Context-aware robotic arm using fast embedded model predictive control

Trimble, Shane; Naeem, Wasif; McLoone, Séamus; Sopasakis, Pantelis

doi:10.1109/issc49989.2020.9180217

Cited by 2 publications

(1 citation statement)

References 35 publications

(41 reference statements)

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“…In our case, we require an accurate prediction of the movement of the arm for given actuator torque inputs over a given finite time window ahead. In robotic MPC applications, this window is typically between 100 milliseconds (e.g., Zhao et al, 2023) and 1 second (e.g., Trimble et al, 2020). An overview of the connection between MPC and system identification for our use case is presented in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Learning-based augmentation of physics-based models: an industrial robot use case

Retzler,

Tóth,

Schoukens

et al. 2024

DCE

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In a Model Predictive Control (MPC) setting, the precise simulation of the behavior of the system over a finite time window is essential. This application-oriented benchmark study focuses on a robot arm that exhibits various nonlinear behaviors. For this arm, we have a physics-based model with approximate parameter values and an open benchmark dataset for system identification. However, the long-term simulation of this model quickly diverges from the actual arm’s measurements, indicating its inaccuracy. We compare the accuracy of black-box and purely physics-based approaches with several physics-informed approaches. These involve different combinations of a neural network’s output with information from the physics-based model or feeding the physics-based model’s information into the neural network. One of the physics-informed model structures can improve accuracy over a fully black-box model.

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Section: Introductionmentioning

confidence: 99%

Learning-based augmentation of physics-based models: an industrial robot use case

Retzler,

Tóth,

Schoukens

et al. 2024

DCE

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An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

Lei

Lü

Laurenzi

et al. 2022

2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)

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Modular reconfigurable manipulators enable quick adaptation and versatility to address different application environments and tailor to the specific requirements of the tasks. Task performance significantly depends on the manipulator's mounted pose and morphology design, therefore posing the need of methodologies for selecting suitable modular robot configurations and mounted pose that can address the specific task requirements and required performance. Morphological changes in modular robots can be derived through a discrete optimization process involving the selective addition or removal of modules. In contrast, the adjustment of the mounted pose operates within a continuous space, allowing for smooth and precise alterations in both orientation and position. This work introduces a computational framework that simultaneously optimizes modular manipulators' mounted pose and morphology. The core of the work is that we design a mapping function that implicitly captures the morphological state of manipulators in the continuous space. This transformation function unifies the optimization of mounted pose and morphology within a continuous space. Furthermore, our optimization framework incorporates a array of performance metrics, such as minimum joint effort and maximum manipulability, and considerations for trajectory execution error and physical and safety constraints. To highlight our method's benefits, we compare it with previous methods that framed such problem as a combinatorial optimization problem and demonstrate its practicality in selecting the modular robot configuration for executing a drilling task with the CONCERT modular robotic platform.

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Context-aware robotic arm using fast embedded model predictive control

Cited by 2 publications

References 35 publications

Learning-based augmentation of physics-based models: an industrial robot use case

Learning-based augmentation of physics-based models: an industrial robot use case

An MPC-Based Framework for Dynamic Trajectory Re-Planning in Uncertain Environments

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