Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Minniti, Maria Vittoria; Farshidian, Farbod; Grandia, Ruben; Hutter, Marco

doi:10.1109/lra.2019.2927955

Cited by 79 publications

(35 citation statements)

References 24 publications

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“…As studied in [29,30], the assembly performance is evaluated by the weighted mean square of assembly deviation δw, which is defined as (20) where Q ∈ R 6×6 is a weighted coefficient matrix which represents the influence of different deviation items on the quality loss. The larger the coefficient related to the assembly deviation is, the more attention is paid to the accuracy index in assembly.…”

Section: Sparse Optimization For Dimensional Adjustment a Problem Formulation Of Dimensional Adjustmentmentioning

confidence: 99%

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Guo

et al. 2021

IEEE Access

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The assembly accuracy of the extendible support structure is of importance for the imaging capability of synthetic aperture radar antennas. In general, due to manufacturing imperfections and installation variations, its assembly accuracy will be inevitably degraded. Therefore, controlling the assembly deviation is highly concerned in practice. To meet the accuracy requirement and make "the control" more efficient, this study proposes a novel method to quantitatively conduct dimensional adjustment of links for extendible support structures of synthetic aperture radar antennas. Since the extendible support structure is generally over-constrained in the deployed configuration, the relationship between the assembly deviation and the variation sources is first derived by means of equivalent transformation. Based on the mathematical expression of assembly deviation, an inequality constrained sparse optimization model for quantitatively resizing links is formulated. Then, an efficient algorithm integrating the alternating direction method of multipliers and binary search is developed to solve the above optimization model, thereby acquiring the optimal combination of link adjustment. Finally, numerical case studies are carried out to demonstrate the effectiveness of the proposed method in Matlab, which show that it can not only achieve satisfactory performance in prediction but also significantly improve the assembly efficiency.

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Section: Sparse Optimization For Dimensional Adjustment a Problem Formulation Of Dimensional Adjustmentmentioning

confidence: 99%

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Guo

et al. 2021

IEEE Access

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“…Those works showed promising results both in simulation and in hardware experiments for fixed based robots. For mobile robots, interaction force control results have been presented using various techniques such as task-space impedance control [18] and dynamic model predictive control [19]. Those approaches require torque-controllable actuators that are not available in most high-payload robots.…”

Section: A Related Work 1) Sequential Linear Quadratic Model Predictmentioning

confidence: 99%

Perceptive Model Predictive Control for Continuous Mobile Manipulation

Pankert

Hutter

2020

IEEE Robot. Autom. Lett.

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A mobile robot needs to be aware of its environment to interact with it safely. We propose a receding horizon control scheme for mobile manipulators that tracks task space reference trajectories. It uses visual information to avoid obstacles and haptic sensing to control interaction forces. Additional constraints for mechanical stability and joint limits are met. The proposed method is faster than state of the art sampling based planners, available as opensource and can be implemented on a broad class of robots. We validate the method both in simulation and through extensive hardware experiments with a multitude of mobile manipulation platforms. The resulting software package is released with this paper.

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“…Efficiently controlling a mobile manipulator is an important yet open question, especially in unstructured dynamic environments. In Reference [14], the authors present a whole-body optimal control framework to jointly solve the problems of manipulation, and the optimization is performed using a Model Predictive Control (MPC) approach. The approach is tested in end-effector pose tracking and door opening tasks.…”

Section: Mobile Manipulationmentioning

confidence: 99%

Learning Mobile Manipulation through Deep Reinforcement Learning

Wang

Zhang

Tian

et al. 2020

Sensors

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Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.

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Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Cited by 79 publications

References 24 publications

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Link Adjustment for Assembly Deviation Control of Extendible Support Structures via Sparse Optimization

Perceptive Model Predictive Control for Continuous Mobile Manipulation

Learning Mobile Manipulation through Deep Reinforcement Learning

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