many robotics, computer vision, and gaming applications. It was recently shown that a pair of oriented 3D points, which are points on the object surface with normals, in a voting framework enables fast and robust pose estimation. Although oriented surface points are discriminative for objects with sufficient curvature changes, they are not compact and discriminative enough for many industrial and real-world objects that are mostly planar. As edges play the key role in 2D registration, depth discontinuities are crucial in 3D. In this paper, we investigate and develop a family of pose estimation algorithms that better exploit this boundary information. In addition to oriented surface points, we use two other primitives: boundary points with directions and boundary line segments. Our experiments show that these carefully chosen primitives encode more information compactly and thereby provide higher accuracy for a wide class of industrial parts and enable faster computation. We demonstrate a practical robotic bin-picking system using the proposed algorithm and a 3D sensor.
  • Supervised Learning of Hidden and Non-Hidden 0-Order Affordances and Detection in Real Scenes Authors: Aldoma, Aitor; Tombari, Federico; Vincze, Markus
    The ability to perceive possible interactions with the environment is a key capability of task-guided robotic agents. An important subset of possible interactions depends solely on the objects of interest and their position and orientation in the scene. We call these object-based interactions $0$-order affordances and divide them among non-hidden and hidden whether the current configuration of an object in the scene renders its affordance directly usable or not. Conversely to other works, we propose that detecting affordances that are not directly perceivable increase the usefulness of robotic agents with manipulation capabilities, so that by appropriate manipulation they can modify the object configuration until the seeked affordance becomes available. In this paper we show how $0$-order affordances depending on the geometry of the objects and their pose can be learned using a supervised learning strategy on 3D mesh representations of the objects allowing the use of the whole object geometry. Moreover, we show how the learned affordances can be detected in real scenes obtained with a low-cost depth sensor like the Microsoft Kinect through object recognition and 6D0F pose estimation and present results for both learning on meshes and detection on real scenes to demonstrate the practical application of the presented approach.
  • Estimating Object Grasp Sliding Via Pressure Array Sensing Authors: Alcazar, Javier Adolfo; Barajas, Leandro
    Advances in design and fabrication technologies are enabling the production and commercialization of sensor-rich robotic hands with skin-like sensor arrays. Robotic skin is poised to become a crucial interface between the robot embodied intelligence and the external world. The need to fuse and make sense out of data extracted from skin-like sensors is readily apparent. This paper presents a real-time sensor fusion algorithm that can be used to accurately estimate object position, translation and rotation during grasping. When an object being grasped moves across the sensor array, it creates a sliding sensation; the spatial-temporal sensations are estimated by computing localized slid vectors using an optical flow approach. These results were benchmarked against an L-inf Norm approach using a nominal known object trajectory generated by sliding and rotating an object over the sensor array using a second, high accuracy, industrial robot. Rotation and slid estimation can later be used to improve grasping quality and dexterity