Data Fusion for Robotic Assembly Tasks Based on Human Skills

Cortesão, Rui; Koeppe, R.; Nunes, Urbano; Hirzinger, Gerd

doi:10.1109/tro.2004.832789

Cited by 30 publications

(15 citation statements)

References 20 publications

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“…For the uncertainties problem in the sensor data and also in the position of the parts is addressed in [8] to identify the contact formation in assembling. More recent researches used hidden Markov, Kalman filters with neural network, and maximization based Gaussian to model the assembling task [9,10,11]. Tele-operating was proposed [12] to execute the assembling of peg-in-hole.…”

Section: Abdullah@zessuni-siegendementioning

confidence: 99%

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Abdullah

Roth

Weyrich

et al. 2015

MATEC Web of Conferences

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Abstract. This paper presents an approach for estimating contact positions of peg-in-hole assembling application using data models created from Force/Torque sensor inputs. The assembling task is carried by an industrial robot where the F/T sensor is mounted between the robot and the gripper to measure six values of forces and torques during the contact between the peg and the hole. The search approach to estimate the current position of the contact point is implemented based on human like intuitive behavior by maneuvering the peg on the hole's edge. Different models were created and evaluated using experiments results acquired from pervious assembling attempts. Assembling steps, data analysis and models results are presented in this research.

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Section: Abdullah@zessuni-siegendementioning

confidence: 99%

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Abdullah

Roth

Weyrich

et al. 2015

MATEC Web of Conferences

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“…An Active Observer (AOB) 11,12 is a variation of a Kalman filter (KF), one of the first estimators to include disturbance in the optimization process. The AOB concept relies on adopting an extra relationship (auxiliary input) to estimate an equivalent disturbance referred to as the system input.…”

Section: The Eaobmentioning

confidence: 99%

“…[11,12], can be used to estimate the disturbance in a dynamic system, as well as where * is the output of the system, * is a unit matrix, and the state observation matrix * 0 0 0 , and * , * and * represent the process noises and measurement noises, respectively, / and * represent the rates at which the vectors of external forces and inertial robot parameters are estimated to vary.…”

Section: The Eaobmentioning

confidence: 99%

Nonlinear bilateral teleoperation using extended active observer for force estimation and disturbance suppression

et al. 2014

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A novel nonlinear teleoperation algorithm for simultaneous inertial parameters and force estimation at the master and slave sides of the teleoperation system is proposed. The scheme, called Extended Active Observer (EAOB), is an extension of the existing active observer. It provides effective force tracking at the master side with accurate position tracking at the slave side in the presence of inertial parameter variation and measurement noise. The proposed method only requires the measurement of robot position, and as a result significantly reduces the difficulty and cost of implementing bilateral teleoperation systems. The approach is described and its stability is analytically verified. The performance of the method is validated through computer simulation and compared with the Nicosia observer-based controller. According to the results, EAOB outperforms the Nicosia observer method and effectively rejects noise. Abstract: A novel nonlinear teleoperation algorithm for simultaneous inertial parameters and force estimation at both master and slave sides of the teleoperation system is proposed. The scheme, called Extended Active Observer (EAOB), is an extension of the existing active observer. It provides effective force tracking at the master side with accurate position tracking at the slave side in the presence of inertial parameter variation and measurement noise. The proposed method only requires the measurement of robot position and as a result significantly reduces the difficulty and cost of implementing bilateral teleoperation systems. The approach is described and its stability is analytically verified. The performance of the method is validated through computer simulation and compared with Nicosia observer based controller. According to the results, EAOB outperforms Nicosia-observer method and effectively rejects noise.

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“…2) Insertion task-frame: The task-frame is identified by the z extrema under constrained motion, p c3 in (4). l is the number of data points i = k, ..., k + l, inside the hole for a peg inserted to a depth, D. Deciding D and k follows from (5) and (6).…”

Section: B Data Miningmentioning

confidence: 99%

“…In one, emphasis is placed on the trajectory, relevant features from the data are obtained as time invariant observations in the demonstrations, and an imitation data set is derived as the one with the least discrepancies or most invariants [3]. The other trend, goal-directed imitation, focuses on the demonstration goal, from which, actions are derived and organised [4]- [6]. In the cited works, tasks were studied and the modalities for using the robot to repeat the task were explored using behavioural networks to learn and reinforce the relevant features observed.…”

Section: Introductionmentioning

confidence: 99%

Human demonstration data for fast task teaching

Okodi

Jiang

Konno

et al. 2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Robot operation requires a succinct interface with fewer parameters to specify. This paper presents an algorithm to interface an integrated torque control law, and human task demonstration data. The data is comprised of 6 degrees of freedom, pendant force and position data, and hand posture tracking data. The aim is to automatically specify all the required parameters for the control law, which include constrained and non-constrained trajectories, reference task frames and distal end compliance. The algorithm is embedded with functions to optimise, filter, synchronise and enhance safety.

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Data Fusion for Robotic Assembly Tasks Based on Human Skills

Cited by 30 publications

References 20 publications

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Force/Torque Data Modeling for Contact Position Estimation in Peg-in-Hole Assembling Application

Nonlinear bilateral teleoperation using extended active observer for force estimation and disturbance suppression

Human demonstration data for fast task teaching

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