Robot Path Planning Using Grasshopper Optimization Algorithm

Abstract

In the world of mobile robots, figuring out the best path is a big deal. Robots use specific plans to move from one point to another. The main aim of path planning is to find safe moves for the robot in places full of obstacles. These moves create a smooth, collision-free path from where the robot starts to its target. There are many ways to solve this path problem, but they're not perfect yet. Recently, researchers have turned to smart methods, called meta-heuristic methods, to solve these problems. In our study, we tried out the particle swarm optimization algorithm (PSO) and the grasshopper optimization algorithm (GOA). Looking at the results, we found that PSO had a best cost value of 7.5558, and GOA had a best cost value of 7.5421. This shows how good the grasshopper optimization algorithm is at path planning and how effective these meta-heuristic methods can be.