Analysis and Implementation of Modified Potential Field Ant Colony Neural Fuzzy Optimization Method for Robot Control and Navigation in Complex Scenarios

Abstract

This research investigates Modified Potential field Ant colony Neural Fuzzy (MPANF) optimization AI technique for path control and navigation planning of robots in various complex scenarios. It is observed that techniques used for robots navigation control and planning require meticulous analysis. MPANF AI method is used in the current research to control intelligent robotics agents in unknown environments while travelling from start point to end objective point. Various sensors are used to map the surroundings embedded in the robot while moving from start point to end point. From the results it has been observed that the robots navigate successfully using MPANF technique while avoiding obstacles effectively. Several exercises are exhibited in connection to path planning of robots. Results show that MPANF method can be successfully employed for various types of robot navigation in complex unknown environmental scenarios. In future MPANF method can be applied for problem solving in engineering and science fields.