Impact-Friendly Robust Control Design with Task-Space Quadratic Optimization

Wang, Yuquan; Kheddar, Abderrahmane

doi:10.15607/rss.2019.xv.032

Cited by 27 publications

(26 citation statements)

References 14 publications

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“…In [24], an impact model has been defined to numerically compute the impact control gains and the reference impact velocity to establish a stable and controlled contact with the target environment, reducing the impact force. A task-space controller has been proposed in [25]. Quadratic programming (QP) has been exploited to generate modular and reactive motions for a wide variety of tasks.…”

Section: ) Impact Controlmentioning

confidence: 99%

Sensorless Optimal Switching Impact/Force Controller

et al. 2021

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Intelligent interaction control is required in many fields of application, in which different operative situations have to be faced with different controllers. Being able to switch between optimized controllers is, indeed, of extreme importance to maximize the task performance in the different operative conditions (i.e., free-space motion and contact), especially when considering sensorless robots. To deal with the proposed context, a sensorless optimal switching impact/force (OSIF) controller is proposed. The low-level robot control is composed of an inner joint position controller, fed by an outer Cartesian impedance controller with a reference position. The estimation of the external wrench is implemented by means of an Extended Kalman Filter (EKF). The high-level controller (feeding the Cartesian impedance controller with the setpoint) is composed of an optimized impact controller (LQR-based controller), an optimized force controller (SDREbased controller), and a continuous switching mechanism (Fuzzy Logic-based). In addition, the output of the switching mechanism is used to adapt the Cartesian impedance control parameters (i.e., stiffness and damping parameters). Experimental tests have been performed on a Franka EMIKA panda robot to validate the proposed controller. Obtained results show the capabilities of the OSIF controller, being able to detect task phase transitions while satisfying the target performance.

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Section: ) Impact Controlmentioning

confidence: 99%

Sensorless Optimal Switching Impact/Force Controller

et al. 2021

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“…A popular controller in locomotion and manipulation is based on the following optimization-based formulation [10], [11], [27]: min.…”

Section: A Constrained Inverse Dynamics Controlmentioning

confidence: 99%

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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“…3 * Alessandro Saccon (corresponding author) is with the Department of Mechanical Engineering, Eindhoven University of Technology (TU/e), The Netherlands (a.saccon@tue.nl) to collisions which cause rapid changes in the system velocities and short-lived post-impact vibrations. To achieve complex dynamic contact tasks, in parallel to adequate compliant mechanical design, it is necessary to develop a coherent framework that encompasses control, learning, planning, and sensing strategies in the presence of impacts and sudden velocity jumps [5], [6]. These methods assume to have at hand reliable forecast of the effects of impact phenomena to be able to cope with expected (and unexpected) impacts and ensure stability and desired level of performance while executing impact tasks.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Post-Impact Velocity of a Robotic Arm via Rigid Multibody Models: an Experimental Study

Ilias

Padois

Saccon

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at nonzero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impactaware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field.

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Impact-Friendly Robust Control Design with Task-Space Quadratic Optimization

Cited by 27 publications

References 14 publications

Sensorless Optimal Switching Impact/Force Controller

Sensorless Optimal Switching Impact/Force Controller

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

Predicting the Post-Impact Velocity of a Robotic Arm via Rigid Multibody Models: an Experimental Study

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