This paper presents a new method of path planning for an industrial robot manipulator that performs thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser triangulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed, based on the application of Discrete Exterior calculus methods, which is characterized by a high computational efficiency. The developed algorithms and their software implementation were experimentally tested with the robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral-based raw materials.

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object’s dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot.

The article presents new results of studying the causes of material loss during microplasma spraying (MPS) and substantiates the choice of optimal parameters for efficient spraying of zirconium wire coatings on steel substrates and obtaining coatings with the desired porosity up to 20.3% on a titanium alloy. Optimum spraying parameters provide the maximum material utilization coefficient (MUC) - up to 95%, and the minimum material losses when spraying coatings on small parts: 47% when spraying onto a substrate with an average cross-sectional area of ​​2 mm and less than 1% when spraying onto a substrate with an average diameter of 8 mm. The studies were carried out using a multifactorial experiment with fractional replicas 24-1 with varying such spraying parameters as current strength, spraying distance, plasma-forming gas flow rate, and wire flow rate. To study the distribution of the coating material in the spray spot, macro photography of the metallization figures was carried out and analyzed with the construction of approximating curves. Scanning electron microscopy was used to determine the pore size and porosity of zirconium coatings at different parameters of microplasma spraying on a titanium alloy. Using the methods of regression analysis of the experimental results, equations were obtained that make it possible to assess the degree of influence of the parameters of microplasma spraying on the value of the MUC and the porosity of the coatings, and it was found that the greatest influence is exerted by the current strength and the plasma-forming gas flow rate. The choice of the optimal MPS parameters of a zirconium coating with a porosity suitable for medical implant coatings has been substantiated. В статье представлены новые результаты исследования причин потерь материала в процессе микроплазменного напыления (МПН) и обоснован выбор оптимальных параметров для эффективного напыления циркониевых проволочных покрытий на стальные подложки и получения покрытий с желаемой пористостью до 20,3 % на титановом сплаве. Оптимальные параметры напыления обеспечивают максимальный коэффициент использования материала (КИМ) - до 95%, и минимальные потери материала при напылении покрытий на детали малых размеров: 47% при напылении на подложку со средним диаметром площади поперечного сечения 2 мм и менее 1% при напылении на подложку со средним диаметром 8 мм. Исследования проводились с применением многофакторного эксперимента с дробными репликами 24–1 с варьированием таких параметров напыления, как сила тока, дистанция напыления, расход плазмообразующего газа и скорость подачи проволоки. Для изучения распределения материала покрытия в пятне напыления проводили макросъемку фигур металлизации и анализировали их с построением аппроксимирующих кривых. Методами сканирующей электронной микроскопии определяли размеры пор и пористость циркониевых покрытий при разных параметрах микроплазменного напыления на титановый сплав. Методами регрессионного анализа экспериментальных результатов были получены уравнения, позволяющие оценить степень влияния параметров микроплазменного напыления на величину КИМ и пористость покрытий и установлено, что наибольшее влияние оказывают сила тока и расход плазмообразующего газа. Обоснован выбор оптимальных параметров МПН циркониевого покрытия с пористостью, подходящей для покрытий медицинских имплантатов. Мақалада микроплазмалық тозаңдату (МПТ) кезінде материалды жоғалту себептерін зерттеудің жаңа нәтижелері келтірілген және болат төсеніштерге цирконий сыммен қаптауды тиімді тозаңдату үшін және титан қорытпасында 20,3% дейін қажетті кеуектілігі бар қаптаманы алу үшін оңтайлы параметрлерді таңдау негізделеді. Тозаңдатудың оңтайлы параметрлері материалды пайдаланудың максималды коэффициентін (МПК) - 95%-ға дейін, және шағын көлемдегі бөлшектерге қаптамаларды тозаңдату кезінде материалдың ең аз шығынын қамтамасыз етеді: көлденең қиманың орташа ауданы 2 мм кезінде 47%-ға тең және орташа диаметрі 8 мм төсемдерге тозаңдату кезінде 1%-дан кем болады....

This article investigates the trajectory tracking control of a differential-drive two-wheeled mobile robot (DDWMR) using its kinematic model, framed in the context of sustainability. A nonlinear-to-linear transformation based on differential flatness is employed to convert the original nonlinear system into two fully decoupled linear subsystems, enabling a simple and robust controller design. Unlike conventional flatness-based methods that rely on exact feedforward linearization around a reference trajectory, the proposed approach performs plant linearization, ensuring reliable tracking across a wide range of trajectories. The resulting two-loop architecture consists of an inner nonlinear loop implementing state prolongation and static feedback and an outer linear controller performing trajectory tracking of the linearized system. Simulation results on a circular reference trajectory demonstrate high tracking accuracy, with a maximum transient deviation of 0.28 m, a settling time of approximately 120 s, and a steady-state mean tracking error below 0.01 m. These results confirm that the plant-linearization-based framework provides accuracy, robustness, and practical applicability for DDWMR trajectory tracking, all within a responsible control environment.

This article investigates the trajectory-tracking control of a differential-drive two-wheeled mobile robot (DDWMR) using its kinematic model. A nonlinear-to-linear transformation based on differential flatness is employed to convert the original nonlinear system into two fully decoupled linear subsystems, enabling a simple and robust controller design. Unlike conventional flatness-based methods that rely on exact feedforward linearization around a reference trajectory, the proposed approach performs plant linearization, ensuring reliable tracking across a wide range of trajectories. The resulting two-loop architecture consists of an inner nonlinear loop implementing state prolongation and static feedback, and an outer linear controller performing trajectory tracking of the linearized system. Simulation results on a circular reference trajectory demonstrate high tracking accuracy, with a maximum transient deviation of 0.28 m, a settling time of approximately 120 s, and a steady-state mean tracking error below 0.01 m. These results confirm that the plant-linearization-based framework provides superior accuracy, robustness, and practical applicability for DDWMR trajectory tracking.

This paper presents new results of an experimental study of the application of machine learning methods to trajectory tracking control of a wheeled mobile robot (WMR). The experiments were conducted using a custom-assembled wheeled mobile platform and software developed for its programmable components, enabling the implementation of WMR trajectory tracking control using machine learning. A model of a skid-steered wheeled mobile robot control was developed based on a recurrent neural network. The neural network was trained using experimental data obtained during testing of the robot control system, and it was demonstrated that the control and data collection system of the experimental robot ensures high accuracy in following a given trajectory. Specifically, the model constructed using machine learning methods allows predicting the robot's movement with high accuracy over a time horizon of approximately 3-4 seconds at an average robot speed of 1.2 meters per second. The results obtained during the trajectory tracking control of the experimental onboard control system are promising for skid-steered wheeled mobile robot of any carrying capacity and provide progress in the current field of machine learning applications in robotics, automatic control, and inertial navigation.