Grasshopper optimization algorithm based path planning for autonomous mobile robot

Autonomous mobile robots have become very popular and essential in our life, especially industry. One of the crucial activities robot is planning path from a start point to target point, avoiding obstacles environment. Recently, received more attention, many methodologies been proposed. Path studies shown effectiveness swarm intelligence complex known or unknown environments. This paper presents global method based on grasshopper optimization algorithm (GOA) static improved using bias factor increase efficiency improve resulting path. The this further enhanced an version multinomial logistic regression (MLR). algorithms were evaluated three different large environments varying complexities. GOA has compared with ant colony (ACO) same experiments superiority terms time convergence cost.