Stereo object matching for mobile robot path planning using artificial fish algorithms

Abstract

The popularity of robots is on the rise, not only in industrial settings but increasingly in daily venues such as airports. Recently, some organizations have carried out experiments utilizing robots specifically created to improve airport hygiene, security, and passengers’ overall satisfaction. Furthermore, the utilization of the artificial fish (AFs) algorithm in path planning for mobile robots yielded exceptional outcomes. The robot can replicate the prey behavior of the AFs algorithm, as evidenced by the prevalence of pos one in the simulation. The robot exhibits another behavior, which is the subsequent behavior. The behavior of the AFs algorithm is influenced by the available food sources. Simultaneously, mobile robots are influenced by the stimulation of their neighboring responses. Afterwards, the three primary classifiers are employed to perform stereo-object matching on different objects. The recognition rate achieved by the AdaBoost classifier is promising, with an accuracy rate of 92.4%. This result shows excellent potential for improving the path planning of mobile robots equipped with visual surveillance systems for their surroundings.