TechTalks from event: Technical session talks from ICRA 2012

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Force, Torque and Contacts in Grasping and Assembly

  • Object Motion-Decoupled Internal Force Control for a Compliant Multifingered Hand Authors: Prattichizzo, Domenico; Malvezzi, Monica; Wimboeck, Thomas; Aggravi, Marco
    Compliance in multifingered hand improves grasp stability and effectiveness of the manipulation tasks. Compliance of robotic hands depends mainly on the joint control parameters, on the mechanical design of the hand, as joint passive springs, and on the contact properties. In object grasping the primary task of the robotic hand is the control of internal forces which allows to satisfy the contact constraints and consequently to guarantee a stable grasp of the object. When compliance is an essential element of the multifingered hand, and the control of the internal forces is not designed to be decoupled from the object motion, it happens that a change in the internal forces causes the object trajectory to deviate from the planned path with consequent performance degradation. This paper studies the structural conditions to design an internal force controller decoupled from object motions. The analysis is constructive and a controller of internal forces is proposed. We will refer to this controller as object motion-decoupled control of internal forces. The force controller has been successfully tested on a realistic model of the DLR Hand II. This controller provides a trajectory interface allowing to vary the internal forces (and to specify object motions) of an underactuated hand, which can be used by higher-level modules, e.g. planning tools.
  • Robust, Inexpensive Resonant Frequency Based Contact Detection for Robotic Manipulators Authors: Backus, Spencer; Dollar, Aaron
    This paper presents a method for detecting contact on a compliant link utilizing a method to sense changes in the resonant frequency of the link due to external contact. The approach uses an inexpensive accelerometer mounted on or inside the compliant link and a phase locked loop circuit to oscillate the link at its resonant frequency. Using this approach, we are able to reliably sense contact anywhere on the link with a contact force threshold sensitivity of between 0.05 and 0.15 N depending on the contact location.
  • Testing Pressurized Spacesuit Glove Torque with an Anthropomorphic Robotic Hand Authors: Roberts, Dustyn; Kim, Joo H.
    While robotic hands have been developed for manipulation and grasping, their potential as tools for performance evaluation of engineered products - particularly compliant garments that are not easily modeled – has not been broadly studied. In this research, the development of a low-cost anthropomorphic robotic hand is introduced that is designed to characterize glove stiffness in a pressurized environment. The anthropomorphic robotic hand was designed to mimic a human hand in a neutral posture corresponding to the naturally relaxed position in zero gravity, and includes the transverse arch, longitudinal arch, and oblique flexion of the rays. The resulting model also allows for realistic donning and doffing of the prototype spacesuit glove, its pressurization, and torque testing of individual joints. Solid models and 3D printing enabled the rapid design iterations necessary to successfully work with the compliant pressure garment. The performance of the robotic hand is experimentally demonstrated with a spacesuit glove for different levels of pressures, and a unique data processing method is used to calculate the required actuator torque at each finger's knuckle joint. The reliable measurement method confirmed that glove finger torque increases as the internal pressure increases. The proposed robotic design and method provide an objective and systematic way of evaluating the performance of compliant gloves.
  • Learning Grasping Force from Demonstration Authors: Lin, Yun; Ren, Shaogang; Clevenger, Matthew; Sun, Yu
    This paper presents a novel force learning framework to learn fingertip force for a grasping and manipulation process from a human teacher with a force imaging approach. A demonstration station is designed to measure fingertip force without attaching force sensor on fingertips or objects so that this approach can be used with daily living objects. A Gaussian Mixture Model (GMM) based machine learning approach is applied on the fingertip force and position to obtain the motion and force model. Then a force and motion trajectory is generated with Gaussian Mixture Regression (GMR) from the learning result. The force and motion trajectory is applied to a robotic arm and hand to carry out a grasping and manipulation task. An experiment was designed and carried out to verify the learning framework by teaching a Fanuc robotic arm and a BarrettHand a pick-and-place task with demonstration. Experimental results show that the robot applied proper motions and forces in the pick-and-place task from the learned model.
  • Revised Force Control Using a Compliant Sensor with a Position Controlled Robot Authors: Lange, Friedrich; Jehle, Claudius; Suppa, Michael; Hirzinger, Gerd
    A different way of force control is presented, that is especially advantageous for position controlled robots. Instead of usual force control laws we rely on the well tuned position control loop and just use the force sensor to measure the target pose or to predict the desired trajectory. In combination with a compliant sensor we introduce an inherently stable framework of force control which almost inhibits all control errors. After an unexpected impact the force error is reduced independently from the sensor's bandwidth or delays in signal processing. Thus the (inevitable) impact force is more significant than the measured force control errors. The special case of a sensor that is mounted far away from a vertex-face contact is discussed, too.
  • Force Controlled Robotic Assembly without a Force Sensor Authors: Stolt, Andreas; Linderoth, Magnus; Robertsson, Anders; Johansson, Rolf
    The traditional way of controlling an industrial robot is to program it to follow desired trajectories. This approach is sufficient as long as the accuracy of the robot and the calibration of the workcell is good enough. In robotic assembly these conditions are usually not fulfilled, because of uncertainties, e.g., variability in involved parts and objects not gripped accurately. Using force control is one way to handle these difficulties. This paper presents a method of doing force control without a force sensor. The method is based on detuning of the low-level joint control loops, and the force is estimated from the control error. It is experimentally verified in a small part assembly task with a kinematically redundant robotic manipulator.

Hybrid Legged Robots

  • Passive Dynamic Walking of Viscoelastic-Legged Rimless Wheel Authors: Asano, Fumihiko; Kawamoto, Junji
    imit cycle walking including passive-dynamic walkers is mathematically modeled as a nonlinear hybrid dynamical system with state jumps in general. The generated motion is natural and energy efficient, but it is still pointed out that there are many differences between limit cycle walking and human walking. Non-existence of the period of double-limb support in the former comes from the assumption of instantaneous inelastic collision and is one of the biggest differences from the latter. In human walking, the period of double-limb support accounts for more than 10% of one cycle, and this must have significant effects on the gait stability and efficiency. Also in robot walking, utilizing the effects of double-limb support is essential to achieve more flexible, adaptive and human-like behavior. This paper then develops a novel mathematical model of a passive rimless wheel that emerges double-limb support by using the leg viscoelasticity, and numerically investigates the fundamental properties.
  • Control of Dynamic Locomotion for the Hybrid Wheel-Legged Mobile Robot by using Unstable-Zeros Cancellation Authors: Suzumura, Akihiro; Fujimoto, Yasutaka
    In this paper, a new method of center of mass trajectory planning using the zero-phase low pass filter is proposed. This method is based on a table-cart model which simply describes the relationship between center of mass and zero moment point. Generally, zero moment point should be controlled to realize dynamic motion. This method can easily generate the center of mass trajectory which realizes the desired zero moment point. In our study, this method is applied to wheel-legged locomotion. We will show the result that zero moment point can be sufficiently controlled even if quadruped wheel-legged mobile robot is apploximated to a table-cart model. The effectiveness of the idea is validated by simulation and experiment.
  • Comparison of Cost Functions for Electrically Driven Running Robots Authors: Remy, C. David; Buffinton, Keith; Siegwart, Roland
    In this work we apply optimal control to create running gaits for the model of an electrically driven one legged hopper, and compare the results obtained for five different objective functions. By using high compliant series elastic actuators, the motions of joint and motor are decoupled, which allows the exploitation of natural dynamics. Depending on the cost function, this exploitation varies. Energy is injected at different points of time, the amplitude of actuator action changes significantly, and the optimal gear ratios differ by a factor of two. Variations are, however, comparable over a wide range of hopping heights and running velocities. Purely force-based cost functions prove to be ill-suited for such non-conservative systems, and it is shown that thermal electrical losses, in contrast to common belief, do not dominate energy expenditure. The numerical results are corroborated by detailed analytical considerations which give general insights into optimal excitation with electric actuators.
  • A Reduced-Order Dynamical Model for Running with Curved Legs Authors: Jun, Jae Yun; Clark, Jonathan
    Some of the unique properties associated with running with curved legs or feet (as opposed to point-contact feet) are examined in this work, including the rolling contact motion, the change of the leg's effective stiffness and rest length, the shift of the effective flexion point along the leg, and the compliant-vaulting motions over its tiptoe during stance. To examine these factors, a novel torque-driven reduced-order dynamical model with a clock-based control scheme and with a simple motor model is developed (named as torque-driven and damped half-circle-leg model (TD-HCL)). The controller parameters are optimized for running efficiency and forward speed using a direct search method, and the results are compared to those of other existing dynamical models such as the torque-driven and damped spring-loaded-inverted-pendulum (TD-SLIP) model, the torque-driven and damped two-segment-leg (TD-TSL) model, and the TD-SLIP with a rolling foot (TD-SLIP-RF) model. The results show that running with rolling is more efficient and more stable than running with legs that involve pin joint contact model. This work begins to explain why autonomous robots using curved legs run efficiently and robustly. New curved legs are designed and manufactured in order to validate these results.
  • FastRunner: A Fast, Efficient and Robust Bipedal Robot. Concept and Planar Simulation Authors: Cotton, Sebastien; OLARU, IONUT MIHAI CONSTANTIN; bellman, matthew; van der ven, tim; Godowski, Johnny C; Pratt, Jerry
    Bipedal robots are currently either slow, energetically inefficient and/or require a lot of control to maintain their stability. This paper introduces the FastRunner, a bipedal robot based on a new leg architecture. Simulation results of a Planar FastRunner demonstrate that legged robots can run fast, be energy efficient and inherently stable. The simulated FastRunner has a cost of transport of 1.4 and requires only a local feedback of the hip position to reach 35.4 kph from stop in simulation.
  • Zero-Moment Point Based Balance Control of Leg-Wheel Hybrid Structures with Inequality Constraints of Dynamic Behavior Authors: An, Sang-ik; Oh, Yonghwan; Kwon, Dong-Soo
    This paper discusses an unified method of the tracking and balancing controls for leg-wheel hybrid structures in an effort to improve the mobility over hard, flat surfaces. Preliminarily, we analyzed the contact constraint to formulate a dynamically decoupled model in the task space. Then, inequality constraints were determined to restrict the dynamic behavior of the system within the given bounds for the dynamic stability and the actuator saturation. The inequality constraints were applied to the reference control input that was designed for the mechanism to traverse the desired trajectories without the constraints. To find the constrained control input, a quadratic objective function was proposed to minimize the modification error of the control inputs. We tested the effectiveness of the proposed algorithm by comparing simulation results with our previous research.

Visual Tracking

  • Generic Realtime Kernel Based Tracking Authors: Hadj-Abdelkader, Hicham; Mezouar, Youcef; Chateau, Thierry
    This paper deals with the design of a generic visual tracking algorithm suitable for a large class of camera (single viewpoint sensors). It is based on the estimation of the relationship between observations and motion on the sphere. This is efficiently achieved using a kernel-based regression function on a generic linearly-weighted sum of non-linear basis functions. We also present two set of experiments. The first one shows the efficiency of our algorithm through the tracking in video sequences acquired with three types of cameras (conventional, dioptric-fisheye and catadioptric). The real-time performances will be shown by tracking one or several planes. The second set of experiments presents an application of our tracking algorithm to visual servoing with a fisheye camera.
  • Generative Object Detection and Tracking in 3D Range Data Authors: Kaestner, Ralf; Maye, Jerome; Pilat, Yves; Siegwart, Roland
    This paper presents a novel approach to tracking dynamic objects in 3D range data. Its key contribution lies in the generative object detection algorithm which allows the tracker to robustly extract objects of varying sizes and shapes from the observations. In contrast to tracking methods using discriminative detectors, we are thus able to generalize over a wide range of object classes matching our assumptions. Whilst the generative model underlying our framework inherently scales with the complexity and the noise characteristics of the environment, all parameters involved in the detection process obey a clean probabilistic interpretation. Nevertheless, our unsupervised object detection and tracking algorithm achieves real-time performance, even in highly dynamic scenarios covering a significant amount of moving objects. Through an application to populated urban settings, we are able to show that the tracking performance of the presented approach yields results which are comparable to state-of-the-art discriminative methods.
  • Moving Vehicle Detection and Tracking in Unstructured Environments Authors: Wojke, Nicolai; Häselich, Marcel
    The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable in urban environments. To overcome the unavailability of road network information for background separation, we introduce a foreground model that incorporates geometric as well as temporal cues. Local shape estimates successfully guide vehicle localization. Extensive evaluation shows that the system works reliable and efficient in various outdoor scenarios without any prior knowledge about the road network. Experiments with our own sensor as well as on publicly available data from the DARPA Urban Challenge revealed more than 96 % correctly identified vehicles.
  • Learning to Place New Objects Authors: Jiang, Yun; Zheng, Changxi; Lim, Marcus; Saxena, Ashutosh
    The ability to place objects in an environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred location/orientation. For instance, it is preferred that a plate be inserted vertically into the slot of a dish-rack as compared to being placed horizontally in it. Unstructured environments such as homes have a large variety of object types as well as of placing areas. Therefore our algorithms should be able to handle placing new object types and new placing areas. These reasons make placing a challenging manipulation task. In this work, we propose using a supervised learning approach for finding good placements given point-clouds of the object and the placing area. Our method combines the features that capture support, stability and preferred configurations, and uses a shared sparsity structure in its the parameters. Even when neither the object nor the placing area is seen previously in the training set, our learning algorithm predicts good placements. In robotic experiments, our method enables the robot to stably place known objects with a 98% success rate and 98% when also considering semantically preferred orientations. In the case of placing a new object into a new placing area, the success rate is 82% and 72%.
  • Lost in Translation (and Rotation): Rapid Extrinsic Calibration for 2D and 3D LIDARs Authors: Maddern, William; Harrison, Alastair; Newman, Paul
    This paper describes a novel method for determining the extrinsic calibration parameters between 2D and 3D LIDAR sensors with respect to a vehicle base frame. To recover the calibration parameters we attempt to optimize the quality of a 3D point cloud produced by the vehicle as it traverses an unknown, unmodified environment. The point cloud quality metric is derived from Rényi Quadratic Entropy and quantifies the compactness of the point distribution using only a single tuning parameter. We also present a fast approximate method to reduce the computational requirements of the entropy evaluation, allowing unsupervised calibration in vast environments with millions of points. The algorithm is analyzed using real world data gathered in many locations, showing robust calibration performance and substantial speed improvements from the approximations.