Deployment of an autonomous mobile manipulator at MBZIRC

Carius, Jan; Wermelinger, Martin; Balasubramanian, R.; Holtmann, Kai; Hutter, Marco

doi:10.1002/rob.21825

Cited by 28 publications

(11 citation statements)

References 21 publications

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“…We support the proposed algorithm with an experimental validation on a compliant 6DoF robotic arm, shown in Figure 1, which makes use of low-power series elastic actuators [10]. The results demonstrate that the controller improves the tracking performance in terms of root mean square tracking error compared to a PID controller, the offset-free MPC scheme presented in [11], the nonlinear MPC scheme [12], and the three learning-based schemes presented in [13], [14].…”

Section: Introductionsupporting

confidence: 52%

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Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm

Carron

Arcari

Wermelinger

et al. 2019

IEEE Robot. Autom. Lett.

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150

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High-precision trajectory tracking is fundamental in robotic manipulation. While industrial robots address this through stiffness and high-performance hardware, compliant and cost-effective robots require advanced control to achieve accurate position tracking. In this paper, we present a model-based control approach, which makes use of data gathered during operation to improve the model of the robotic arm and thereby the tracking performance. The proposed scheme is based on an inverse dynamics feedback linearization and a data-driven error model, which are integrated into a model predictive control formulation. In particular, we show how offset-free tracking can be achieved by augmenting a nominal model with both a Gaussian Process, which makes use of offline data, and an additive disturbance model suitable for efficient online estimation of the residual disturbance via an extended Kalman filter. The performance of the proposed offset-free GPMPC scheme is demonstrated on a compliant 6 degrees of freedom (DoF) robotic arm, showing significant performance improvements compared to other robot control algorithms.

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Section: Introductionsupporting

confidence: 52%

“…In order to validate the tracking capabilities of the proposed method, we implemented the algorithm on a compliant 6DoF robotic arm [10]. All joints are equipped with the same high-performance Series Elastic Actuator (SEA) units.…”

Section: Resultsmentioning

confidence: 99%

Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm

Carron

Arcari

Wermelinger

et al. 2019

IEEE Robot. Autom. Lett.

Self Cite

150

View full text Add to dashboard Cite

show abstract

“…These challenges are located in complex unstructured and dangerous environment such as disaster rescuing, and need the robots to perform a series of complex tasks combining perception, manipulation, localization and even multi-robot cooperation. The robots should have manipulation ability with a high-level requirement [10,11]. Houseroom is another common application situation, which is unstructured and hard for robots to do the housework.…”

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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“…Dynamic manipulation belongs to the family of nonprehensile (graspless) manipulation, which generally in- [22]. The series elastic actuated joints allow for highly dynamic motions, and high impact robustness.…”

Section: Problem Description a Dynamic Object Manipulationmentioning

confidence: 99%

Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation

Sleiman

Carius

Grandia

et al. 2019

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

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We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hardcontact model is assumed, and the unilateral constraints are imposed in the form of complementarity conditions. Newton's impact law is adopted for enhanced physical correctness. The optimal control problem is formulated as a multi-staged program through a multiple-shooting scheme. This problem structure is exploited within the FORCES Pro framework to retrieve optimal motion plans, contact sequences and control inputs with increased computational efficiency. We investigate our method on a variety of dynamic object manipulation tasks, performed by a six degrees of freedom robot. The dynamic feasibility of the optimal trajectories, as well as the repeatability and accuracy of the task-satisfaction are verified through simulations and real hardware experiments on one of the manipulation problems.

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Deployment of an autonomous mobile manipulator at MBZIRC

Cited by 28 publications

References 21 publications

Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm

Data-Driven Model Predictive Control for Trajectory Tracking With a Robotic Arm

Learning Mobile Manipulation through Deep Reinforcement Learning

Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation

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