Discrete-Time Formulation for Optimal Impact Control in Interaction Tasks

Roveda, Loris; Iannacci, Niccolò; Tosatti, Lorenzo Molinari

doi:10.1007/s10846-017-0683-6

Cited by 31 publications

(13 citation statements)

References 43 publications

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“…Considering model-based approaches, state of the art methods allow to estimate and control the interaction between the robot and the environment only making use of the robot dynamical model, without modeling and estimating the environment dynamics. Such modeling and estimation, however, is of great importance to design the interaction controller to avoid instabilities and to achieve required performance (Hogan 1988;Roveda et al 2018b). From the stateof-the-art review, the only sensorless approach estimating the environment compliance can be found in Dehghan et al (2015).…”

Section: Related Workmentioning

confidence: 99%

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Roveda

Piga

2021

Auton Robot

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Industrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.

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Section: Related Workmentioning

confidence: 99%

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Roveda

Piga

2021

Auton Robot

Self Cite

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“…The errors in pose estimation by the camera are due to its resolution, hence the maximum δx to be compensated by the impedance control depends on it. During the grasping phase, in the worst case scenario of maximum error in position estimate, the error after impedance compensation will be e g ss as in (16), the same applies to place position and we will have e p ss , where superscripts g and p stands for grasping and place respectively. Knowing the repeatability range rr of the camera for both the poses and assuming normal distribution, the maximum displacements to be compensated, due to linear position estimate, can be computed as δx g lin = , moreover δx p has to compensate for e g ss too, hence δx p = δx p lin + e g ss .…”

Section: Augmentioning

confidence: 99%

Precise Robotic Manipulation of Bulky Components

Franceschi¹,

Castaman

Ghidoni

et al. 2020

IEEE Access

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“…The traditional programming method in the structured environment can no longer meet the production requirements that require frequent upgrades. The programming model of the robot has changed from hard coding to teaching-playback for the rapid changes in the production line [6][7][8][9][10]. The teaching-playback method greatly reduces the workload of programming.…”

Section: Introductionmentioning

confidence: 99%

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

et al. 2021

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The method of tactile perception can accurately reflect the contact state by collecting force and torque information, but it is not sensitive to the changes in position and posture between assembly objects. The method of visual perception is very sensitive to changes in pose and posture between assembled objects, but they cannot accurately reflect the contact state, especially since the objects are occluded from each other. The robot will perceive the environment more accurately if visual and tactile perception can be combined. Therefore, this paper proposes the alignment method of combined perception for the peg-in-hole assembly with self-supervised deep reinforcement learning. The agent first observes the environment through visual sensors and then predicts the action of the alignment adjustment based on the visual feature of the contact state. Subsequently, the agent judges the contact state based on the force and torque information collected by the force/torque sensor. And the action of the alignment adjustment is selected according to the contact state and used as a visual prediction label. Whereafter, the network of visual perception performs backpropagation to correct the network weights according to the visual prediction label. Finally, the agent will have learned the alignment skill of combined perception with the increase of iterative training. The robot system is built based on CoppeliaSim for simulation training and testing. The simulation results show that the method of combined perception has higher assembly efficiency than single perception.

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Discrete-Time Formulation for Optimal Impact Control in Interaction Tasks

Cited by 31 publications

References 43 publications

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Precise Robotic Manipulation of Bulky Components

Alignment Method of Combined Perception for Peg‐in‐Hole Assembly with Deep Reinforcement Learning

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