Model Predictive Impedance Control

Bednarczyk, Maciej; Omran, Hassan; Bayle, B.

doi:10.1109/icra40945.2020.9196969

Cited by 34 publications

(16 citation statements)

References 28 publications

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“…Another relevant work is [26], which presented a novel controller that deals with periodic tasks by estimating interaction forces, and by adapting feed-forward force and impedance, but also by modifying the reference trajectory to deal with rigid environments. Two very recent works are also noteworthy: [27] approaches optimal control through model predictive impedance control and [28] derives passivity conditions that provide bounds on the desired impedance gains.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Choosing Stiffness and Damping for Optimal Impedance Planning

et al. 2023

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Section: B Literature Reviewmentioning

confidence: 99%

“…However, condition ( 27) is hardly applicable in practice since it leads to an additional nonlinear constraint. A more practical, sufficient condition for decoupling, which is also a particular case of (27), is…”

Section: A Preliminariesmentioning

confidence: 99%

Choosing Stiffness and Damping for Optimal Impedance Planning

et al. 2023

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“…Aiming at this purpose, an innovative control scheme for HRC, which enforces dynamic constraints even in the presence of external forces, is designed in [77] by Kimmel and Hirche. Then, Bednarczyk et al combine impedance control (IC) and MPC in [78], since this control field is relatively new for HRC and no research has been performed yet. Thus, a new controller named model predictive impedance controller (MPIC) is presented by the authors.…”

Section: Safety-oriented Control System Designmentioning

confidence: 99%

“…In a multitude of cases, they are used in combination with other techniques, e.g., learning-based and sensor-based, and change the cost function according to the target and problem analyzed. On the one hand, optimal control is implemented to respect the new ISO industrial safety requirements by exploiting CBFs [35], [36], that overcome the limitations of both SSM and PFL [34], [54]- [56], and in the future can be combined with human gesture prediction [113] and integrated with MPC [78] to plan the best safe behavior in the long term. As it is wellknown, MPC is a high-tech control technique that solves at every time step a finite-horizon control problem with multiple constraints in multi-variable systems.…”

Section: B Emerging Control Issues and Challengesmentioning

confidence: 99%

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Proia

Carli

Cavone

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario.The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees' safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution.Note to Practitioners-The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

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“…), the transition function, defining the robot's dynamics, and several hard inequality constraints, integrating physical limits and obstacle avoidance. Despite abundant applications of optimizationbased approaches to mobile robots, the computational costs limit applicability when dealing with high-dimensional configuration spaces [4,5]. Data-driven approaches to speed up the optimization process usually come with reduced generalization abilities [3] and require prior, often costly, data acquisition.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization Fabrics for Motion Generation

Spahn¹,

Wisse²,

Alonso–Mora³

2022

Preprint

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Optimization fabrics represent a geometric approach to real-time motion planning, where trajectories are designed by the composition of several differential equations that exhibit a desired motion behavior. We generalize this framework to dynamic scenarios and prove that fundamental properties can be conserved. We show that convergence to trajectories and avoidance of moving obstacles can be guaranteed using simple construction rules of the components. Additionally, we present the first quantitative comparisons between optimization fabrics and model predictive control and show that optimization fabrics can generate similar trajectories with better scalability, and thus, much higher replanning frequency (up to 500 Hz with a 7 degrees of freedom robotic arm). Finally, we present empirical results on several robots, including a non-holonomic mobile manipulator with 10 degrees of freedom, supporting the theoretical findings.An open-source implementation and the video will be made public upon publication.

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Model Predictive Impedance Control

Cited by 34 publications

References 28 publications

Choosing Stiffness and Damping for Optimal Impedance Planning

Choosing Stiffness and Damping for Optimal Impedance Planning

Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey

Dynamic Optimization Fabrics for Motion Generation

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