TechTalks from event: Technical session talks from ICRA 2012

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Soft Tissue Interaction

  • Novel Indentation Depth Measuring System for Stiffness Characterization in Soft Tissue Palpation Authors: Wanninayake, Indika Bandara; Althoefer, Kaspar; Seneviratne, lakmal
    This paper presents a novel approach to measuring the indentation depth of a stiffness sensor in real time during a soft tissue palpation activity. The proposed system is integrated into a stiffness probe and is designed to intra-operatively aid the surgeon to rapidly identify the tissue abnormalities with minimum measurement inaccuracies due to tissue surface profile variations. Stiffness probe and the associated surface profile sensors are pneumatic and the newly designed system can concurrently measure the indentation depth and surface profile variations while sliding over the soft tissues in any direction in a near frictionless manner. With the pneumatic pressure maintained constant, the displacement of the sensing element is a direct function of the stiffness of the tissue under investigation. The sensor has a tunable force range and the indentation force can be adjusted externally to match tissue limitations. The prototype of the new design of stiffness probe was calibrated and tested on silicone blocks simulating soft tissue. The results show that this sensor can measure indentation depth more accurately than air cushion probe alone. The structure, working principle, and a mathematical model for this new design are described.
  • Robotic Compression of Soft Tissue Authors: Nia Kosari, Sina; Ramadurai, Srikrishnan; Chizeck, Howard; Hannaford, Blake
    This paper investigates automation of soft tissue compression for robot-assisted surgery. This is a fundamental task in surgery and includes interaction with a variety of tissues with unknown properties. In addition, due to sterilization and size constraints the use of contact force and position sensors are often avoided in surgical applications. We propose an Adaptive Model Predictive Control approach for execution of given tool trajectories in contact with unknown tissues in the absence of contact measurements. The Unscented Kalman Filter is employed in advance of system operation to identify the dynamics of a cable driven manipulator. These dynamics are then used to estimate contact force and position in free motion and in contact with tissue. An optimal control problem for automating tissue compression is formulated and is solved in real-time using Differential Dynamic Programming with Automatic Differentiation. The proposed methods are evaluated in experiments on an artificial tissue sample with unknown properties.
  • Soft Tissue Force Control Using Active Observers and Viscoelastic Interaction Model Authors: Moreira, Pedro; Liu, Chao; Zemiti, Nabil; Poignet, Philippe
    Controlling the interaction between the robot and living soft tissues has became an important issue as the number of robots inside the operating room increases. Many research works have been done in order to control this interaction. Nowadays, researches are running in force control for helping surgeons in medical procedures such as motion compensation in beating heart surgeries and tele-operation systems with haptic feedback. The viscoelasticity property of the interaction between organ tissue and robotic instrument further complicates the force control design which is much easier in other applications by assuming the interaction model to be elastic (industry, stiff object manipulation, etc.). In order to increase the performance of a model based force control, this work presents a force control scheme using Active Observer (AOB) based on a viscoelastic interaction model. The control scheme has shown to be stable through theoretical analysis and its performance was evaluated and compared with a control scheme based on a classical elastic model through experiments, showing that a more realistic model can increases the performance of the force control.
  • Estimation of Soft Tissue Mechanical Parameters from Robotic Manipulation Data Authors: Boonvisut, Pasu; Jackson, Russell; Cavusoglu, M. Cenk
    Robotic motion planning algorithms used for task automation in robotic surgical systems rely on availability of accurate models of target soft tissue's deformation. Relying on generic tissue parameters in constructing the tissue deformation models is problematic; because, biological tissues are known to have very large (inter- and intra-subject) variability. A priori mechanical characterization (e.g., uniaxial bench test) of the target tissues before a surgical procedure is also not usually practical. In this paper, a method for estimating mechanical parameters of soft tissue from sensory data collected during robotic surgical manipulation is presented. The method uses force data collected from a multiaxial force sensor mounted on the robotic manipulator, and tissue deformation data collected from a stereo camera system. The tissue parameters are then estimated using an inverse finite element method. The effects of measurement and modeling uncertainties on the proposed method are analyzed in simulation. The results of experimental evaluation of the method are also presented.
  • Modeling of Needle-Tissue Interaction Forces During Surgical Suturing Authors: Jackson, Russell; Cavusoglu, M. Cenk
    This paper presents a model of needle tissue interaction forces that a rigid suture needle experiences during surgical suturing. The needle-tissue interaction forces are modeled as the sum of lumped parameters. The model has three main components; friction, tissue compression, and cutting forces. The tissue compression force uses the area that the needle sweeps out during a suture to estimate both the force magnitude and force direction. The area that the needle sweeps out is a direct result of driving the needle in a way that does not follow the natural curve of the needle. The friction force is approximated as a static friction force along the shaft of the needle. The cutting force acts only on the needle tip. The resulting force and torque model is experimentally validated using a tissue phantom. These results indicate that the proposed lumped parameter model is capable of accurately modeling the forces experienced during a suture.
  • Modeling of a Steerable Catheter Based on Beam Theory Authors: Khoshnam, Mahta; Azizian, Mahdi; Patel, Rajnikant V.
    Catheter-based cardiac ablation is an interventional treatment for heart arrhythmias. Pull-wire steerable catheters are guided to the heart chambers through the vasculature in order to deliver energy to destroy faulty electrical pathways in the heart. The effectiveness of this treatment is dependent on the accuracy of positioning the catheter tip at the target location and also on maintaining contact with the target while the heart is beating. Therefore, it is desirable to perform hybrid force/position control of the catheter tip. We have studied the problem of modeling the distal part of a steerable catheter using beam theory and have developed and validated a static force-deflection model through extensive experiments. It is shown that the model can estimate the shape of the bending section of a catheter using force information and without requiring any knowledge of the catheter’s internal structure.

Pose Estimation

  • Invariant Momentum-Tracking Kalman Filter for Attitude Estimation Authors: Persson, Sven Mikael; Sharf, Inna
    This paper presents the development, simulation and experimental testing of a non-linear Kalman filter for attitude estimation. This non-linear filter is able to conserve the invariants of the Kalman filter, i.e., the expectations on state estimates and their covariances, by operating in the Lie algebra of SO(3) and along the trajectory of evolving angular momentum. The main feature of this novel discrete-time filter is that the linearization of the Gaussian uncertainty around these permanent trajectories leads to a locally optimal Kalman gain matrix. Results confirm that this Invariant Momentum-tracking Kalman Filter (IMKF) out-performs state-of-the-art approaches such as the Extended Kalman Filter (EKF), and Invariant Extended Kalman Filter (IEKF). At very-low sampling rates, EKFs suffer from divergence as the uncertainty propagation is corrupted by the underlying system approximations. The IMKF suffers no such problems according to the theoretical developments and results reported here.
  • Complementary Filtering Approach to Orientation Estimation Using Inertial Sensors Only Authors: Kubelka, Vladimir; Reinstein, Michal
    Precise and reliable estimation of orientation plays crucial role for any mobile robot operating in unknown environment. The most common solution to determination of the three orientation angles: pitch, roll, and yaw, relies on the Attitude and Heading Reference System (AHRS) that exploits inertial data fusion (accelerations and angular rates) with magnetic measurements. However, in real world applications strong vibration and disturbances in magnetic field usually cause this approach to provide poor results. Therefore, we have devised a new approach to orientation estimation using inertial sensors only. It is based on modified complementary filtering and was proved by precise laboratory testing using rotational tilt platform as well as by robot field-testing. In the final, the algorithm well outperformed the commercial AHRS solution based on magnetometer aiding.
  • Design of Complementary Filter for High-Fidelity Attitude Estimation Based on Sensor Dynamics Compensation with Decoupled Properties Authors: Masuya, Ken; Sugihara, Tomomichi; Yamamoto, Motoji
    A high-fidelity attitude estimation technique for wide and irregular movements is proposed, in which heterogeneous inertial sensors are combined in complementary way. Although the working frequency ranges of each sensor are not necessarily complementary, inverse sensor models are utilized in order to restore the original movements. In the case of 3D rotation, the sensor dynamics displays a highly nonlinear property. Even if it is approximated by a linear system, the inverse model of a sensor tends to be non-proper and unstable. An idea is to decouple it into the dynamics compensation part approximated by a linear transfer function and the strictly nonlinear coordinate transformation part. Bandpass filters inserted before the coordinate transformation guarantee that the total transfer function becomes proper and stable. Particularly, the differential operator of a high-pass filter cancels the integral operator included in the dynamics compensation of the rate gyroscope, which causes instability. The proposed method is more beneficial than Kalman filter in terms of the implementation since it facilitates a systematic design of the filter.
  • A Low-Cost and Fail-Safe Inertial Navigation System for Airplanes Authors: Leutenegger, Stefan; Siegwart, Roland
    A typical Inertial Navigation System (INS) fuses acceleration and angular rate readings with aiding measurements obtained by GPS and a compass. Here we present a robust state estimation framework based on the Extended Kalman Filter (EKF) applied to low-cost electronics typically installed on-board small unmanned airplanes. It uses airspeed measurements as a backup operation mode replacing GPS updates when temporarily unavailable. We demonstrate the applicability of the proposed approach to real-world scenarios using a challenging dataset recorded on-board a manned glider including long-term circling. A comparison between the normal operation mode and the backup solution reveals minimal difference between the respective orientation estimates, a position error growth sub-linear with time during GPS outage and a seamless transition back to GPS-based operation.
  • Robust Multi-Sensor, Day/Night 6-DOF Pose Estimation for a Dynamic Legged Vehicle in GPS-Denied Environments Authors: Ma, Jeremy; susca, sara; Bajracharya, Max; Matthies, Larry; Malchano, Matthew; Wooden, David
    We present a real-time system that enables a highly capable dynamic quadruped robot to maintain an accurate 6-DOF pose estimate (better than 0.5m over every 50m traveled) over long distances traversed through complex, dynamic outdoor terrain, during day and night, in the presence of camera occlusion and saturation, and occasional large external disturbances, such as slips or falls. The system fuses a stereo-camera sensor, inertial measurement units (IMU), and leg odometry with an Extended Kalman Filter (EKF) to ensure robust, low-latency performance. Extensive experimental results obtained from multiple field tests are presented to illustrate the performance and robustness of the system over hours of continuous runs over hundreds of meters of distance traveled in a wide variety of terrains and conditions.
  • Global Pose Estimation with Limited GPS and Long Range Visual Odometry Authors: Rehder, Joern; Gupta, Kamal; Nuske, Stephen; Singh, Sanjiv
    Here we present an approach to estimate the global pose of a vehicle in the face of two distinct problems; first, when using stereo visual odometry for relative motion estimation, a lack of features at close range causes a bias in the motion estimate. The other challenge is localizing in the global coordinate frame using very infrequent GPS measurements. Solving these problems we demonstrate a method to estimate and correct for the bias in visual odometry and a sensor fusion algorithm capable of exploiting sparse global measurements. Our graph-based state estimation framework is capable of inferring global orientation using a unified representation of local and global measurements and recovers from inaccurate initial estimates of the state, as intermittently available GPS information may delay the observability of the entire state. We also demonstrate a reduction of the complexity of the problem to achieve real-time throughput. In our experiments, we show in an outdoor dataset with distant features where our bias corrected visual odometry solution makes a five-fold improvement in the accuracy of the estimated translation compared to a standard approach. For a traverse of 2km we demonstrate the capabilities of our graph-based state estimation approach to successfully infer global orientation with as few as 6 GPS measurements and with two-fold improvement in mean position error using the corrected visual odometry.

Humanoid Motion Planning and Control

  • Controlling the Planar Motion of a Heavy Object by Pushing with a Humanoid Robot Using Dual-Arm Force Control Authors: Nozawa, Shunichi; Kakiuchi, Yohei; Okada, Kei; Inaba, Masayuki
    Pushing heavy and large objects in a plane requires generating correct operational forces that compensate for unpredictable ground-object friction forces. This is a challenge because the reaction forces from the heavy object can easily cause a humanoid robot to slip at its feet or lose balance and fall down. Although previous research has addressed humanoid robot balancing problems to prevent falling down while pushing an object, there has been little discussion about the problem of avoiding slipping due to the reaction forces from the object. We extend a full-body balancing controller by simultaneously controlling the reaction forces of both hands using dual-arm force control. The main contribution of this paper is a method to calculate dual-arm reference forces considering the moments around the vertical axis of the humanoid robot and objects. This method involves estimating friction forces based on force measurements and controlling reaction forces to follow the reference forces. We show experimental results on the HRP-2 humanoid robot pushing a 90[kg] wheelchair.
  • Hopping at the Resonance Frequency: A Pattern Generation Technique for Bipedal Robots with Elastic Joints Authors: Ugurlu, Barkan; Saglia, Jody Alessandro; Tsagarakis, Nikolaos; Caldwell, Darwin G.
    It is known that bipedal robots with passive compliant structures have obvious advantages over stiff robots, as they are able to handle the potential energy management. Therefore, this paper is aimed at presenting a jumping pattern generation method that takes advantage of this property via the utilization of the ankle joint resonance frequency, which is of special importance. To begin with, the resonance frequency is determined through a system identification procedure on our actual robot. Consequentially, the vertical component of the CoM is generated via a periodic function in which the resonance frequency is employed. The horizontal component of the CoM is obtained using the ZMP criterion to guarantee the dynamic balance. Having analytically generated the necessary elements of the CoM trajectory, joint motions are computed with the help of translational and angular momenta constraints. In order to validate the method, two legged jumping experiments are conducted on our actual compliant robot. In conclusion, we satisfactorily observed repetitive, continuous, and dynamically equilibrated jumping cycles with successful landing phases.
  • Humanoid Motion Optimization Via Nonlinear Dimension Reduction Authors: Kang, Hyuk; Park, Frank
    This paper examines the extent to which nonlinear dimension reduction techniques from machine learning can be exploited to determine dynamically optimal motions for high degree-of-freedom systems. Using the Gaussian Process Latent Variable Model (GPLVM) to learn the low-dimensional embedding, and a density function that provides a nonlinear mapping from the low-dimensional latent space to the full-dimensional pose space, we determine optimal motions by optimizing the latent space, and mapping the optimal trajectory in the latent space to the pose space. The notion of variance tubes are developed to ensure that kinematic constraints and other are appropriately satisfied without sacrificing naturalness or richness of the motions. Case studies of a 62-dof humanoid performing two sports motions---a golf swing and throwing a baseball---demonstrate that our method can be a highly effective, computationally efficient method for generating dynamically optimal motions.
  • A Neurorobotic Model of Bipedal Locomotion Based on Principles of Human Neuromuscular Architecture Authors: Klein, Theresa; Lewis, M. Anthony
    In this paper, we present a walking biped, based on principles of mammalian neuromuscular architecture. Walking in mammals is a fluid, dynamical interaction between a central pattern generator, the biomechanics of the body, the environment, and sensory feedback. Our robot is designed based on principles of human leg muscle architecture. We incorporate load detecting force sensors that model Golgi tendon organs in the muscles, as well as foot pressure and joint angle sensors. These sensory feedback sources model those available in the human body. The robot is controlled by a spiking neuron simulation that integrates centrally generated (CPG) with peripheral (reflexive) responses. Using recent understanding of the neurobiology of locomotion, we are able to generate an effective and stable walking pattern using interactions between the biomechanics, CPG, and reflexive responses. The CPG drives overall limb motion at the hips, while phase modulated reflexive responses adapt the pattern of the lower limb to the needs of the step cycle. Load detection by the force sensors in the limb generates propulsive stepping, and controls entrainment of the CPG through positive force feedback. These concepts are important ones for locomotion in mammals that should be considered by roboticists developing walking robots.
  • Walking Control of Fully Actuated Robots Based on the Bipedal SLIP Model Authors: Garofalo, Gianluca; Ott, Christian; Albu-Schäffer, Alin
    The goal of this paper is to generate and stabilize a periodic walking motion for a five degrees of freedom planar robot. First of all we will consider a biped version of the spring loaded inverted pendulum (SLIP), which shows openloop stable behavior. Then we will control the robot behavior as close as possible to the simple model. In this way we take advantage of the open-loop stability of the walking pattern related to the SLIP, and additional control actions are used to increase the robustness of the system and reject external disturbances. To this end an upper level controller will deal with the stabilization of the SLIP model, while a lower level controller will map the simple virtual model onto the real robot dynamics. Two different approaches are implemented for the lower level: in the first one, we aim at exactly reproducing the same acceleration that a SLIP would have when put in the same condition, while in the second one, we aim at a simpler control law without exactly reproducing the aforementioned acceleration. The latter case is equivalent to considering a SLIP with additional external disturbances, which have to be handled by the upper level controller. Both approaches can successfully reproduce a periodic walking pattern for the robot.
  • Muscle Force Transmission to Operational Space Accelerations During Elite Golf Swings Authors: Demircan, Emel; Besier, Thor F.; Khatib, Oussama
    The paper investigates the dynamic characteristics that shape human skills using the task-space methods found in robotics research. It is driven by the hypothesis that each subject's physiology can be reflected to the task dynamics using the operational space acceleration characteristics and that elite performers achieve the optimum transmission from their available muscle induced torque capacity to the desired task in goal oriented dynamic skills. The methodology is presented along with the full body human musculoskeletal model used for the task-based analyzes. The robotics approach for human motion characterization is demonstrated in the biomechanical analysis of an elite golf swing. This approach allows us to trace the acceleration capacities in a given subject's task space. The results of the motion characterization show that humans in fact follow a path of trajectory in line with the maximum available operational space accelerations benefiting from their physiology shaped by the combination of the force generating capacities of the muscles as well as by the joint and limb mechanics.