Abstract
Recently, the path planning problem may be considered one of the most interesting researched topics in autonomous robotics. That is why finding a safe path in a cluttered environment for amobile robot is a significant requisite. A promising route planning for mobile robots on one side saves time and, on the other side, reduces the wear and tear on the robot, saving the capital investment. Numerous route planning methods for the mobile robot have been developed and applied. According to our best knowledge, no method offers an optimum solution among the existing methods. Particle Swarm Optimization (PSO), a numerical optimization method based on the mobility of virtual particles in a multidimensional space, is considered one of the best algorithms for route planning under constantly changing environmental circumstances. Among the researchers, reactive methods are increasingly common and extensively used for the training of neural networks in order to have efficient route planning for mobile robots. This paper proposes a PSO Weighted Grey Wolf Optimization (PSOWGWO) algorithm. PSOWGWO is a hybrid algorithm based on enhanced Grey Wolf Optimization (GWO) with weights. In order to measure the statistical efficiency of the proposed algorithm, Wilcoxon rank-sum and ANOVA statistical tests are applied. The experimental results demonstrate a 25% to 45% enhancement in terms of Area Under Curve (AUC). Moreover, superior performance in terms of data size, path planning time, and accuracy is demonstrated over other state-of-the-art techniques.