A Quasi-static Model and Simulation Approach for Pushing, Grasping, and Jamming

Halm, Mathew; Posa, Michael

doi:10.1007/978-3-030-44051-0_29

Cited by 16 publications

(19 citation statements)

References 22 publications

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“…Quasistatic dynamics has been used with great success to simplify the planning and control of simple tasks such as planar pushing [20], where modeling robots as prescribed motion trajectories is sufficient. However, for multi-contact tasks such as grasping [21], such simplifications can lead to non-unique contact forces or violation of non-penetration constraints [22], [23]. By modeling robots as impedances, our recent multibody quasistatic model [24] can faithfully reproduce the steady-state behavior of stiffness-controlled robot arms in multi-contact scenarios.…”

Section: B Quasistatic Dynamics Models In Manipulationmentioning

confidence: 99%

“…The bi-lateralness of (6b) is less concerning. As contact sensors are inevitably noisy, only contact forces above a threshold are added to QP (22). In addition, by (9a), the change in contact force is bounded as long as q l+1 cmd − q l is bounded, and the boundedness of q l+1 cmd − q l is enforced by (22e).…”

Section: Qp Controller With Quasistatic Dynamics a Frictionless Contactsmentioning

confidence: 99%

“…Therefore, we will retain the simpler frictionless contact model for controlling through a frictional contact, and mitigate the side effects of the wrong contact model by modifying the frictionless QP (22) to which has the same constraints as ( 22) but a different objective. In the rest of this section, we will elaborate on the reason for both terms in the objective (24a).…”

Section: B Frictional Contactsmentioning

confidence: 99%

“…Such a W v C cmd would lead to the robot separating from the obstacle at l+1. When the frictionless QP (22) is constructed again at l+1, no contact force constraints are added but q l+2 ref can still lead the robot to contact with a large amount of penetration. Therefore, the robot could re-establish contact with a large contact force at l + 2.…”

Section: B Frictional Contactsmentioning

confidence: 99%

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Easing Reliance on Collision-free Planning with Contact-aware Control

Pang¹,

Tedrake²

2021

Preprint

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We believe that the future of robot motion planning will look very different than how it looks today: instead of complex collision avoidance trajectories with a brittle dependence on sensing and estimation of the environment, motion plans should consist of smooth, simple trajectories and be executed by robots that are not afraid of making contact. Here we present a "contact-aware" controller which continues to execute a given trajectory despite unexpected collisions while keeping the contact force stable and small. We introduce a quadratic programming (QP) formulation, which minimizes a trajectorytracking error subject to quasistatic dynamics and contact-force constraints. Compared with the classical null-space projection technique, the inequality constraint on contact forces in the proposed QP controller allows for more gentle release when the robot comes out of contact. In the quasistatic dynamics model, control actions consist only of commanded joint positions, allowing the QP controller to run on stiffness-controlled robots which do not have a straightforward torque-control interface nor accurate dynamic models. The effectiveness of the proposed QP controller is demonstrated on a KUKA iiwa arm. Project video: https://youtu.be/M-7JMQRkiPk.

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Section: B Quasistatic Dynamics Models In Manipulationmentioning

confidence: 99%

Section: Qp Controller With Quasistatic Dynamics a Frictionless Contactsmentioning

confidence: 99%

Section: B Frictional Contactsmentioning

confidence: 99%

Section: B Frictional Contactsmentioning

confidence: 99%

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Easing Reliance on Collision-free Planning with Contact-aware Control

Pang¹,

Tedrake²

2021

Preprint

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“…Considering sensorless characteristic and unstable communication condition, these works inspired us that noncommunicative cooperative control is essential for pushing manipulation of HMSR. e problem of pushing manipulation has been researched in 1999 by Rezzoug and Gorce [12], and massive documents have recorded achievements on its model establishment and controller design [13,14]. For example, Ma and Rodriguez [15] built an anisotropic friction model to better understand the variability and predictability of planar friction, and experiments were conducted for comparison with more standard isotropic friction models.…”

Section: Introductionmentioning

confidence: 99%

A Noncommunicative Memory-Pushing Fuzzy Control Strategy for Sensorless Multirobot Systems

et al. 2020

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Considering the characteristics of equipment on underground fully mechanized coal mining face, a multirobot system, which takes heavy-duty mobile support robot (HMSR) as the pushing robot and middle trough (MT) as the manipulated object, is established. To overcome the problem of unstable communication and potential pressure loss, a memory-pushing fuzzy control strategy is proposed to achieve better practical performance without human-guided operations. The pushing dynamics without communication is derived to proof the convergence of the dynamic system, and the time-based memory-pushing fuzzy model is built for compensating the potential pressure loss. Finally, the proposed control strategy is simulated in virtual environment, which integrates our pushing dynamics, and an industrial experiment is demonstrated as well. Both the simulation and industrial experiments show the efficiency and feasibility of the proposed method.

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