Counteracting modeling errors for sensitive observer-based manipulator collision detection

Sotoudehnejad, Vahid; Takhmar, Amir; Kermani, Mehrdad R.; Polushin, Ilia G.

doi:10.1109/iros.2012.6386198

Cited by 25 publications

(12 citation statements)

References 17 publications

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“…Haddadin et al (2008) and Li et al (2019) proposed to use two observers, one to detect slow or soft collisions using low-pass filtered collision torques and one to detect fast collisions using band-pass filtered collision torques. Sotoudehnejad et al (2012) proposed using time-variant thresholds that take into account uncertainties in inertial parameters of the robot as well as friction parameters. To estimate uncertainties authors proposed to conduct time-consuming experiments with a robot applying various collision torque at different states.…”

Section: Collision Detectionmentioning

confidence: 99%

Practical Aspects of Model-Based Collision Detection

Mamedov

Mikhel

2020

Front. Robot. AI

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Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities.

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Section: Collision Detectionmentioning

confidence: 99%

Practical Aspects of Model-Based Collision Detection

Mamedov

Mikhel

2020

Front. Robot. AI

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“…In order to reduce the influence of friction on the virtual sensor, this paper improves the sensitivity of the torque observer by identifying and compensating the joint friction [20,21]. Kennedy et al pointed out that the Stribeck friction model is suitable for describing the conventional industrial robot joint friction [22], and its basic expression can be expressed as Ffalse(trueq˙false)=Fcsgnfalse(trueq˙false)+Fse−false(trueq˙/q˙sfalse)σsgnfalse(trueq˙false)+Fvtrueq˙, where Fs is the Stribeck parameter, q˙s is the Stribeck velocity, Fc is the Coulomb friction coefficient, Fcsgnfalse(trueq˙false) is the Coulomb friction, Fv is the viscous friction coefficient, Fvtrueq˙ is the viscous friction, and σ is the constant associated with the contact surface geometry.…”

Section: Friction Identification and Compensationmentioning

confidence: 99%

A Virtual Sensor for Collision Detection and Distinction with Conventional Industrial Robots

2019

Sensors

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Physical contact inevitably occurs during robot interaction with outside environments. A robot should have the ability to detect and distinguish whether a physical interaction between a human and the robot is contact or collision, so as to ensure human safety and improve interaction performance. In this paper, a virtual sensor that can detect and distinguish contact and collision between humans and industrial robots is proposed. Based on the generalized momentum of the robot, two observers with low-pass and band-pass filter characteristics were designed in this virtual sensor to realize the robot collision detection. Using the different frequency distribution ranges of the lighter contact force signal and the heavier collision force signal, the filter parameters in the two observers were appropriately selected to distinguish between collisions and contacts in close interaction between humans and robots. The virtual sensor does not require acceleration information or inverse dynamics calculations. It only needs to sample the motor driving current and position information of the robot joint, and can easily be applied to conventional industrial robots. The experimental results show that the low-pass and band-pass torque observers can detect different force signals in real-time, and the proposed virtual sensor can be used for collision detection and distinction in human–robot interactions.

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“…Imposing a threshold serves to reduce false positives, but it can also delay detection for contacts with small forces. If the thresholding approach is not sufficient, we can apply model-based adaptive thresholding [30] or learning based methods [31]. Let Xt denote the current set of particles {r,...,rm]}, and Xinit be fixed set of particles which are evenly sampled from the surface of the robot.…”

Section: Contact Particle Filtermentioning

confidence: 99%

Localizing external contact using proprioceptive sensors: The Contact Particle Filter

Manuelli

Tedrake

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In order for robots to interact safely and intelligently with their environment they must be able to reliably estimate and localize external contacts. This paper introduces the CPF, the Contact Particle Filter, which is a general algorithm for detecting and localizing external contacts on rigid body robots without the need for external sensing. The CPF finds external contact points that best explain the observed external joint torque, and returns sensible estimates even when the external torque measurement is corrupted with noise. We demonstrate the capability of the CPF in multiple scenarios. We show how it can track multiple external contacts on a simulated Atlas robot, and also perform extensive simulation and hardware experiments on a Kuka iiwa robot arm.

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Counteracting modeling errors for sensitive observer-based manipulator collision detection

Cited by 25 publications

References 17 publications

Practical Aspects of Model-Based Collision Detection

Practical Aspects of Model-Based Collision Detection

A Virtual Sensor for Collision Detection and Distinction with Conventional Industrial Robots

Localizing external contact using proprioceptive sensors: The Contact Particle Filter

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