On-line rigid object recognition and pose estimation based on inertial parameters

Kubus, Daniel; Kröger, Torsten; Wahl, Friedrich M.

doi:10.1109/iros.2007.4399184

Cited by 38 publications

(41 citation statements)

References 10 publications

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“…The end-effector F/T measurements were processed with a notch filter (cut frequency 186.5 Hz, attenuation -50dB) to cancel out the noise derived from the robot fans vibration. Also, the dynamics of the linear tool was online compensated, 31 resulting in a 0.02 N accuracy of the force measurements at the end-effector.…”

Section: Experimental Protocolmentioning

confidence: 99%

Nonlinear Force Feedback Enhancement for Cooperative Robotic Neurosurgery Enforces Virtual Boundaries on Cortex Surface

Beretta

Ferrigno

Momi

2016

J. Med. Robot. Res.

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Surgeons can benefit from the cooperation with a robotic assistant during the repetitive execution of precise targeting tasks on soft tissues, such as brain cortex stimulation procedures in open-skull neurosurgery. Position-based force-to-motion control schemes may not be satisfactory solution to provide the manipulator with the high compliance desirable during guidance along wide trajectories. A new torque controller with non-linear force feedback enhancement (FFE) is presented to provide augmented haptic perception to the operator from instrument-tissue interaction. Simulation tests were performed to evaluate the system stability according to different non-linear force modulation functions (power, sigmoidal and arc tangent). The FFE controller with power modulation was experimentally validated with a pool of non-expert users using brain-mimicking gelatin phantoms (8%-16% concentration). Besides providing hand tremor rejection for a stable holding of the tool, the FFE controller was proven to allow for a safer tissue contact with respect to both robotic assistance without force feedback and freehand executions (50% and 75% reduction of the indentation depth, respectively). Future work will address the evaluation of the safety features of the FFE controller with expert surgeons on a realistic brain phantom, also accounting for unpredictable tissue motions as during seizures due to cortex stimulation.

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Section: Experimental Protocolmentioning

confidence: 99%

Nonlinear Force Feedback Enhancement for Cooperative Robotic Neurosurgery Enforces Virtual Boundaries on Cortex Surface

Beretta

Ferrigno

Momi

2016

J. Med. Robot. Res.

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“…Atkeson et al have proposed a method for estimating inertial parameters of a grasped object based on force/torque measurements [12], and Kubus et al have used sensor fusion combining measurements of acceleration, velocities, position, forces, and torques to estimate inertial parameters and principal moments of inertia of a grasped object, fitting parameters to estimate object pose [13]. Both these approaches require free motion in a prespecified trajectory, and can not be applied to estimate the contact point of a tool online as it is being used.…”

Section: Related Workmentioning

confidence: 99%

“…In this way, we can perform a meaningful task and simultaneously generate signals e f andṗ e . In particular, the motion along the surface not only generatesṗ e that span the orthogonal complement of the normal direction required in (13) but gives rise to tangential forces owing to dynamical friction that can be added to the normal interaction forces, see (5), in order to generate an appropriate signal e f to excite (9) by spanning a surface in the Cartesian space.…”

Section: Force/motion Controlmentioning

confidence: 99%

Online contact point estimation for uncalibrated tool use

Karayiannidis

Smith

Vina

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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One of the big challenges for robots working outside of traditional industrial settings is the ability to robustly and flexibly grasp and manipulate tools for various tasks. When a tool is interacting with another object during task execution, several problems arise: a tool can be partially or completely occluded from the robot's view, it can slip or shift in the robot's hand -thus, the robot may lose the information about the exact position of the tool in the hand. Thus, there is a need for online calibration and/or recalibration of the tool. In this paper, we present a model-free online tool-tip calibration method that uses force/torque measurements and an adaptive estimation scheme to estimate the point of contact between a tool and the environment. An adaptive force control component guarantees that interaction forces are limited even before the contact point estimate has converged. We also show how to simultaneously estimate the location and normal direction of the surface being touched by the tool-tip as the contact point is estimated. The stability of the the overall scheme and the convergence of the estimated parameters are theoretically proven and the performance is evaluated in experiments on a real robot.

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“…However, inertial parameters may also serve as recognition features. Moreover, inertial parameters can be used to estimate gripping poses of manipulated objects [4]. Since suitable CAD data is rarely provided for complex tools or gripped objects in robotics applications, the inertial parameters of the load have to be estimated experimentally.…”

Section: Introductionmentioning

confidence: 99%

“…These approaches did neither consider time-efficient on-line estimation nor did they incorporate sensor offset handling. The on-line estimation approach in [4] and [3] employed a recursive instrumental variables method -effectively combining acceleration signals from different sources -and considered sensor offsets. Nevertheless, all these approaches have the same shortcoming: the employed estimation approach is based on least-squares (LS) techniques, thus ignoring errors in the data matrix, i.e., noise and disturbance affecting acceleration and angular velocity signals.…”

Section: Introductionmentioning

confidence: 99%

On-line estimation of inertial parameters using a recursive total least-squares approach

Kubus

Kröger

Wahl

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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The estimation of the ten inertial parameters of rigid loads, which are attached to manipulators, may benefit several robotics applications, e.g.: force control, object recognition, and pose estimation. These applications require sufficiently accurate, robust, and fast estimation of the inertial parameters. Existing approaches, however, do not allow for robust on-line estimation, since they use standard batch leastsquares techniques, which ignore noise in the data matrix. The proposed approach, however, estimates the inertial parameters on-line and very fast (approx. 1.5s), while explicitly considering noise in the data matrix by a total least-squares approach. Apart from estimation equations and estimation approaches, the design of estimation trajectories is addressed in this paper. The performance of the proposed estimation approach is compared with the recursive ordinary least-squares (RLS) and the recursive instrumental variables (RIV) method. Experimental results clearly recommend the proposed recursive total leastsquares approach (RTLS).

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On-line rigid object recognition and pose estimation based on inertial parameters

Cited by 38 publications

References 10 publications

Nonlinear Force Feedback Enhancement for Cooperative Robotic Neurosurgery Enforces Virtual Boundaries on Cortex Surface

Nonlinear Force Feedback Enhancement for Cooperative Robotic Neurosurgery Enforces Virtual Boundaries on Cortex Surface

Online contact point estimation for uncalibrated tool use

On-line estimation of inertial parameters using a recursive total least-squares approach

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