Satellite clock bias prediction plays a crucial role in enhancing the accuracy of satellite navigation systems. In this paper, we propose an approach utilizing Long Short-Term Memory (LSTM) networks to predict satellite clock bias. We gather data from the PRN 8 satellite of the Galileo and preprocess it to obtain a single difference sequence, crucial for normalizing the data. Normalization allows resampling of the data, ensuring that the predictions are equidistant and complete. Our methodology involves training the LSTM model on varying lengths of datasets, ranging from 7 days to 31 days. We employ a training set consisting of two days' worth of data in each case. Our LSTM model exhibits exceptional accuracy, with a Root Mean Square Error (RMSE) of 2.11 $\times$ 10$^{-11}$. Notably, our approach outperforms traditional methods used for similar time-series forecasting projects, being 170 times more accurate than RNN, 2.3 $\times$ 10$^7$ times more accurate than MLP, and 1.9 $\times$ 10$^4$ times more accurate than ARIMA. This study holds significant potential in enhancing the accuracy and efficiency of low-power receivers used in various devices, particularly those requiring power conservation. By providing more accurate predictions of satellite clock bias, the findings of this research can be integrated into the algorithms of such devices, enabling them to function with heightened precision while conserving power. Improved accuracy in clock bias predictions ensures that low-power receivers can maintain optimal performance levels, thereby enhancing the overall reliability and effectiveness of satellite navigation systems. Consequently, this advancement holds promise for a wide range of applications, including remote areas, IoT devices, wearable technology, and other devices where power efficiency and navigation accuracy are paramount.

For example, a U-Net [31] model can be used for some studies have utilized manually annotated images for deep semantic segmentation, and a Convolutional Neural Network learning applications involving microplastics, their datasets are (CNN) can then classify the segmented pixels, as demonstrated not publicly accessible [22], [23], [25]. Notably, there is only in [22], [24]. It is also possible to perform instance segmentation one other open-source Scanning Electron Microscopy (SEM) directly from the start. For instance, a Mask R-CNN dataset on microplastics, presented in [24], which categorizes model can simultaneously identify regions of interest, classify particles by shape (e.g., fragments, fibers, and beads) and each detected object, and generate a mask for each instance, features a more limited size distribution. These contributions as shown by [23]. Additionally, Faster R-CNN, primarily used not only address the urgent environmental issue of microplastic for object detection, has been applied to microscopic images to contamination but also set a new benchmark for detecting and classify microplastics into two polymer types [25]. Given the analyzing microplastics in aquatic environments, paving the nature of our dataset, where overlapping and crowded MNPs way for future innovations in the field.

In this paper, we present a novel problem coined multi-robot, multi-objective, and multi-mode routing and scheduling (M^3RS). The formulation for M^3RS is introduced for time-bound multi-robot, multi-objective routing and scheduling missions where each task has multiple execution modes. Different execution modes have distinct resource consumption, associated execution time, and quality. M^3RS assigns the optimal sequence of tasks and the execution modes to each agent. The routes and associated modes depend on user preferences for different objective criteria. The need for M^3RS comes from multi-robot applications in which a trade-off between multiple criteria arises from different task execution modes. We use M^3RS for the application of multi-robot disinfection in public locations. The objectives considered for disinfection application are disinfection quality and number of tasks completed. A mixed-integer linear programming model is proposed for M^3RS. Then, a time-efficient column generation scheme is presented to tackle the issue of computation times for larger problem instances. The advantage of using multiple modes over fixed execution mode is demonstrated using experiments on synthetic data. The results suggest that M^3RS provides flexibility to the user in terms of available solutions and performs well in joint performance metrics. The application of the proposed problem is shown for a team of disinfection robots.} The videos for the experiments are available on the project website: https://sites.google.com/view/g-robot/m3rs/ .

The travelling salesperson problem (TSP) is a classic resource allocation problem used to find an optimal order of doing a set of tasks while minimizing (or maximizing) an associated objective function. It is widely used in robotics for applications such as planning and scheduling. In this work, we solve TSP for two objectives using reinforcement learning (RL). Often in multi-objective optimization problems, the associated objective functions can be conflicting in nature. In such cases, the optimality is defined in terms of Pareto optimality. A set of these Pareto optimal solutions in the objective space form a Pareto front (or frontier). Each solution has its trade-off. We present the Pareto frontier approximation network (PA-Net), a network that generates good approximations of the Pareto front for the bi-objective travelling salesperson problem (BTSP). Firstly, BTSP is converted into a constrained optimization problem. We then train our network to solve this constrained problem using the Lagrangian relaxation and policy gradient. With PA-Net we improve the performance over an existing deep RL-based method. The average improvement in the hypervolume metric, which is used to measure the optimality of the Pareto front, is 2.3%. At the same time, PA-Net has 4.5x faster inference time. Finally, we present the application of PA-Net to find optimal visiting order in a robotic navigation task/coverage planning. Our code is available on the project website.