A System to Track the Behaviour or Pattern of Mobile Robot Through RNN Technique

Abstract

The agricultural business has seen a rise in the use of mobile robots because of their ease of navigation in crop fields. Numerous significant issues pertaining to agricultural robot navigation are covered in this paper, such as obstacle detection, route planning, mapping, localization, and controlled guiding. The proposed hybrid recurrent neural network (RNN) model seeks to optimise mobile autonomous robot tracking performance using installed laser-based weeding equipment. The system can handle a range of trajectories, including curves and straight lines, that are observed in agricultural settings by integrating spiral, lateral, and linear speed controls. A comprehensive field test is carried out on a tracked mobile platform to validate the controllers’ functionality in a variety of scenarios, including loose dirt, stones, and humidity.