Experimental Validation of the Open-Source DMPC Framework GRAMPC-D applied to the Remotely Accessible Robotarium

Burk, Daniel; Völz, Andreas; Graichen, Knut

doi:10.1109/icma52036.2021.9512773

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…areas are shown in Figure 4, that use the Robotarium to experimentally validate their algorithms 1 . Through its years of use, the testbed has been used to support research fields like multi-agent model predictive control [13], networked control theory [14] as well as emerging fields like distributed, multiagent machine learning [15]. An unexpected consequence of providing this type of remote-access research testbed to anyone has been a demand from research fields that deal with similar multi-agent problems, such as entomologists testing insect inspired algorithms that produce ensemble dynamics observed in nature.…”

Section: The Demand For and Diverse Usage Of The Robotariummentioning

confidence: 99%

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education

Wilson

Egerstedt

2023

IEEE Open J. Control. Syst.

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In robotic research and education, the cost in terms of money, expertise, and time required to instantiate and maintain robotic testbeds can prevent researchers and educators from including hardware based experimentation in their laboratories and classrooms. This results in robotic algorithms often being validated by low-fidelity simulation due to the complexity and computational demand required by highfidelity simulators. Unfortunately, these simulation environments often neglect real world complexities, such as wheel slip, actuator dynamics, computation time, communication delays, and sensor noise. The Robotarium provides a solution to these problems by providing a state-of-the-art, multi-robot research facility to everyone around the world free of charge for academic and educational purposes. This paper discusses the remote usage of the testbed since its opening in 2017, details the testbeds design, and provides a brief tutorial on how to use it.

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Section: The Demand For and Diverse Usage Of The Robotariummentioning

confidence: 99%

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education

Wilson

Egerstedt

2023

IEEE Open J. Control. Syst.

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“…Similarly, Ebel and Eberhard (2021) use formation control to compare a cooperative DMPC scheme to a distributed control method based on algebraic graph theory, confirming advantages of DMPC with respect to constraint handling also in hardware experiments. Recently, Burk et al (2021a) tested the performance of an open-source DMPC framework with formation control, including real-world hardware but without truly distributed computation in the experiments. Novoth et al (2021) study more complex scenarios including obstacle avoidance, but without the consideration of hardware experiments and distributed computation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we focus on the implementation, numerics, and experimental testing with real robot hardware. Van Parys and Pipeleers (2017) and Burk et al (2021a) made notable contributions to the experimental validation of cooperative DMPC. The former tested an ADMM-based DMPC scheme on nonholonomic mobile robots with nonlinear dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…Even with infinitely many iterations, the application of ADMM to problems with nonconvex constraint sets does not admit general convergence guarantees. Burk et al (2021a) tested the open-source framework GRAMPC-D for the cooperative control of nonlinear unicycles running multiple ADMM iterations per control step. However, the test setup did not allow for true distributed execution as all control algorithms were implemented on a single computer.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Stomberg¹,

Ebel²,

Faulwasser³

et al. 2023

Preprint

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Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

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Experimental Validation of the Open-Source DMPC Framework GRAMPC-D applied to the Remotely Accessible Robotarium

Cited by 2 publications

References 17 publications

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education

Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

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