Adaptive weight grey wolf algorithm application on path planning in unknown environments

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to goal avoiding hurdles current subjects of autonomous robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated this work two approaches: first, Adaptive Adjustment Approach Control Parameters, second, Variable Weights method. Those methods utilized for updating wolf position, accelerate convergence, cut down on time. proposed online optimization approach used three different environments including an environment with unknown static obstacles, dynamic target. method performed phases which sensors reading phase path calculation phase. can solve local minima problem obstacles. A comparison study result between other revealed that algorithm better include situation minima. Finally, when put Hybrid Fuzzy-Wind Driven Particle Swarm average improvement rates length 2.86% 4.70391%, respectively.