Dynamic Modeling and Control of Deformable Linear Objects for Single-Arm and Dual-Arm Robot Manipulations

Lv, Naijing; Liu, Jianhua; Jia, Yunyi

doi:10.1109/tro.2021.3139838

Cited by 42 publications

(28 citation statements)

References 49 publications

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“…For example, Discrete Elastic Rods (DER) [1,2] has arisen as a robust and efficient algorithm for simulating flexible rods. [18] used DER as a predictive modeling tool, and achieved promising performance in DLO manipulation tasks. Though various ways to model deformable objects exists, each has their respective strengths and weaknesses and often possess a trade-off between computational efficiency and accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…They achieve a success rate of 66% but require empirical hardcoded adjustments to their controlling scheme, indicating the intuitive approach's unsuitability for extreme precision deployment. Additionally, [18] use a precise physical numerical model to predict the DLO's configuration during deployment. However, they use a trial-and-error method to exhaustively solve the optimal deployment path, which is computationally expensive and slow.…”

Section: Related Workmentioning

confidence: 99%

“…The second category in-volves robots attempting to precisely control the exact shape of the DLOs. For this task, a key challenge is formulating a mapping between the robot's motions and the shape of the manipulated DLO [18,27,40]. In this article, we look into how to design a manipulation scheme for controlling the shape of elastic rods through deployment, which involves manipulating one end of DLO in a way that graduallly lays the DLO on a substrate in a desired pattern, with superhuman accuracy, sufficient efficiency, and strong robustness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sim2Real Physically Informed Neural Controllers for Robotic Deployment of Deformable Linear Objects

Tong¹,

Choi²,

Qin³

et al. 2023

Preprint

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Deformable linear objects, such as rods, cables, and ropes, play important roles in daily life.However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task -accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physics-based simulations to develop a physically informed neural controller for deployment.We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns.In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting and tying knots.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sim2Real Physically Informed Neural Controllers for Robotic Deployment of Deformable Linear Objects

Tong¹,

Choi²,

Qin³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Controlling dual-arm manipulators is an interesting topic, which can be classified into low-level control and high-level planning, wherein addressing the low-level control for DARs remains an important focus. Unlike the recent works [1][2][3], this paper deals with the tracking and fault-tolerant control of DARs considering nonlinear viscosity and viscoelastic friction in their joints.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

Tuan,

2024

Robotica

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Closed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

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“…From the manipulation side, the DLOs intrinsic deformability results in a high-dimensional state space with complex and nonlinear dynamics. Thus, modeling and predicting their behavior during a manipulation task is challenging [3], [4].…”

Section: Introductionmentioning

confidence: 99%

RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation

Caporali

Galassi

Žagar

et al. 2023

IEEE Trans. Ind. Inf.

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Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs centerlines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time.

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Dynamic Modeling and Control of Deformable Linear Objects for Single-Arm and Dual-Arm Robot Manipulations

Cited by 42 publications

References 49 publications

Sim2Real Physically Informed Neural Controllers for Robotic Deployment of Deformable Linear Objects

Sim2Real Physically Informed Neural Controllers for Robotic Deployment of Deformable Linear Objects

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation

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