Intelligent control system for service robot based on soft computing

Methods for designing an integrated control system for mobile robot for service use based on soft computing logic are developed. The effectiveness of the developed methods for route control and obstacle avoidance by the robot, and for precise positioning of the manipulation device in unpredicted situations is demonstrated. Obstacle avoidance route control is based on computer modeling of the dynamics of a robotic system, and the search for possible solutions is carried out using genetic algorithms. Optimal solutions obtained through genetic algorithms are used to construct and train a fuzzy neural network, which forms a knowledge base for a fuzzy controller to control navigation and perform technological operations, such as opening a door, using a manipulator. Genetic algorithms, fuzzy neural networks, and fuzzy controllers with embedded knowledge bases have demonstrated their effectiveness for path planning for a mobile robot for service use in the conditions of potential obstacles in the form of objects and people. The article considers the application of intelligent computing based on evolutionary and genetic algorithms, and fuzzy logic is considered in control systems for a manipulator with three degrees of freedom in unpredicted control situations. To demonstrate the results of fuzzy modeling of the robot control system, the results of the experimental studies are described.

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