Mobile Robot Navigational Planning Using Grasshopper Algorithm

Abstract

In this study, we propose the Grasshopper Optimization Algorithm (GOA) as an innovative approach to mobile robot path planning and obstacle avoidance. The algorithm draws inspiration from the swarming behavior of grasshoppers, emulating their intricate movement patterns and interactions to identify optimal routes while effectively circumventing obstacles. The algorithm guides the robot's trajectory through complex environments by considering a combination of factors such as social interaction, gravitational influence, wind advection, and obstacle avoidance. Through extensive simulations, the experimental results reveal the algorithm's remarkable proficiency in identifying optimal paths while deftly evading potential hazards. Moreover, a comparative analysis against other methods underscores the superior performance of the GOA-based approach. As such, the Grasshopper Optimization Algorithm presents a nature-inspired and promising avenue for enhancing the navigation capabilities of autonomous mobile robots.