Motion control is one of the most intensively developing areas that require a constant development of theory and applications.
Nowadays one of the most topical problems is control of nonlinear multiply connected systems functioning autonomously under the conditions of uncertain parameters and disturbances and at presence of stationary and mobile obstacles. This problem doesn’t have a proper solution yet. The most important level for the operation of autonomous objects is the motion planning level that should ensure setting the intermediary goals and operation of the vehicle in the environment with stationary and mobile obstacles.
So, the application of the described structure in implementation of a position-path control system allows for a comprehensive implementation of the controller level and of a number of functions of the planning level. Besides, the position-path control systems provide an effective coupling of the planner with the controller level.
At present, the researchers are actively developing the modifications of position path control. One of such modifications is an approach based on finding and using biological analogies, particularly using the results of neurophysiologic
Features of Position-Path Control Systems based on Artificial Neural Networks
Application area: intelligent position-path control systems for robotic vehicles operating under the conditions of environmental uncertainty.
Distinctive features: technical imitation of biological systems ensuring “intelligent” behavior in a complicated non-formalized environment; matching the segments of the environment to the reacting elements of the neural network of afferent synthesis. The sought path is generally formed by a physical modeling of the afferent synthesis process on the neural network.
Advantages over other methods: simplicity and clearness, low computational expenditures, capability of dynamic control of vehicle’s motion in real time.
The examples of implementations of position-path control systems based on artificial neural networks
Unmanned boat with autonomous control system under the conditions of multiple above-water obstacles.
Visualization of the artificial neural network formed by the autonomous control system on detection of the obstacle by the computer vision system.
The process of tuning the artificial neural network during stabilization and motion of the robotic helicopter in complicated weather conditions.
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