The paper is devoted to the problem of optimizing the trajectories of parallel robots in the positioning process. The problem of minimizing the duration of the robot positioning cycle in order to increase its productivity is discussed. A new optimization problem has been formulated, aimed at minimizing the total mileage of electric drives during the cycle in order to increase the energy efficiency of the robot. The objective functions of optimization problems based on modified metrics are proposed: Manhattan and Chebyshev. A comparison of the efficiency of using optimal trajectories instead of the "obvious" ones was carried out for various parallel robots: planar, tripod, and delta robots. Conclusions are drawn about the basic requirements for the trajectory of the robot to ensure maximum productivity and energy efficiency.

Keywords: parallel robot, performance, duration of the positioning cycle, energy efficiency, electric drive mileage, objective function, Chebyshev metric, Manhattan metric, optimal trajectory, comparative modeling, planar robot, tripod robot, delta robot

An ensemble of models for predicting the position of a mobile robot moving in an unstructured environment is presented. An architecture has been developed that integrates a kinematic motion model with trainable models utilizing elevation map data and semantic segmentation. The principles for constructing a spatial feature map are described, incorporating geometric characteristics such as the terrain roughness index and a fuzzy traversability index. A modular structure of the following blocks is proposed: data preprocessing, geometric property computation, segmentation, and decision-making. Test results demonstrate the advantage of combining kinematic and sensor-based models for autonomous navigation in complex environments.

Keywords: traversability model, elevation map, point cloud, kinematic model, segmentation, machine learning, feature map

This article explores visual data processing methods for underwater navigation and environmental reconstruction, based on modern approaches in computer vision and robotics. A system implemented in the ROS (Robot Operating System) environment is proposed, enabling simultaneous localization and mapping (SLAM) in underwater conditions. The system evaluation methodology includes virtual experiments using the Gazebo simulator, which replicate realistic underwater operational scenarios. The study demonstrates the feasibility of integrating stereo cameras, confirms the effectiveness of image processing methods in underwater environments, and presents quantitative metrics for navigation accuracy and object reconstruction. The results validate the proposed solutions as promising for real-world applications in autonomous underwater exploration.

Keywords: underwater navigation, environmental reconstruction, computer vision, visual odometry, SLAM, ROS, Gazebo, simulation, visual data, robotics

The article explores the interaction of Tello EDU, a small-sized educational drone, with Turtlebot3, an unmanned ground vehicle, in rooms with a weak signal. The article examines how, using a local network and the robot operating system (ROS), it is possible to achieve effective collaboration between these two devices. It analyzes how a local network can be used to broadcast data and monitor devices in conditions of a weak external signal. The role of ROS as the main tool for managing and interacting with devices is being investigated. In addition, the article examines specific use cases, including interaction and coordination between Tello EDU and Turtlebot3. A diagram of the interaction of two unmanned vehicles is also presented, a detailed description of their operation is described, and Python code is presented using various libraries based on the ROS robotic operating system.

Keywords: Tello Edu, operating system (ROS), UAV, local area network, Wi-fi, nodes, SLAM, weak signal, route planning, autonomous robot, Turtlebot3

The movement of robotic systems can occur in conditions of interference disturbances, different in quality and power. In this case, the actual task is to correct the initial planned trajectory of the robot's movement in order to evade the latter from the action of these sources in order to maximize some quality functionality. It is advisable to associate the latter with the probability of successful passage of the target trajectory in the field of interference effects. The peculiarity of such an adjustment is the complexity of optimizing the corresponding probability functionals, which leads to the need to develop approximate optimization methods based, however, on a fairly accurate calculation of the probabilities of successful passage for each specific trajectory. In this article, we propose such an approximate correction technique that allows us to effectively bypass interference sources defined by their known areas of action and characteristic probabilistic functions. This technique is based on an iterative procedure of successive approximations to such a trajectory, which has a given probability of successful passage. The developed technique can be effectively integrated into the movement planner of robotic objects moving in conditions of obstacles with fixed boundaries, as well as corresponding repeller sources, information about which allows us to estimate with sufficient accuracy the probability of successful passage of any trajectories in their vicinity at a given speed mode of movement.

Keywords: robotics complex, repeller sources, motion planning, probability of successful completion, iterative procedure, target functionality

This article proposes a method for correcting the intermediate trajectory obtained by one of the planning methods, taking into account the limitations on the linear velocity and acceleration of the apparatus, as well as on the angle of its pitch. This technique is combined with the smoothing procedure, which includes the stage of minimizing the length of a piecewise polyline trajectory and rounding the corners at the vertices with the construction of a smooth time parametric representation of it using the modified Dubins method.

Keywords: robotics complex, unmanned aerial vehicle, stability and controllability of the vehicle, motion planning, local adjustment of the planned trajectory, reduction of energy costs