Multiple trajectory optimization and control of robotic agents using hybrid fuzzy embedded artificial intelligence...

Wheeled robot is preferred for their ability to replace human efforts in performing tedious and complex tasks. To accomplish the goal of present global scenario like replacement of human effort and work on fixed automation, it is needed to target multi-objective problems in context of robotic path optimization and less time consumption. Path optimization and control over wheeled robots is very challenging and interesting part of robotic research. Here, Fuzzy logic and modified marine predator optimization algorithm are hybridized and implemented on mobile robots to fulfill real-time objectives of present scenario. In hybridization process, initially obstacle distances from robot location are fed into fuzzy-logic and interim output obtained from Fuzzy logic is again fed to marine predator optimization algorithm to obtain final output. A Petri-Net controller is added with proposed novel fuzzy- marine predator optimization algorithm that further optimizes the navigational parameters by avoiding inter-collision among robots in presence of moving obstacles. Simulation is performed through MATLAB software and the results are tested against real-time experiments under laboratory condition. Simulation and real-time experiments authenticate successful navigation of multiple robots by achieving their objectives. Furthermore, the proposed technique is tested against different AI techniques and existing paper. An average improvement of approximately 10% or more is observed in navigational parameters.