Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

Robotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot's trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

Automated summary

This paper presents an intelligent controller that combines the KH optimization algorithm, fuzzy logic controller, and Petri-net controller for trajectory planning and control of mobile robots in complex environments, showing promising results through simulation and real-time experiments.