Trajectory tracking of an omnidirectional mobile robot using Gaussian process regression

Eschmann, Hannes; Ebel, Henrik; Eberhard, Peter

doi:10.1515/auto-2021-0019

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…are used, where x, x ∈ R 3 and t, t ∈ N 0 . This choice of the kernel is a product of the SE kernel (3) and the periodic kernel (4), which respects the periodicity of the considered trajectories, see (19). The kernel hyperparameters σ s,j , σ n,j , A j , PER,j are set to the values from Table II.…”

Section: Resultsmentioning

confidence: 99%

“…In our case, to ensure a good generalization to all possible input combinations satisfying (9) while sufficiently covering the corresponding input space, we generate this data using a special automatic model tuning procedure from refs. [18, 19]. To generate the training data in the hardware experiment, the hit-and-run sampler from ref.…”

Section: Data-based Model Of the Mobile Robotmentioning

confidence: 99%

“…For more information on the specific training procedure, it is referred to ref. [19]. Some of the trajectories that are used in the training process are depicted in Fig.…”

Section: Data-based Model Of the Mobile Robotmentioning

confidence: 99%

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Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

2023

Self Cite

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Mobile robots are a key component for the automation of many tasks that either require high precision or are deemed too hazardous for human personnel. One of the typical duties for mobile robots in the industrial sector is to perform trajectory tracking, which involves pursuing a specific path through both space and time. In this paper, an iterative learning-based procedure for highly accurate tracking is proposed. This contribution shows how data-based techniques, namely Gaussian process regression, can be used to tailor a motion model to a specific reoccurring reference. The procedure is capable of explorative behavior meaning that the robot automatically explores states around the prescribed trajectory, enriching the data set for learning and increasing the robustness and practical training accuracy. The trade-off between highly accurate tracking and exploration is done automatically by an optimization-based reference generator using a suitable cost function minimizing the posterior variance of the underlying Gaussian process model. While this study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. The effectiveness of this approach is validated in meaningful real-world experiments on a custom-built omnidirectional mobile robot where it is shown that explorative behavior can outperform purely exploitative approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Data-based Model Of the Mobile Robotmentioning

confidence: 99%

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Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

2023

Self Cite

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“…Since real, imperfect, hardware is used, there is a nonnegligible plant-model mismatch for the DMPC controller. Indeed, although built in the same manner, each individual robot behaves slightly differently than the other robots (Eschmann et al, 2021), i.e., the robots exhibit characteristic nonlinear behavior, due to friction, the complicated wheel-floor contacts, and manufacturing imperfections.…”

Section: Hardware and Experiments Environmentmentioning

confidence: 99%

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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“…The structure of the output layer is relatively simple. The linear weighted sum of the output of the hidden nodes obtained after continuous calculation is the output result [12]. Figure 1 is the basic structure of radial basis neural network.…”

Section: Picking Robot Trajectory Tracking Control Algorithmmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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Trajectory tracking of an omnidirectional mobile robot using Gaussian process regression

Cited by 12 publications

References 14 publications

Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

Exploration-exploitation-based trajectory tracking of mobile robots using Gaussian processes and model predictive control

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

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

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