Algorithmization Of Automatic Motion Control of a Mobile Robot in an Environment with Obstacles by an Automatic Logical Method

This article addresses the development of algorithms for automatic mobile robot motion control in static environments with obstacles, aiming for safe and effective navigation to a target while avoiding collisions. It compares two key approaches: finite automata, characterized by discrete switching between "move to goal" and "obstacle avoidance" states, and fuzzy logic, which enables smoother transitions and adaptive control under sensor uncertainty. An original Logical-Dynamic Automatic Control System based on fuzzy logic principles is proposed. This system utilizes three specialized fuzzy controllers: an obstacle avoidance controller using data from three lidar sensors (right, left, center); a goal-seeking controller operating on the robot’s angular deviation from the target; and a group fuzzy controller-coordinator. The coordinator dynamically weighs and combines the control actions from the first two controllers, prioritizing obstacle avoidance when necessary. The proposed LogicalDynamic Automatic Control System’s effectiveness was evaluated through computer simulations in MATLAB Simulink using the Mobile Robotics Simulation Toolbox. А differential drive robot’s motion was simulated in environments containing various static obstacles. Performance was compared against a baseline finite automaton model using metrics such as trajectory length, time to reach the goal, average speed, and an integral objective function. The simulations demonstrated that the LogicalDynamic Automatic Control System provides smoother wheel angular velocity changes, reduces abrupt mode switching, and enhances overall control efficiency by 2.22 % compared to the automaton-based approach, highlighting its potential for practical application. This fuzzy logic approach shows promise for real-world robotic systems.

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