Mobile robots are enjoying increasing popularity in a number of different automation tasks. Omnidirectional mobile robots especially allow for a very flexible operation. They are able to accelerate in every direction, regardless of their orientation. In this context, we developed our own robot platform for research on said types of robots. It turns out that these mobile robots show interesting behaviour, which commonly used models for omnidirectional mobile robots fail to reproduce. As the exact sources and structures of mismatches are still unknown, non-parametric Gaussian process regression is used to develop a data-based model extension of the robot. A common control task for industrial applications is trajectory tracking, where a robot needs to follow a predefined path, for example in a warehouse, as close as possible in space and time. Appropriate feed-forward solutions for the data-based model are developed and finally leveraged in closed-loop control via nonlinear model predictive control. In real-world experiments, the results are compared to commonly used proportional position-based feedback. This novel contribution builds upon the preliminary work in [7] but, for the first time, includes also closed-loop (trajectory) tracking.
For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structured step-by-step procedure, a model predictive control (MPC) scheme for a Schunk PowerCube robot is derived. Neweul-M 2 provides the necessary nonlinear model in symbolical and numerical form. To handle the heavy online computational burden involved with the derived nonlinear model, a linear time-varying MPC scheme is developed based on linearizing the nonlinear system concerning the desired trajectory and the a priori known corresponding feed-forward controller. To improve the identification of the nonlinear friction models of the joints, a nonlinear regression method and the Sparse Identification of Nonlinear Dynamics (SINDy) are compared with each other concerning robustness, online adaptivity, and necessary preprocessing of the input data. Everything is implemented on a slim, low-cost control system with a standard laptop PC.
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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