Boris Gurenko

Control System Design for Autonomous Underwater Vehicle

This paper presents adaptive control system for the autonomous underwater vehicle. A nonlinear interrelated dynamic model of the underwater vehicle is considered. The novelty of developed control system is in use of positional-trajectory control method. This means feasibility of Autonomous Underwater Vehicle in complicated trajectories with relatively small computational resources. Adaptation of the control system is based on robust disturbances estimation. Modeling results validate proposed methods.

Homing and Docking Autopilot Design for Autonomous Underwater Vehicle

This paper presents homing and docking autopilot design for the autonomous underwater vehicle (AUV). A nonlinear interrelated dynamic model of the underwater vehicle is considered. The AUV autopilot is designed on base of a position-trajectory control method. Adaptation of the control system is based on robust disturbances estimation. Modeling and hardware results approved feasibility of the proposed methods.

Position-Trajectory Control System for Unmanned Robotic Airship

Abstract This paper considers design of the control system for prototype of stratospheric airship, that is distinctive for its hybrid shape, leading to essential aerodynamic moments in flight. Mathematical model of the airship is presented. Control system implements remotely controlled by pilot flight and autonomous flight. Algorithm of automatic distribution of controlling forces and moments in actuators is presented. Adaptation of control system is provided with robust estimator of disturbances as indirect robust control. Control system is experimentally tested with hardware and software complex for HIL-simulation and pilots training.

Development of Indirect Adaptive Control for Underwater Vehicles Using Nonlinear Estimator of Disturbances

In this paper, authors propose a new method of disturbance estimation that can be used for marine and air vehicles. As stability area for nonlinear systems is connected with performance of estimation, indirect measurement is proposed to estimate the error. The base for calculation is vehicle accelerations provided by navigation system. New algorithms were tested in simulation for which authors developed a block diagram of indirect adaptive control system that features independence of estimator from controller. Analysis of results showed that the error of the developed estimator in transient mode is 30-40% less than error of the linear estimator. In steady state mode, gain of the proposed estimator is equal to gain of the linear estimator and output noise is the same. Simulated system was implemented in mini motor boat, and showed good results in experiment. New estimator allowed to increase the accuracy of moving along the paths.

Development of Simulator for Intelligent Autonomous Underwater Vehicle

Testing and debugging of real equipment is a time consuming task. In particular, in the case of marine robots, it is necessary each time to carry out the transportation and deployment of a robot on the water. Experiments with not yet fully functional prototype of marine robot equipped with expensive hardware is in the meantime very risky. Therefore, the use of simulators is affordable way to accelerate the development of robotic systems from the viewpoint of labor effort and cost of experiments. This paper presents a simulator specifically designed for autonomous unmanned underwater vehicles.

Decentralized Control of a Group of Homogeneous Vehicles in Obstructed Environment

The presented solution is a decentralized control system with a minimal informational interaction between the objects in the group. During control and path planning the obstacles are transformed into repellers by the synthesized controls. The main feature distinguishing the developed approach from the potential fields method is that the vehicle moves in the fields of forces depending not only on the mutual positions of a robot and an obstacle but also on the additional variables allowing solving the problem of robot’s path planning using a distributed control system (Pshikhopov and Ali, 2011). Unlike the work by Pshikhopov and Ali, 2011, here an additional dynamic variable is used to introduce stable and unstable states depending on the state variables of the robot and the neighboring objects. The local control system of each vehicle uses only the values of its own speeds and coordinates and those of the neighboring objects. There is no centralized control algorithm. In the local control algorithms the obstacles are represented as vehicles being a part of the group which allows us to unify the control systems for heterogeneous groups. An analysis was performed that proves existence and asymptotic stability of the steady state motion modes. The preformed simulation confirms the synthesis and analysis results.

Implementation of Intelligent Control System for Autonomous Underwater Vehicle

This paper introduces the implementation of intelligent motion control and planning for autonomous underwater vehicle (AUV). Previously developed control system features intelligent motion control and planning subsystem, based on artificial neural networks. It allows detecting and avoiding moving obstacles in front of the AUV. The motion control subsystem uses position-trajectory control method to position AUV, move from point to point and along given path with given speed. Control system was tested in the multi-module simulation complex. Simulation showed good results – AUV successfully achieved given goals avoiding collisions not only with static obstacles, but also with mobile ones. That allows using the proposed control system for the groups of vehicles. Besides simulation, control system was implemented in hardware. AUV prototype passed tests in Azov Sea and proved its efficiency.

Functional and modular organization of planning subsystems of mobile robot behaviour with partial uncertainty for the two-dimensional space

Recently we can see significant increase in consumer demand for autonomous mobile robots that are used for a wide range of functional tasks in a not completely determined environment. The actual task that we consider is solution problem of development of an effective situational planning subsystem for the behaviour of autonomous mobile robots. The purpose of writing this article is a brief coverage of basic principles (proposed by the authors) of functional and modular organization of situational planning subsystem, that is included in system of position trajectory control for mobile robots. The situational planning subsystem is designed to determine the values of the parameters of the safest movement in environment with moving and stationary obstacles to the goal. Designed situational planning subsystem structural is differed from others by using of neural network basis in the construction of a situational planning subsystem functional kernel. The presence of built-in detection means and short-term auto-tracking trajectories of movable obstacles with considering a priori to certain limitations, the calculation of the spatial and velocity zones where collisions with the obstacles are the most possible are differed our planning subsystem too. Also we determinate parameters of mobile robot manoeuvre on the plane, which in this case should be perform. In the basis of functional and modular organization for situational planning subsystem was put situational planning method that was developed by the authors. We designated it CDVH-NN (distance vector histogram — neural network complex method). Integration of algorithmic means of short-term forecasting of the external environment with the formal-logical basis of direct distribution neural networks are different CDVH-NN method from others. The productivity and the efficiency of the functional and modular organization of situational planning subsystem are confirmed by the results of software simulation in MatLab and executed natural experiments. In the article, we present results of modelling of CDVH-NN kernel in basis on FPGA for family Altera Cyclone IVe. We note that the implementation of situational planning subsystem using SOPC technology increase on several times of situational planning subsystem performance with comparison to its full program implementation. It allows freeing up resources of mobile robot board computer for other important operational task.

Design method for an multidimensional neuronet based extrapolating path planner

We present a design method used to develop an extrapolating multidimensional neural network planner for mobile object with intelligent position-trajectory control system. The modeling results of a modified neural network neural network planner used for a robotic mobile object are presented. The design method is based on the bionic environment sensing in undefined conditions with stationary and mobile obstacles in a multidimensional space. The main design principal of the neural network planner structure is the hierarchical principle of information-processing systems. Based on this we present a hierarchical structure of a complex extrapolating multidimensional neural-alike network. Such network contains separate layers used on different stages to process the environment plan received from a robotic objects technical vision system. The hierarchical structure of a complex multidimensional neural-alike network is based on the following principles: object-oriented parametric synthesis, synthesis of weighted object position features with time sampling, and direction vector plans used to extrapolate such features. These plans with a certain probability determine the spatial position of related objects in future. We present the modelling results of selected methods used to detect round or spherical mobile obstacles based on technical vision system data and to predict their trajectories in two-dimensional and three-dimensional space. We present the results of software-based modelling of this approach to design neural network planner for the intelligent position-trajectory control system of mobile objects in two-dimensional space and in the simulation software package in three-dimensional space.

Decentralized control algorithms of a group of vehicles in 2D space

The problem of decentralized control of group of robots, described by kinematic and dynamic equations of motion in the plane, is considered. Group performs predetermined rectangular area passing at a fixed speed, keeping the line and a uniform distribution. The environment may contain a priori unknown moving or stationary obstacles. Decentralized control algorithms, based on the formation of repellers in the state space of robots, are proposed. These repellers form repulsive forces generated by dynamic subsystems that extend the state space of robots. These repulsive forces are dynamic functions of distances and velocities of robots in the area of operation of the group. The process of formation of repellers allows to take into account the dynamic properties of robots, such as the maximum speed and acceleration. The robots local control law formulas are derived based on positionally-trajectory control method, which allows to operate with non-linear models. Lyapunov function in the form of a quadratic function of the state variables is constructed to obtain a nonlinear closed-loop control system. Due to the fact that a closed system is decomposed into two independent subsystems Lyapunov function is also constructed as two independent functions. Numerical simulation of the motion of a group of five robots is presented. In this simulation obstacles are presented by the boundaries of working area and a movable object of a given radius, moving rectilinear and uniform. Obstacle speed is comparable to the speeds of the robots in a group. The advantage of the proposed method is ensuring the stability of the trajectories and consideration of the limitations on the speed and acceleration at the trajectory planning stage. Proposed approach can be used for more general robots’ models, including robots in the three-dimensional environment.