Robotic Cutting: Mechanics and Control of Knife Motion

Mu, Xiaoqian; Xue, Yuechuan; Jia, Yan-Bin

doi:10.1109/icra.2019.8793880

Cited by 42 publications

(19 citation statements)

References 17 publications

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“…A comprehensive treatment of the mechanics involved in cutting of metals, biomaterials and non-metals is given in Atkins and Atkins [2]. Analytical models of the forces acting on a knife as it cuts through soft materials have been derived in [15,63].…”

Section: Related Work a Modeling And Simulation Of Cuttingmentioning

confidence: 99%

“…Such trained policies are then transferred to a physical robotic surgery system. By studying the mechanics of cutting, [63] derive control strategies involving pressing, pushing, and slicing motions. Similarly, [15,16] analyze cutting "by pressing and slicing," while taking into account the blade sharpness of the knife.…”

Section: Related Work a Modeling And Simulation Of Cuttingmentioning

confidence: 99%

See 1 more Smart Citation

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Heiden

Macklin

Narang

et al. 2021

Robotics: Science and Systems XVII

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Robotic cutting of soft materials is critical for applications such as food processing, household automation, and surgical manipulation. As in other areas of robotics, simulators can facilitate controller verification, policy learning, and dataset generation. Moreover, differentiable simulators can enable gradient-based optimization, which is invaluable for calibrating simulation parameters and optimizing controllers. In this work, we present DiSECt: the first differentiable simulator for cutting soft materials. The simulator augments the finite element method (FEM) with a continuous contact model based on signed distance fields (SDF), as well as a continuous damage model that inserts springs on opposite sides of the cutting plane and allows them to weaken until zero stiffness, enabling crack formation. Through various experiments, we evaluate the performance of the simulator. We first show that the simulator can be calibrated to match resultant forces and deformation fields from a state-of-the-art commercial solver and real-world cutting datasets, with generality across cutting velocities and object instances. We then show that Bayesian inference can be performed efficiently by leveraging the differentiability of the simulator, estimating posteriors over hundreds of parameters in a fraction of the time of derivativefree methods. Finally, we illustrate that control parameters in the simulation can be optimized to minimize cutting forces via lateral slicing motions. We publish videos and additional results on our project website at https://diff-cutting-sim.github.io.

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Section: Related Work a Modeling And Simulation Of Cuttingmentioning

confidence: 99%

Section: Related Work a Modeling And Simulation Of Cuttingmentioning

confidence: 99%

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Heiden

Macklin

Narang

et al. 2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

show abstract

“…There are control strategies for exerting forces along a path of a tool that rely on classical notions of impedance control, force control or hybrid position/force control (Matsuzaki et al, 2013;Asada and Asari, 1988;Mu et al, 2019). However, these methods do not factor in the grasp on the object.…”

Section: Task-constrained Motionmentioning

confidence: 99%

Force-and-Motion Constrained Planning for Tool Use

Holladay

Lozano-Pérez

Rodríguez

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The use of hand tools presents a challenge for robot manipulation in part because it calls for motions requiring continuous force application over a whole trajectory, usually involving large joint-angle excursions. The feasible application of a tool, such as pulling a nail with a hammer claw, requires careful coordination of the choice of grasp and joint trajectories to ensure kinematic and force limits are not exceededin the grasp as well as the robot mechanism. In this thesis, we formulate this type of problem as choosing the values of decision variables in the presence of various constraints. We evaluate the impact of the various constraints in some representative instances of tool use. To aid others in further investigating this class of problems, we have released materials such as printable tool models and experimental data. We hope that these can serve as the basis of a benchmark problem for investigating tasks that involve many kinematic, actuation, friction, and environment constraints.

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“…A particularly representative deformable object manipulation task involves food cutting, for example fruits or cheese. This process has time-varying nonlinear dynamics that are extremely difficult to describe analytically [13] (although attempts have been made [3], [14], [15]). As a result, learning-based techniques have been proposed to address this challenge.…”

Section: Related Workmentioning

confidence: 99%

“…The design of effective learning and adaptive control strategies for precision cutting remains an open problem in robotics [1], [2], [3]. This is particularly true for the case where cutting involves moving between two mediums, and where there is uncertainty in the location of these.…”

Section: Introductionmentioning

confidence: 99%

Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification

Straižys¹,

Burke²,

Ramamoorthy³

2019

Preprint

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Precision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we consider the challenge of cutting along the boundary between two soft mediums, a problem that is made extremely difficult due to visibility constraints, which means that the precise location of the cutting trajectory is typically unknown. This paper introduces a novel strategy to address this task, using a binary medium classifier trained using joint torque measurements, and a closed loop control law that relies on an error signal compactly encoded in the decision boundary of the classifier. We illustrate this on a grapefruit cutting task, successfully modulating a nominal trajectory fit using dynamic movement primitives to follow the boundary between grapefruit pulp and peel using torque based medium classification. Results show that this control strategy is successful in 72 % of attempts in contrast to control using a nominal trajectory, which only succeeds in 50 % of attempts.

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Robotic Cutting: Mechanics and Control of Knife Motion

Cited by 42 publications

References 17 publications

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting

Force-and-Motion Constrained Planning for Tool Use

Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification

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