A Novel Hybrid Dragonfly-Cuckoo Search Algorithm For Optimized Mobile Robot Navigation

Based on the fact that the robot’s mobile navigation problem is an NP-hard optimization problem, this study investigates potential solutions to this issue by using optimization algorithms that draw inspiration from natural design. Autonomous mobile robot navigation problems may be broken down into three interrelated parts: map making, route planning, and movement. These factors inform the development of a mobile robot route planning algorithm that finds the best, most efficient way to move from A to B in a complicated and confusing environment, without crashing. The suggested study primarily focuses on mobile robot route planning. Many different optimization methods that draw inspiration from swarm intelligence and biotechnology are now used in the navigation of mobile robots. Some examples are the Firefly algorithm, ACO, PSO, and DA. Hybridization integrates the strengths of two or more algorithms to provide superior optimum solutions for problems with many objectives, as compared to the strength of each algorithm alone. The adaptability and capacity to achieve optimum solutions for complicated optimization problems led to the selection of the DA and the Cuckoo Searching algorithm (CS) for hybridization in this study.