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The current state-of-the art motion planners for mobile robots typically do not consider the future movement of moving obstacles. Instead they work by rapid replanning, which makes them reactively adapt to any changes in the environment. This can result in a sub-optimal behavior, which we address in this work by proposing a predictive motion planner that integrates motion predictions into all planning steps. We demonstrate the validity of our approach by evaluating our proposed planner in a dynamic environment where the robot moves slower than the moving obstacles. We benchmark our predictive planner with a reactive planning approach and observe better performance, both in avoiding collisions and maintaining the robots position in the goal region.

Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method.Practitioner Points Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency. This paper showcases two case studies for automated data transfer in digital twins. One uses a tailor-made infrastructure for data transfer between the facility and digital twin, the other uses edge computing. The digital twin objective should determine the required simulation frequency and, thus, sets boundaries on suitable implementation methods. image

Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model. In this letter, we show their equivalence to a (relaxed) indirect approach, allowing us to reformulate direct methods in terms of estimated parameters and covariance matrices. This allows us to provide further insights into how these direct predictive control methods work, showing that, for unconstrained problems, the direct methods are equivalent to subspace predictive control with a reduced weight on the tracking cost, and analyzing the impact of the data length on tuning strategies. Via a numerical experiment, we also illustrate why the performance of direct DDPC methods with fixed regularization tends to degrade as the number of training samples increases.

Software sensors, or soft sensors, can be a feasible option to monitor parameters that are difficult (or impossible) to measure with hardware sensors. At Henriksdal water resource recovery facility (WRRF), the operators have long experienced issues with a clogging sensor for the dry solids (DS) content in thickened primary sludge. A soft sensor was developed, and in the process, two methods were compared: long short-term memory (LSTM) network, and linear regression. The first is a recurrent neural network that can capture non-linear dynamics, whereas the latter is a linear static model. The LSTM network was the best at predicting the DS content, with a mean squared error (MSE) of 0.341 with respect to laboratory data. The linear regression model performed worse than estimating a long-time average of daily manual samples but outperformed the online sensor. Replacing the existing sensor with the developed soft sensor can open possibilities to more efficient control and operation of the thickener unit.

The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs). These models have become recently popular in the machine learning community since, owing to their parallelizability, they can be efficiently and scalably trained to tackle extremely long sequence classification and regression problems. Interestingly, SSMs appear as an effective way to learn deep Wiener models, which allows us to reframe SSMs as an extension of a model class commonly used in system identification. To stimulate a fruitful exchange of ideas between the machine learning and system identification communities, we deem it useful to summarize the recent contributions on the topic in a structured and accessible form. At last, we highlight future research directions for which this community could provide impactful contributions. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for this is that the learned controller may not be able to excite the system dynamics well enough initially, and therefore it can take a long time to get data that is informative enough to learn for good control. The paper contributes by augmentation of reinforcement learning with a simple guiding feedback controller, for example, a proportional controller. The key advantage in set point control is a much improved excitation that improves the convergence properties of the reinforcement learning controller significantly This can be very important in real-world control where quick and accurate convergence is needed. The proposed method is evaluated with simulation and on a real-world double tank process with promising results.

Linear regression models have a wide range of applications in statistics, signal processing, and machine learning. In this Lecture Notes column we will examine the performance of the least-squares (LS) estimator with a focus on the case when there are more parameters than training samples, which is often overlooked in textbooks on estimation.

Motion planning is a mature field within robotics with many successful solutions. Despite this, current state-of-the-art planners are still computationally heavy. To address this, recent work have employed ideas from machine learning, which have drastically reduced the computational cost once a planner has been trained. It is mainly static environments that have been studied in this way. We continue along the same research direction but expand the problem to include dynamic environments, hence increasing the difficulty of the problem. Analogously to previous work, we use imitation learning, where a planning policy is learnt from an expert planner in a supervised manner. Our main contribution is a planner mimicking an expert that considers the future movement of all the obstacles in the environment, which is key in order to learn a successful policy in dynamic environments. We illustrate this by evaluating our approach in a dynamic environment and by comparing our planner with a conventional planner that re-plans at every iteration, which is a common approach in dynamic motion planning. We observe that our approach yields a higher success rate, while also taking less time and accumulating less distance to reach the goal.