Extremal problems for infinite order parabolic systems with time-varying lags are presented. An optimal boundary control problem for infinite order parabolic systems in which time-varying lags appear in the state equations and in the boundary conditions simultaneously is solved. The time horizon is fixed. Making use of Dubovicki-Milutin scheme, necessary and sufficient conditions of optimality for the Neumann problem with quadratic performance functionals and constrained control are derived.
This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support. The simultaneous use of colour (RGB) and depth (D) data allows to improve segmentation accuracy, especially in case of colour camouflage, illumination changes and occurrence of shadows. Three GPUs were used to accelerate calculations: embedded NVIDIA Jetson TX2 (Maxwell architecture), mobile NVIDIA GeForce GTX 1050m (Pascal architecture) and efficient NVIDIA RTX 2070 (Turing architecture).Segmentation accuracy comparable to previously published works was obtained. Moreover, the use of a GPU platform allowed to get real-time image processing. In addition, the system has been adapted to work with two RGB-D sensors: RealSense D415 and D435 from Intel.
In this paper some parameterizations of controls are examined in a Lie algebraic method of motion planning for driftless nonholonomic systems. The purpose of the examination is to establish how numerous the parameterization should be and which items of a harmonic basis are to be included into the parameterization. An algorithm is presented to evaluate parameterizations without (or reduced) impact of a local, desired direction of motion.
This paper presents a decentralized control approach based on a neural network for a hydrostatic transmission. The bent-axis angle of the hydraulic motor is adjusted by a pure feedforward control law based on identified physical parameters, whereas the corresponding motor angular velocity is controlled using a combination of a generalized proportional-derivative (PD) controller and a multilayer perceptron with one hidden layer that is trained by an error-feedback learning approach and uses only measurable input variables. In this observer-free control structure, the neural network learns the inverse dynamics by minimizing the PD controller output and, as a consequence, an accurate tracking of the desired trajectory is achieved. As no physical modelling is required for the motor velocity control design, it can be considered as model-free. The tracking performance shows the robustness of the overall control structure for the hydrostatic transmission despite disturbances and uncertainties. The proposed control scheme is investigated by simulations first. Second, experimental results are presented taken from a dedicated test rig at the Chair of Mechatronics, University of Rostock. Finally, an experimental comparison with results from previous work is provided.
In this paper, an enhanced Artificial Potential Field (AFP) algorithm applied to autonomous mobile robot is presented. The proposed solution is extended by an additional BUG algorithm and a ground quality indicator. The modification allows to avoid local minima in path planning caused by complex terrain obstacles. The developed algorithm takes into account the substrate quality, classifying a poor ground as an obstacle. It was implemented and tested in Matlab software, utilizing the track planning algorithm as a state machine. The simulation environment enables graphical presentation of the chosen path and the arrangement of moving area. The article presents and discusses a basic issue in local path planning of autonomous mobile platforms, i.e. the ground quality, which is ignored in classical algorithms. This is a non-trivial problem, which impacts the success rate of getting the final destination. The developed algorithm is extended by a BUG rule and ground quality indicator which allows to avoid immobilization of the platform.
