Real-Time External Contact Force Estimation and Localization for Collaborative Robot

Popov, Dmitry; Klimchik, Alexandr

doi:10.1109/icmech.2019.8722893

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…To make a fair comparison, the single contact situation is considered. The first algorithm is a proposed modification of particle filter on the graph (PFG) with 50 particles, the second one is a two-step optimization (TS) algorithm from [17], the third one is a one-layer feed-forward neural network with 100 neurons in a hidden layer (NN) and the fourth one is Direct-based optimization (DR) from [11]. The first two approaches use information about the robot surface and find a force inside a friction cone, the rest two approaches use a cylindrical approximation of a robot shape and assume an external force normal to the link.…”

Section: Contact Localization Resultsmentioning

confidence: 99%

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Multi-Scenario Contacts Handling for Collaborative Robots Applications

Popov

Mikhel

Yagfarov

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The goal of this work is to propose a way of dealing with physical interactions for collaborative robots that will ensure the safety of a human operator and improve the performance of a common task by implementing multiple robot behavior scenarios. In this scope, all collisions of a robotic arm are detected and analyzed to chooses an appropriate reaction strategy. The points of contact on the robot's surface for each collision are estimated, the external forces are identified and collisions are classified by the set of predefined categories. Based on these categories and the current robot state, the algorithm chose an appropriate behavior scenario.All presented algorithms are based only on proprioceptive sensors information and were tested in a simulated environment and on the real collaborative robots KUKA iiwa and Universal Robots UR10e. The result for contact localization showed 4 cm mean accuracy, the classification algorithm was able to identify collisions with hard and soft objects with 98% accuracy for KUKA iiwa 14.

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Section: Contact Localization Resultsmentioning

confidence: 99%

“…In both works, the authors examined forces acting to the normal of the robot surface constrained with a friction cone, which allowed to create more particles and decrease the computational load. In one of our previous works [17] the contact isolation was done using two-step optimization on the mesh surface of the robot KUKA iiwa 14.…”

Section: B Localizationmentioning

confidence: 99%

Multi-Scenario Contacts Handling for Collaborative Robots Applications

Popov

Mikhel

Yagfarov

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Compared to algorithms based on a particle filter [8], [10], our approach shows better runtime frequency since we do not spend time on resampling and projecting points on surface steps. The absence of a numerical optimization procedure inside like Direct in [6], [7], or QP in [11], [8], [10] also helps to improve run-time.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In one of our previous works [11] the contact isolation was done using two-step optimization on the mesh surface of the robot KUKA iiwa 14. In another work [12], we used a neural network trained to find a contact location in simulation and then transferred this network to the real robot.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Estimation of Multiple Potential Contact Locations and Forces

Popov

Klimchik

Pashkevich

2021

IEEE Robot. Autom. Lett.

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In this work, we propose a contact point localization and external force estimation algorithm for collaborative robots. In comparison with existing approaches, the proposed algorithm can detect and evaluate multiple solutions and more than 3x faster without loss of accuracy. To achieve real-time performance, a hierarchical robot representation and surface mesh preprocessing was used, allowing us to achieve a 50x speedup in a run-time, compared to checking all contact points. Mesh preprocessing includes two-step clustering in the space of vertices normals vectors and vertices positions. The localization method was tested in a simulated Kinova Jaco 2 and real KUKA iiwa LBR 14 collaborative robots. Our solution allows estimating the contact point on the robot surface with 2.3 cm average accuracy in a more than 600 Hz loop.

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“…Based on dynamics model of the robot manipulator, the external forces can be estimated by subtracting the inertial, Coriolis, gravity and friction terms from the input motor torque (Villagrossi et al , 2018; Popov and Klimchik, 2019; Wahrburg et al , 2014). In fact, model errors are inevitable, and the signal-to-noise ratio of the acceleration signals can also reduce the estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

External force estimation for robot manipulator based on a LuGre-linear-hybrid friction model and an improved square root cubature Kalman filter

Wang

Chen

Zhang

et al. 2022

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Purpose The sensorless external force estimation of robot manipulator can be helpful for reducing the cost and complexity of the robot system. However, the complex friction phenomenon of the robot joint and uncertainty of robot model and signal noise significantly decrease the estimation accuracy. This study aims to investigate the friction modeling and the noise rejection of the external force estimation. Design/methodology/approach A LuGre-linear-hybrid (LuGre-L) friction model that combines the dynamic friction characteristics of the robot joint and static friction of the drive motor is proposed to improve the modeling accuracy of robot friction. The square root cubature Kalman filter (SCKF) is improved by integrating a Sage Window outer layer and a nonlinear disturbance observer (NDOB) inner layer. In the outer layer, Sage Window is integrated in the square root Kalman filter (W-SCKF) to dynamically adjust noise statistics. NDOB is applied as the inner layer of W-SCKF (NDOB-WSCKF) to obtain the uncertain state variables of the state model. Findings A peg-in-hole contact experiment conducted on a real robot demonstrates that the average accuracy of the estimated joint torque based on LuGre-L is improved by 4.9% in contrast to the LuGre model. Based on the proposed NDOB-WSCKF, the average estimation accuracy of the external joint torque can reach up to 92.1%, which is improved by 4%–15.3% in contrast to other estimation methods (SCKF and NDOB). Originality/value A LuGre-L friction model is proposed to handle the coupling of static and dynamic friction characteristics for the robot manipulator. An improved SCKF is applied to estimate the external force of the robot manipulator. To improve the noise rejection ability of the estimation method and make it more resistant to unmodeled state variable, SCKF is improved by integrating a Sage Window and NDOB, and a NDOB-WSCKF external force estimator is developed. Validation results demonstrate that the accuracy of the robot dynamics model and the estimated external force is improved by the proposed method.

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Real-Time External Contact Force Estimation and Localization for Collaborative Robot

Cited by 20 publications

References 16 publications

Multi-Scenario Contacts Handling for Collaborative Robots Applications

Multi-Scenario Contacts Handling for Collaborative Robots Applications

Real-Time Estimation of Multiple Potential Contact Locations and Forces

External force estimation for robot manipulator based on a LuGre-linear-hybrid friction model and an improved square root cubature Kalman filter

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