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.

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 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.

The paper presents a trajectory control method for nonlinear differentially flat control systems, referred to as the generalized Computed Torque Control (CTC) method. It is shown that the classical Computed Torque Control, traditionally applied to serial manipulators, can be regarded as a special case of a general trajectory control method for a specific class of flat systems, denoted as class A systems. An approach to the linearization of such systems is proposed, based on class C coordinate transformations, which transform the original nonlinear control system into an equivalent trivial linear control system while preserving equivalence in the sense of control system theory. It is noted that class C transformations admit hardware implementation using physically realizable signal-processing elements. It is shown that flat systems of class A possess a flat output depending only on the current state of the system, which extends the applicability of the proposed method to other nonlinear control objects, including certain types of mobile robots. In addition, the analysis of the classical CTC method makes it possible to formulate a disturbance rejection criterion for the linear controller included in the trajectory control scheme.

In this paper we present a method for representing a continuous curve in the plane by a function defined on a unit interval, which we call the angular characteristic function of the curve. This representation is invariant under translations, rotations and universal scaling and is well suited for curve shape recognition. We have used this function for feature extraction in an experimental system for optical recognition of handwritten characters and the approach, briefly described in this paper, has shown good results. For graphic primitives - circular arcs and line segments - the angular characteristic function representing them has a particularly simple form. This circumstance allows using the angular characteristic function in motion planning problems and in robotics. As an example of such applications, we present an algorithm that can be used in a system for automatic program generation for a robotic manipulator used for a cutting process. This is an algorithm for approximating a smooth curve in the plane by a sequence of such graphic primitives forming a continuous smooth curve (in which each segment is conjugate to its neighbors).