Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.
Combinatorial optimization is a key component in critical decision problems such as routing, scheduling, network design, and graph optimization. Although combinatorial optimization methods, including exact algorithms, approximation methods, constraint programming, mixed integer p…
This study considers the digital transformation of Kazakhstan’s agro-industrial complex, which has created an urgent need for scientifically grounded methods that can optimize marketing strategies under conditions of resource limitations, production seasonality, and heterogeneous…
Machine learning technologies are increasingly embedded in financial decision-making processes, yet their influence on investor behavior and market efficiency remains insufficiently understood. This study investigates how machine learning-assisted investment decisions affect inve…
Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decomposition on prediction accurac…
This study investigates the pressing issue of credit card fraud in the context of evolving e-commerce platforms and the necessity for improved fraud detection mechanisms. Since the advent of credit cards, the surge in their usage has led to a corresponding increase in fraud rates…
This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 fr…