Sensorless friction-compensated passive lead-through programming for industrial robots

Stolt, Andreas; Carlson, Fredrik Bagge; Ardakani, M. Mahdi Ghazaei; Lundberg, Ivan; Robertsson, Anders; Johansson, Rolf

doi:10.1109/iros.2015.7353870

Cited by 18 publications

(18 citation statements)

References 13 publications

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“…The importance of the dithering torque at zerovelocity has been illustrated. Secondly, we present an example illustrating the functionality of the active LTP proposed in this article, and finally a comparison between our active LTP and a passive LTP [8] is presented.…”

Section: Resultsmentioning

confidence: 99%

“…The friction compensation torque has been calculated similarly to [8], based on the friction model presented in Sec. II-B.…”

Section: B Friction Compensationmentioning

confidence: 99%

“…In general, the interplay between the force estimation and force control may cause instabilities in stiff contact situations. Therefore, to allow a stable interaction with highly stiff materials, a passive approach (no feedback of interaction force) has been proposed [8]. In this method, the robot is gravity and partially friction compensated.…”

Section: Introductionmentioning

confidence: 99%

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Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control

Capurso

Ardakani

Johansson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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In modern day industry, robots are indispensable for achieving high production rates and competitiveness. In small and medium scale enterprises, where the production may shift rapidly, it is vital to be able to reprogram robots quickly. Kinesthetic teaching, also known as lead-through programming (LTP), provides a fast approach for teaching a trajectory. In this approach, a trajectory is demonstrated by physical interaction with the robot, i.e., the user manually guides the manipulator. This paper presents a sensorless approach to LTP for redundant robots that eliminates the need for expensive force/torque sensors. The active implementation enhances the passive LTP by an admittance control in joint space based on the external forces applied by the user, estimated with a Kalman filter using the generalized momentum formulation. To improve the quality of the estimation and hence LTP, we use a dithering technique. The active LTP has been implemented on ABB YuMi robot and experimental comparison with an earlier passive LTP is presented.

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Section: Resultsmentioning

confidence: 99%

“…The friction compensation torque has been calculated similarly to [8], based on the friction model presented in Sec. II-B.…”

Section: B Friction Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control

Capurso

Ardakani

Johansson

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

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“…In [14] a control strategy was proposed without joint friction model identification, although a feed-forward term for partial friction compensation could be added. It was noticed that with such a method the force required to move larger robots becomes too large for human operators, thus the approach is not general.…”

Section: A Related Workmentioning

confidence: 99%

General Hand Guidance Framework using Microsoft HoloLens

Puljiz

Stöhr

Hein

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Hand guidance emerged from the safety requirements for collaborative robots, namely possessing joint-torque sensors. Since then it has proven to be a powerful tool for easy trajectory programming, allowing lay-users to reprogram robots intuitively. Going beyond, a robot can learn tasks by user demonstrations through kinesthetic teaching, enabling robots to generalise tasks and further reducing the need for reprogramming. However, hand guidance is still mostly relegated to collaborative robots. Here we propose a method that doesn't require any sensors on the robot or in the robot cell, by using a Microsoft HoloLens augmented reality head mounted display. We reference the robot using a registration algorithm to match the robot model to the spatial mesh. The in-built hand tracking and localisation capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations and propagating it down through the kinematic chain, we achieve a generalised hand guidance without the need to build a dynamic model of the robot itself. We tested our approach on a commonly used industrial manipulator, the KUKA KR-5.

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“…More recent works proposed improvements on joint torque and external forces estimation, while the joints were not moving [3], or were moving at very low velocities [4]. The presented methods accounted for the significant torque disturbances due to static, Coulomb and viscous friction, modeling them either as a deterministic affine function [5], either as a uniformly distributed noise, with a range (the Coulomb "friction band") dependent on the velocity, combined to a Gaussian noise of zero mean, with a variance increasing with joint velocity [4]. In both cases, the respective parameters were identified manually and few details were given on the procedure.…”

Section: Introductionmentioning

confidence: 99%

Identification of Motor Parameters on Coupled Joints

Guedelha

Traversaro

Pucci

2019

Advances in Intelligent Systems and Computing

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The estimation of the motor torque and friction parameters are crucial for implementing an efficient low level joint torque control. In a set of coupled joints, the actuators torques are mapped to the output joint torques through a coupling matrix, such that the motor torque and friction parameters appear entangled from the point of view of the joints. As a result, their identification is problematic when using the same methodology as for single joints. This paper proposes an identification method with an improved accuracy with respect to classical closed loop methods on coupled joints. The method stands out through the following key points: it is a direct open loop identification; it addresses separately each motor in the coupling; it accounts for the static friction in the actuation elements. The identified parameters should significantly improve the contribution of the feed-forward terms in the low level control of coupled joints with static friction.

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Sensorless friction-compensated passive lead-through programming for industrial robots

Cited by 18 publications

References 13 publications

Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control

Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control

General Hand Guidance Framework using Microsoft HoloLens

Identification of Motor Parameters on Coupled Joints

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