Neural networks and behaviour based control for education botanical robot navigation

Abstract

Abstract The development of robots began when the military needed it as war equipment, and then it was used by some industries to develop production until now widely used for education and agriculture. Educational robots are usually more universal and simpler than industrial or military robots because robots for education are made only for simulations or prototypes. In this study, the authors surveyed prototypes of Educational botany robots, namely robots used to distinguish fruit maturity. In this journal, the behaviour-based control (BBC) algorithm will continue implemented into mobile robots. The movement of the mobile robot prepared in advance, and then actually the mobile robot is wheeled compared to the desired path. Besides, both kinematic and dynamic smelling mobile robots are derived and considered. In this study, only focused on mobile robots three-wheel differential drive, which will explore in a circular and straight path. In this journal, it can be found that image processing techniques can be used to determine the maturity of watermelons which are shown in different average values in each image obtained by the camera. The best models are generated by layer 32 hidden with an accuracy value of 87% at the training dataset level 60:10:30. The Behaviour Based Control Method has directional movements. But there was an error in each test so that I couldn’t take the watermelon correctly. The error rate reaches 25% from 40 tests or about ten failures in the form of deviating from the object point.