Research Information System
5th INTERNATIONAL ENGINEERING RESEARCH SYMPOSIUM , Düzce, Turkey, 7 - 09 March 2024, pp.1-10
In recent years, studies have been carried out on path planning, which plays a critical role in mobile robot
navigation, and metaheuristic algorithms are used to solve this problem. Path planning for mobile robots is
the process of determining the optimal route to reach a speciic goal or task without hitting any obstacles.
This process is an optimization problem that aims to ensure that the mobile robot reaches the target point
from a starting point at the least cost. Path planning can be performed using metaheuristic algorithms.
Metaheuristic algorithms are classiied into two categories: population-based and single-solution-based.
Population-based algorithms are divided into ive different subcategories: swarm-based, physic-based,
evolutionary-based, human-based and math-based. Within the scope of this study, three different
metaheuristic algorithms will be used for path planning. There are Simulated Annealing (SA) which is a
single-solution-based algorithm, Genetic Algorithm (GA) which is a population and evolutionary-based
algorithm, and Cuckoo Search (CS) which is a population and swarm-based algorithm. Various grid-based
maps with specialized scenarios were created to compare the performance of these metaheuristic
algorithms. Thus, the kind of results the algorithms produced in different path planning problems was
observed. As a result of the observations, the performances of these algorithms were tested and compared
on the maps that were initially designed.